Posts in category: Google AI

A recap of Google’s top announcements and initiatives in 2022.Read More

Posted by Jinsung Yoon and Sercan O. Arik, Research Scientists, Google Research, Cloud AI Team

Analysis of Electronic Health Records (EHR) has a tremendous potential for enhancing patient care, quantitatively measuring performance of clinical practices, and facilitating clinical research. Statistical estimation and machine learning (ML) models trained on EHR data can be used to predict the probability of various diseases (such as diabetes), track patient wellness, and predict how patients respond to specific drugs. For such models, researchers and practitioners need access to EHR data. However, it can be challenging to leverage EHR data while ensuring data privacy and conforming to patient confidentiality regulations (such as HIPAA).

Conventional methods to anonymize data (e.g., de-identification) are often tedious and costly. Moreover, they can distort important features from the original dataset, decreasing the utility of the data significantly; they can also be susceptible to privacy attacks. Alternatively, an approach based on generating synthetic data can maintain both important dataset features and privacy.

To that end, we propose a novel generative modeling framework in “EHR-Safe: Generating High-Fidelity and Privacy-Preserving Synthetic Electronic Health Records“. With the innovative methodology in EHR-Safe, we show that synthetic data can satisfy two key properties: (i) high fidelity (i.e., they are useful for the task of interest, such as having similar downstream performance when a diagnostic model is trained on them), (ii) meet certain privacy measures (i.e., they do not reveal any real patient’s identity). Our state-of-the-art results stem from novel approaches for encoding/decoding features, normalizing complex distributions, conditioning adversarial training, and representing missing data.

Generating synthetic data from the original data with EHR-Safe.

Challenges of Generating Realistic Synthetic EHR Data

There are multiple fundamental challenges to generating synthetic EHR data. EHR data contain heterogeneous features with different characteristics and distributions. There can be numerical features (e.g., blood pressure) and categorical features with many or two categories (e.g., medical codes, mortality outcome). Some of these may be static (i.e., not varying during the modeling window), while others are time-varying, such as regular or sporadic lab measurements. Distributions might come from different families — categorical distributions can be highly non-uniform (e.g., for under-represented groups) and numerical distributions can be highly skewed (e.g., a small proportion of values being very large while the vast majority are small). Depending on a patient’s condition, the number of visits can also vary drastically — some patients visit a clinic only once whereas some visit hundreds of times, leading to a variance in sequence lengths that is typically much higher compared to other time-series data. There can be a high ratio of missing features across different patients and time steps, as not all lab measurements or other input data are collected.

Examples of real EHR data: temporal numerical features (upper) and temporal categorical features (lower).

EHR-Safe: Synthetic EHR Data Generation Framework

EHR-Safe consists of sequential encoder-decoder architecture and generative adversarial networks (GANs), depicted in the figure below. Because EHR data are heterogeneous (as described above), direct modeling of raw EHR data is challenging for GANs. To circumvent this, we propose utilizing a sequential encoder-decoder architecture, to learn the mapping from the raw EHR data to the latent representations, and vice versa.

Block diagram of EHR-Safe framework.

While learning the mapping, esoteric distributions of numerical and categorical features pose a great challenge. For example, some values or numerical ranges might dominate the distribution, but the capability of modeling rare cases is essential. The proposed feature mapping and stochastic normalization (transforming original feature distributions into uniform distributions without information loss) are key to handling such data by converting to distributions for which the training of encoder-decoder and GAN are more stable (details can be found in the paper). The mapped latent representations, generated by the encoder, are then used for GAN training. After training both the encoder-decoder framework and GANs, EHR-Safe can generate synthetic heterogeneous EHR data from any input, for which we feed randomly sampled vectors. Note that only the trained generator and decoders are used for generating synthetic data.

Datasets

We focus on two real-world EHR datasets to showcase the EHR-Safe framework, MIMIC-III and eICU. Both are inpatient datasets that consist of varying lengths of sequences and include multiple numerical and categorical features with missing components.

Fidelity Results

The fidelity metrics focus on the quality of synthetically generated data by measuring the realisticness of the synthetic data. Higher fidelity implies that it is more difficult to differentiate between synthetic and real data. We evaluate the fidelity of synthetic data in terms of multiple quantitative and qualitative analyses.

Visualization

Having similar coverage and avoiding under-representation of certain data regimes are both important for synthetic data generation. As the below t-SNE analyses show, the coverage of the synthetic data (blue) is very similar with the original data (red). With membership inference metrics (will be introduced in the privacy section), we also verify that EHR-Safe does not just memorize the original train data.

t-SNE analyses on temporal and static data on MIMIC-III (upper) and eICU (lower) datasets.

Statistical Similarity

We provide quantitative comparisons of statistical similarity between original and synthetic data for each feature. Most statistics are well-aligned between original and synthetic data — for example a measure of the KS statistics, i.e,. the maximum difference in the cumulative distribution function (CDF) between the original and the synthetic data, are mostly lower than 0.03. More detailed tables can be found in the paper. The figure below exemplifies the CDF graphs for original vs. synthetic data for three features — overall they seem very close in most cases.

CDF graphs of two features between original and synthetic EHR data. Left: Mean Airway Pressure. Right: Minute Volume Alarm.

Utility

Because one of the most important use cases of synthetic data is enabling ML innovations, we focus on the fidelity metric that measures the ability of models trained on synthetic data to make accurate predictions on real data. We compare such model performance to an equivalent model trained with real data. Similar model performance would indicate that the synthetic data captures the relevant informative content for the task. As one of the important potential use cases of EHR, we focus on the mortality prediction task. We consider four different predictive models: Gradient Boosting Tree Ensemble (GBDT), Random Forest (RF), Logistic Regression (LR), Gated Recurrent Units (GRU).

Mortality prediction performance with the model trained on real vs. synthetic data. Left: MIMIC-III. Right: eICU.

In the figure above we see that in most scenarios, training on synthetic vs. real data are highly similar in terms of Area Under Receiver Operating Characteristics Curve (AUC). On MIMIC-III, the best model (GBDT) on synthetic data is only 2.6% worse than the best model on real data; whereas on eICU, the best model (RF) on synthetic data is only 0.9% worse.

Privacy Results

We consider three different privacy attacks to quantify the robustness of the synthetic data with respect to privacy.

• Membership inference attack: An adversary predicts whether a known subject was a present in the training data used for training the synthetic data model.
• Re-identification attack: The adversary explores the probability of some features being re-identified using synthetic data and matching to the training data.
• Attribute inference attack: The adversary predicts the value of sensitive features using synthetic data.

Privacy risk evaluation across three privacy metrics: membership-inference (top-left), re-identification (top-right), and attribute inference (bottom). The ideal value of privacy risk for membership inference is random guessing (0.5). For re-identification, the ideal case is to replace the synthetic data with disjoint holdout original data.

The figure above summarizes the results along with the ideal achievable value for each metric. We observe that the privacy metrics are very close to the ideal in all cases. The risk of understanding whether a sample of the original data is a member used for training the model is very close to random guessing; it also verifies that EHR-Safe does not just memorize the original train data. For the attribute inference attack, we focus on the prediction task of inferring specific attributes (e.g., gender, religion, and marital status) from other attributes. We compare prediction accuracy when training a classifier with real data against the same classifier trained with synthetic data. Because the EHR-Safe bars are all lower, the results demonstrate that access to synthetic data does not lead to higher prediction performance on specific features as compared to access to the original data.

Comparison to Alternative Methods

We compare EHR-Safe to alternatives (TimeGAN, RC-GAN, C-RNN-GAN) proposed for time-series synthetic data generation. As shown below, EHR-Safe significantly outperforms each.

Downstream task performance (AUC) in comparison to alternatives.

Conclusions

We propose a novel generative modeling framework, EHR-Safe, that can generate highly realistic synthetic EHR data that are robust to privacy attacks. EHR-Safe is based on generative adversarial networks applied to the encoded raw data. We introduce multiple innovations in the architecture and training mechanisms that are motivated by the key challenges of EHR data. These innovations are key to our results that show almost-identical properties with real data (when desired downstream capabilities are considered) with almost-ideal privacy preservation. An important future direction is generative modeling capability for multimodal data, including text and image, as modern EHR data might contain both.

Acknowledgements

We gratefully acknowledge the contributions of Michel Mizrahi, Nahid Farhady Ghalaty, Thomas Jarvinen, Ashwin S. Ravi, Peter Brune, Fanyu Kong, Dave Anderson, George Lee, Arie Meir, Farhana Bandukwala, Elli Kanal, and Tomas Pfister.

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Posted by Pritish Kamath and Pasin Manurangsi, Research Scientists, Google Research

Differential privacy (DP) is an approach that enables data analytics and machine learning (ML) with a mathematical guarantee on the privacy of user data. DP quantifies the “privacy cost” of an algorithm, i.e., the level of guarantee that the algorithm’s output distribution for a given dataset will not change significantly if a single user’s data is added to or removed from it. The algorithm is characterized by two parameters, ε and δ, where smaller values of both indicate “more private”. There is a natural tension between the privacy budget (ε, δ) and the utility of the algorithm: a smaller privacy budget requires the output to be more “noisy”, often leading to less utility. Thus, a fundamental goal of DP is to attain as much utility as possible for a desired privacy budget.

A key property of DP that often plays a central role in understanding privacy costs is that of composition, which reflects the net privacy cost of a combination of DP algorithms, viewed together as a single algorithm. A notable example is the differentially-private stochastic gradient descent (DP-SGD) algorithm. This algorithm trains ML models over multiple iterations — each of which is differentially private — and therefore requires an application of the composition property of DP. A basic composition theorem in DP says that the privacy cost of a collection of algorithms is, at most, the sum of the privacy cost of each. However, in many cases, this can be a gross overestimate, and several improved composition theorems provide better estimates of the privacy cost of composition.

In 2019, we released an open-source library (on GitHub) to enable developers to use analytic techniques based on DP. Today, we announce the addition to this library of Connect-the-Dots, a new privacy accounting algorithm based on a novel approach for discretizing privacy loss distributions that is a useful tool for understanding the privacy cost of composition. This algorithm is based on the paper “Connect the Dots: Tighter Discrete Approximations of Privacy Loss Distributions”, presented at PETS 2022. The main novelty of this accounting algorithm is that it uses an indirect approach to construct more accurate discretizations of privacy loss distributions. We find that Connect-the-Dots provides significant gains over other privacy accounting methods in literature in terms of accuracy and running time. This algorithm was also recently applied for the privacy accounting of DP-SGD in training Ads prediction models.

Differential Privacy and Privacy Loss Distributions

A randomized algorithm is said to satisfy DP guarantees if its output “does not depend significantly” on any one entry in its training dataset, quantified mathematically with parameters (ε, δ). For example, consider the motivating example of DP-SGD. When trained with (non-private) SGD, a neural network could, in principle, be encoding the entire training dataset within its weights, thereby allowing one to reconstruct some training examples from a trained model. On the other hand, when trained with DP-SGD, we have a formal guarantee that if one were able to reconstruct a training example with non-trivial probability then one would also be able to reconstruct the same example even if it was not included in the training dataset.

The hockey stick divergence, parameterized by ε, is a measure of distance between two probability distributions, as illustrated in the figure below. The privacy cost of most DP algorithms is dictated by the hockey stick divergence between two associated probability distributions P and Q. The algorithm satisfies DP with parameters (ε, δ), if the value of the hockey stick divergence for ε between P and Q is at most δ. The hockey stick divergence between (P, Q), denoted δ_P||Q(ε) is in turn completely characterized by it associated privacy loss distribution, denoted by PLD_P||Q.

Illustration of hockey stick divergence δP||Q(ε) between distributions P and Q (left), which corresponds to the probability mass of P that is above eεQ, where eεQ is an eε scaling of the probability mass of Q (right).

The main advantage of dealing with PLDs is that compositions of algorithms correspond to the convolution of the corresponding PLDs. Exploiting this fact, prior work has designed efficient algorithms to compute the PLD corresponding to the composition of individual algorithms by simply performing convolution of the individual PLDs using the fast Fourier transform algorithm.

However, one challenge when dealing with many PLDs is that they often are continuous distributions, which make the convolution operations intractable in practice. Thus, researchers often apply various discretization approaches to approximate the PLDs using equally spaced points. For example, the basic version of the Privacy Buckets algorithm assigns the probability mass of the interval between two discretization points entirely to the higher end of the interval.

Illustration of discretization by rounding up probability masses. Here a continuous PLD (in blue) is discretized to a discrete PLD (in red), by rounding up the probability mass between consecutive points.

Connect-the-Dots : A New Algorithm

Our new Connect-the-Dots algorithm provides a better way to discretize PLDs towards the goal of estimating hockey stick divergences. This approach works indirectly by first discretizing the hockey stick divergence function and then mapping it back to a discrete PLD supported on equally spaced points.

Illustration of high-level steps in the Connect-the-Dots algorithm.

This approach relies on the notion of a “dominating PLD”, namely, PLD_P’||Q’ dominates over PLD_P||Q if the hockey stick divergence of the former is greater or equal to the hockey stick divergence of the latter for all values of ε. The key property of dominating PLDs is that they remain dominating after compositions. Thus for purposes of privacy accounting, it suffices to work with a dominating PLD, which gives us an upper bound on the exact privacy cost.

Our main insight behind the Connect-the-Dots algorithm is a characterization of discrete PLD, namely that a PLD is supported on a given finite set of ε values if and only if the corresponding hockey stick divergence as a function of e^ε is linear between consecutive e^ε values. This allows us to discretize the hockey stick divergence by simply connecting the dots to get a piecewise linear function that precisely equals the hockey stick divergence function at the given e^ε values. See a more detailed explanation of the algorithm.

Comparison of the discretizations of hockey stick divergence by Connect-the-Dots vs Privacy Buckets.

Experimental Evaluation

The DP-SGD algorithm involves a noise multiplier parameter, which controls the magnitude of noise added in each gradient step, and a sampling probability, which controls how many examples are included in each mini-batch. We compare Connect-the-Dots against the algorithms listed below on the task of privacy accounting DP-SGD with a noise multiplier = 0.5, sampling probability = 0.2 x 10^-4 and δ = 10^-8.

• Renyi DP : The original approach used for the privacy accounting of DP-SGD. This accounting method is also supported in the Google-DP library.
• Privacy Buckets : The previous PLD-based approach used in Google-DP library, which rounds up probability masses between discretization points.
• Microsoft PRV Accountant : This approach improves upon the Privacy Buckets approach using a discretization that preserves the mean of the PLD.

We plot the value of the ε computed by each of the algorithms against the number of composition steps, and additionally, we plot the running time of the implementations. As shown in the plots below, privacy accounting using Renyi DP provides a loose estimate of the privacy loss. However, when comparing the approaches using PLD, we find that in this example, the implementation of Connect-the-Dots achieves a tighter estimate of the privacy loss, with a running time that is 5x faster than the Microsoft PRV Accountant and >200x faster than the previous approach of Privacy Buckets in the Google-DP library.

Left: Upper bounds on the privacy parameter ε for varying number of steps of DP-SGD, as returned by different algorithms (for fixed δ = 10-8). Right: Running time of the different algorithms.

Conclusion & Future Directions

This work proposes Connect-the-Dots, a new algorithm for computing optimal privacy parameters for compositions of differentially private algorithms. When evaluated on the DP-SGD task, we find that this algorithm gives tighter estimates on the privacy loss with a significantly faster running time.

So far, the library only supports the pessimistic estimate version of Connect-the-Dots algorithm, which provides an upper bound on the privacy loss of DP-algorithms. However, the paper also introduces a variant of the algorithm that provides an “optimistic” estimate of the PLD, which can be used to derive lower bounds on the privacy cost of DP-algorithms (provided those admit a “worst case” PLD). Currently, the library does support optimistic estimates as given by the Privacy Buckets algorithm, and we hope to incorporate the Connect-the-Dots version as well.

Acknowledgements

This work was carried out in collaboration with Vadym Doroshenko, Badih Ghazi, Ravi Kumar. We thank Galen Andrew, Stan Bashtavenko, Steve Chien, Christoph Dibak, Miguel Guevara, Peter Kairouz, Sasha Kulankhina, Stefan Mellem, Jodi Spacek, Yurii Sushko and Andreas Terzis for their help.

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Posted by Tal Schuster, Research Scientist, Google Research

Language models (LMs) are the driving force behind many recent breakthroughs in natural language processing. Models like T5, LaMDA, GPT-3, and PaLM have demonstrated impressive performance on various language tasks. While multiple factors can contribute to improving the performance of LMs, some recent studies suggest that scaling up the model’s size is crucial for revealing emergent capabilities. In other words, some instances can be solved by small models, while others seem to benefit from increased scale.

Despite recent efforts that enabled the efficient training of LMs over large amounts of data, trained models can still be slow and costly for practical use. When generating text at inference time, most autoregressive LMs output content similar to how we speak and write (word after word), predicting each new word based on the preceding words. This process cannot be parallelized since LMs need to complete the prediction of one word before starting to compute the next one. Moreover, predicting each word requires significant computation given the model’s billions of parameters.

In “Confident Adaptive Language Modeling”, presented at NeurIPS 2022, we introduce a new method for accelerating the text generation of LMs by improving efficiency at inference time. Our method, named CALM, is motivated by the intuition that some next word predictions are easier than others. When writing a sentence, some continuations are trivial, while others might require more effort. Current LMs devote the same amount of compute power for all predictions. Instead, CALM dynamically distributes the computational effort across generation timesteps. By selectively allocating more computational resources only to harder predictions, CALM generates text faster while preserving output quality.

Confident Adaptive Language Modeling

When possible, CALM skips some compute effort for certain predictions. To demonstrate this, we use the popular encoder-decoder T5 architecture. The encoder reads the input text (e.g., a news article to summarize) and converts the text to dense representations. Then, the decoder outputs the summary by predicting it word by word. Both the encoder and decoder include a long sequence of Transformer layers. Each layer includes attention and feedforward modules with many matrix multiplications. These layers gradually modify the hidden representation that is ultimately used for predicting the next word.

Instead of waiting for all decoder layers to complete, CALM attempts to predict the next word earlier, after some intermediate layer. To decide whether to commit to a certain prediction or to postpone the prediction to a later layer, we measure the model’s confidence in its intermediate prediction. The rest of the computation is skipped only when the model is confident enough that the prediction won’t change. For quantifying what is “confident enough”, we calibrate a threshold that statistically satisfies arbitrary quality guarantees over the full output sequence.

Text generation with a regular language model (top) and with CALM (bottom). CALM attempts to make early predictions. Once confident enough (darker blue tones), it skips ahead and saves time.

Language Models with Early Exits

Enabling this early exit strategy for LMs requires minimal modifications to the training and inference processes. During training, we encourage the model to produce meaningful representations in intermediate layers. Instead of predicting only using the top layer, our learning loss function is a weighted average over the predictions of all layers, assigning higher weight to top layers. Our experiments demonstrate that this significantly improves the intermediate layer predictions while preserving the full model’s performance. In one model variant, we also include a small early-exit classifier trained to classify if the local intermediate layer prediction is consistent with the top layer. We train this classifier in a second quick step where we freeze the rest of the model.

Once the model is trained, we need a method to allow early-exiting. First, we define a local confidence measure for capturing the model’s confidence in its intermediate prediction. We explore three confidence measures (described in the results section below): (1) softmax response, taking the maximum predicted probability out of the softmax distribution; (2) state propagation, the cosine distance between the current hidden representation and the one from the previous layer; and (3) early-exit classifier, the output of a classifier specifically trained for predicting local consistency. We find the softmax response to be statistically strong while being simple and fast to compute. The other two alternatives are lighter in floating point operations (FLOPS).

Another challenge is that the self-attention of each layer depends on hidden-states from previous words. If we exit early for some word predictions, these hidden-states might be missing. Instead, we attend back to the hidden state of the last computed layer.

Finally, we set up the local confidence threshold for exiting early. In the next section, we describe our controlled process for finding good threshold values. As a first step, we simplify this infinite search space by building on a useful observation: mistakes that are made at the beginning of the generation process are more detrimental since they can affect all of the following outputs. Therefore, we start with a higher (more conservative) threshold, and gradually reduce it with time. We use a negative exponent with user-defined temperature to control this decay rate. We find this allows better control over the performance-efficiency tradeoff (the obtained speedup per quality level).

Reliably Controlling the Quality of the Accelerated Model

Early exit decisions have to be local; they need to happen when predicting each word. In practice, however, the final output should be globally consistent or comparable to the original model. For example, if the original full model generated “the concert was wonderful and long”, one would accept CALM switching the order of the adjectives and outputting “the concert was long and wonderful”. However, at the local level, the word “wonderful” was replaced with “long”. Therefore, the two outputs are globally consistent, but include some local inconsistencies. We build on the Learn then Test (LTT) framework to connect local confidence-based decisions to globally consistent outputs.

In CALM, local per-timestep confidence thresholds for early exiting decisions are derived, via LTT calibration, from user-defined consistency constraints over the full output text. Red boxes indicate that CALM used most of the decoder’s layers for that specific prediction. Green boxes indicate that CALM saved time by using only a few Transformer layers. Full sentence shown in the last example of this post.

First, we define and formulate two types of consistency constraints from which to choose:

1. Textual consistency: We bound the expected textual distance between the outputs of CALM and the outputs of the full model. This doesn’t require any labeled data.
2. Risk consistency: We bound the expected increase in loss that we allow for CALM compared to the full model. This requires reference outputs against which to compare.

For each of these constraints, we can set the tolerance that we allow and calibrate the confidence threshold to allow early exits while reliably satisfying our defined constraint with an arbitrarily high probability.

CALM Saves Inference Time

We run experiments on three popular generation datasets: CNN/DM for summarization, WMT for machine translation, and SQuAD for question answering. We evaluate each of the three confidence measures (softmax response, state propagation and early-exit classifier) using an 8-layer encoder-decoder model. To evaluate global sequence-level performance, we use the standard Rouge-L, BLEU, and Token-F1 scores that measure distances against human-written references. We show that one can maintain full model performance while using only a third or half of the layers on average. CALM achieves this by dynamically distributing the compute effort across the prediction timesteps.

As an approximate upper bound, we also compute the predictions using a local oracle confidence measure, which enables exiting at the first layer that leads to the same prediction as the top one. On all three tasks, the oracle measure can preserve full model performance when using only 1.5 decoder layers on average. In contrast to CALM, a static baseline uses the same number of layers for all predictions, requiring 3 to 7 layers (depending on the dataset) to preserve its performance. This demonstrates why the dynamic allocation of compute effort is important. Only a small fraction of the predictions require most of the model’s complexity, while for others much less should suffice.

Performance per task against the average number of decoder layers used.

Finally, we also find that CALM enables practical speedups. When benchmarking on TPUs, we saved almost half of the compute time while maintaining the quality of the outputs.

Example of a generated news summary. The top cell presents the reference human-written summary. Below is the prediction of the full model (8 layers) followed by two different CALM output examples. The first CALM output is 2.9x faster and the second output is 3.6x faster than the full model, benchmarked on TPUs.

Conclusion

CALM allows faster text generation with LMs, without reducing the quality of the output text. This is achieved by dynamically modifying the amount of compute per generation timestep, allowing the model to exit the computational sequence early when confident enough.

As language models continue to grow in size, studying how to efficiently use them becomes crucial. CALM is orthogonal and can be combined with many efficiency related efforts, including model quantization, distillation, sparsity, effective partitioning, and distributed control flows.

Acknowledgements

It was an honor and privilege to work on this with Adam Fisch, Ionel Gog, Seungyeon Kim, Jai Gupta, Mostafa Dehghani, Dara Bahri, Vinh Q. Tran, Yi Tay, and Donald Metzler. We also thank Anselm Levskaya, Hyung Won Chung, Tao Wang, Paul Barham, Michael Isard, Orhan Firat, Carlos Riquelme, Aditya Menon, Zhifeng Chen, Sanjiv Kumar, and Jeff Dean for helpful discussions and feedback. Finally, we thank Tom Small for preparing the animation in this blog post.

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Posted by Quan Wang, Senior Staff Software Engineer, and Fan Zhang, Staff Software Engineer, Google

In 2019 we launched Recorder, an audio recording app for Pixel phones that helps users create, manage, and edit audio recordings. It leverages recent developments in on-device machine learning to transcribe speech, recognize audio events, suggest tags for titles, and help users navigate transcripts.

Nonetheless, some Recorder users found it difficult to navigate long recordings that have multiple speakers because it’s not clear who said what. During the Made By Google event this year, we announced the “speaker labels” feature for the Recorder app. This opt-in feature annotates a recording transcript with unique and anonymous labels for each speaker (e.g., “Speaker 1”, “Speaker 2”, etc.) in real time during the recording. It significantly improves the readability and usability of the recording transcripts. This feature is powered by Google’s new speaker diarization system named Turn-to-Diarize, which was first presented at ICASSP 2022.

Left: Recorder transcript without speaker labels. Right: Recorder transcript with speaker labels.

System Architecture

Our speaker diarization system leverages several highly optimized machine learning models and algorithms to allow diarizing hours of audio in a real-time streaming fashion with limited computational resources on mobile devices. The system mainly consists of three components: a speaker turn detection model that detects a change of speaker in the input speech, a speaker encoder model that extracts voice characteristics from each speaker turn, and a multi-stage clustering algorithm that annotates speaker labels to each speaker turn in a highly efficient way. All components run fully on the device.

Architecture of the Turn-to-Diarize system.

Detecting Speaker Turns

The first component of our system is a speaker turn detection model based on a Transformer Transducer (T-T), which converts the acoustic features into text transcripts augmented with a special token <st> representing a speaker turn. Unlike preceding customized systems that use role-specific tokens (e.g., <doctor> and <patient>) for conversations, this model is more generic and can be trained on and deployed to various application domains.

In most applications, the output of a diarization system is not directly shown to users, but combined with a separate automatic speech recognition (ASR) system that is trained to have smaller word errors. Therefore, for the diarization system, we are relatively more tolerant to word token errors than errors of the <st> token. Based on this intuition, we propose a new token-level loss function that allows us to train a small speaker turn detection model with high accuracy on predicted <st> tokens. Combined with edit-based minimum Bayes risk (EMBR) training, this new loss function significantly improved the interval-based F1 score on seven evaluation datasets.

Extracting Voice Characteristics

Once the audio recording has been segmented into homogeneous speaker turns, we use a speaker encoder model to extract an embedding vector (i.e., d-vector) to represent the voice characteristics of each speaker turn. This approach has several advantages over prior work that extracts embedding vectors from small fixed-length segments. First, it avoids extracting an embedding from a segment containing speech from multiple speakers. At the same time, each embedding covers a relatively large time range that contains sufficient signals from the speaker. It also reduces the total number of embeddings to be clustered, thus making the clustering step less expensive. These embeddings are processed entirely on-device until speaker labeling of the transcript is completed, and then deleted.

Multi-Stage Clustering

After the audio recording is represented by a sequence of embedding vectors, the last step is to cluster these embedding vectors, and assign a speaker label to each. However, since audio recordings from the Recorder app can be as short as a few seconds, or as long as up to 18 hours, it is critical for the clustering algorithm to handle sequences of drastically different lengths.

For this we propose a multi-stage clustering strategy to leverage the benefits of different clustering algorithms. First, we use the speaker turn detection outputs to determine whether there are at least two different speakers in the recording. For short sequences, we use agglomerative hierarchical clustering (AHC) as the fallback algorithm. For medium-length sequences, we use spectral clustering as our main algorithm, and use the eigen-gap criterion for accurate speaker count estimation. For long sequences, we reduce computational cost by using AHC to pre-cluster the sequence before feeding it to the main algorithm. During the streaming, we keep a dynamic cache of previous AHC cluster centroids that can be reused for future clustering calls. This mechanism allows us to enforce an upper bound on the entire system with constant time and space complexity.

This multi-stage clustering strategy is a critical optimization for on-device applications where the budget for CPU, memory, and battery is very small, and allows the system to run in a low power mode even after diarizing hours of audio. As a tradeoff between quality and efficiency, the upper bound of the computational cost can be flexibly configured for devices with different computational resources.

Diagram of the multi-stage clustering strategy.

Correction and Customization

In our real-time streaming speaker diarization system, as the model consumes more audio input, it accumulates confidence on predicted speaker labels, and may occasionally make corrections to previously predicted low-confidence speaker labels. The Recorder app automatically updates the speaker labels on the screen during recording to reflect the latest and most accurate predictions.

At the same time, the Recorder app’s UI allows the user to rename the anonymous speaker labels (e.g., “Speaker 2”) to customized labels (e.g., “car dealer”) for better readability and easier memorization for the user within each recording.

Recorder allows the user to rename the speaker labels for better readability.

Future Work

Currently, our diarization system mostly runs on the CPU block of Google Tensor, Google’s custom-built chip that powers more recent Pixel phones. We are working on delegating more computations to the TPU block, which will further reduce the overall power consumption of the diarization system. Another future work direction is to leverage multilingual capabilities of speaker encoder and speech recognition models to expand this feature to more languages.

Acknowledgments

The work described in this post represents joint efforts from multiple teams within Google. Contributors include Quan Wang, Yiling Huang, Evan Clark, Qi Cao, Han Lu, Guanlong Zhao, Wei Xia, Hasim Sak, Alvin Zhou, Jason Pelecanos, Luiza Timariu, Allen Su, Fan Zhang, Hugh Love, Kristi Bradford, Vincent Peng, Raff Tsai, Richard Chou, Yitong Lin, Ann Lu, Kelly Tsai, Hannah Bowman, Tracy Wu, Taral Joglekar, Dharmesh Mokani, Ajay Dudani, Ignacio Lopez Moreno, Diego Melendo Casado, Nino Tasca, Alex Gruenstein.

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Posted Keerthana Gopalakrishnan and Kanishka Rao, Google Research, Robotics at Google

Major recent advances in multiple subfields of machine learning (ML) research, such as computer vision and natural language processing, have been enabled by a shared common approach that leverages large, diverse datasets and expressive models that can absorb all of the data effectively. Although there have been various attempts to apply this approach to robotics, robots have not yet leveraged highly-capable models as well as other subfields.

Several factors contribute to this challenge. First, there’s the lack of large-scale and diverse robotic data, which limits a model’s ability to absorb a broad set of robotic experiences. Data collection is particularly expensive and challenging for robotics because dataset curation requires engineering-heavy autonomous operation, or demonstrations collected using human teleoperations. A second factor is the lack of expressive, scalable, and fast-enough-for-real-time-inference models that can learn from such datasets and generalize effectively.

To address these challenges, we propose the Robotics Transformer 1 (RT-1), a multi-task model that tokenizes robot inputs and outputs actions (e.g., camera images, task instructions, and motor commands) to enable efficient inference at runtime, which makes real-time control feasible. This model is trained on a large-scale, real-world robotics dataset of 130k episodes that cover 700+ tasks, collected using a fleet of 13 robots from Everyday Robots (EDR) over 17 months. We demonstrate that RT-1 can exhibit significantly improved zero-shot generalization to new tasks, environments and objects compared to prior techniques. Moreover, we carefully evaluate and ablate many of the design choices in the model and training set, analyzing the effects of tokenization, action representation, and dataset composition. Finally, we’re open-sourcing the RT-1 code, and hope it will provide a valuable resource for future research on scaling up robot learning.

RT-1 absorbs large amounts of data, including robot trajectories with multiple tasks, objects and environments, resulting in better performance and generalization.

Robotics Transformer (RT-1)

RT-1 is built on a transformer architecture that takes a short history of images from a robot’s camera along with task descriptions expressed in natural language as inputs and directly outputs tokenized actions.

RT-1’s architecture is similar to that of a contemporary decoder-only sequence model trained against a standard categorical cross-entropy objective with causal masking. Its key features include: image tokenization, action tokenization, and token compression, described below.

Image tokenization: We pass images through an EfficientNet-B3 model that is pre-trained on ImageNet, and then flatten the resulting 9×9×512 spatial feature map to 81 tokens. The image tokenizer is conditioned on natural language task instructions, and uses FiLM layers initialized to identity to extract task-relevant image features early on.

Action tokenization: The robot’s action dimensions are 7 variables for arm movement (x, y, z, roll, pitch, yaw, gripper opening), 3 variables for base movement (x, y, yaw), and an extra discrete variable to switch between three modes: controlling arm, controlling base, or terminating the episode. Each action dimension is discretized into 256 bins.

Token Compression: The model adaptively selects soft combinations of image tokens that can be compressed based on their impact towards learning with the element-wise attention module TokenLearner, resulting in over 2.4x inference speed-up.

RT-1’s architecture: The model takes a text instruction and set of images as inputs, encodes them as tokens via a pre-trained FiLM EfficientNet model and compresses them via TokenLearner. These are then fed into the Transformer, which outputs action tokens.

To build a system that could generalize to new tasks and show robustness to different distractors and backgrounds, we collected a large, diverse dataset of robot trajectories. We used 13 EDR robot manipulators, each with a 7-degree-of-freedom arm, a 2-fingered gripper, and a mobile base, to collect 130k episodes over 17 months. We used demonstrations provided by humans through remote teleoperation, and annotated each episode with a textual description of the instruction that the robot just performed. The set of high-level skills represented in the dataset includes picking and placing items, opening and closing drawers, getting items in and out drawers, placing elongated items up-right, knocking objects over, pulling napkins and opening jars. The resulting dataset includes 130k+ episodes that cover 700+ tasks using many different objects.

Experiments and Results

To better understand RT-1’s generalization abilities, we study its performance against three baselines: Gato, BC-Z and BC-Z XL (i.e., BC-Z with same number of parameters as RT-1), across four categories:

1. Seen tasks performance: performance on tasks seen during training
2. Unseen tasks performance: performance on unseen tasks where the skill and object(s) were seen separately in the training set, but combined in novel ways
3. Robustness (distractors and backgrounds): performance with distractors (up to 9 distractors and occlusion) and performance with background changes (new kitchen, lighting, background scenes)
4. Long-horizon scenarios: execution of SayCan-type natural language instructions in a real kitchen

RT-1 outperforms baselines by large margins in all four categories, exhibiting impressive degrees of generalization and robustness.

Performance of RT-1 vs. baselines on evaluation scenarios.

Incorporating Heterogeneous Data Sources

To push RT-1 further, we train it on data gathered from another robot to test if (1) the model retains its performance on the original tasks when a new data source is presented and (2) if the model sees a boost in generalization with new and different data, both of which are desirable for a general robot learning model. Specifically, we use 209k episodes of indiscriminate grasping that were autonomously collected on a fixed-base Kuka arm for the QT-Opt project. We transform the data collected to match the action specs and bounds of our original dataset collected with EDR, and label every episode with the task instruction “pick anything” (the Kuka dataset doesn’t have object labels). Kuka data is then mixed with EDR data in a 1:2 ratio in every training batch to control for regression in original EDR skills.

Training methodology when data has been collected from multiple robots.

Our results indicate that RT-1 is able to acquire new skills by observing other robots’ experiences. In particular, the 22% accuracy seen when training with EDR data alone jumps by almost 2x to 39% when RT-1 is trained on both bin-picking data from Kuka and existing EDR data from robot classrooms, where we collected most of RT-1 data. When training RT-1 on bin-picking data from Kuka alone, and then evaluating it on bin-picking from the EDR robot, we see 0% accuracy. Mixing data from both robots, on the other hand, allows RT-1 to infer the actions of the EDR robot when faced with the states observed by Kuka, without explicit demonstrations of bin-picking on the EDR robot, and by taking advantage of experiences collected by Kuka. This presents an opportunity for future work to combine more multi-robot datasets to enhance robot capabilities.

Long-Horizon SayCan Tasks

RT-1’s high performance and generalization abilities can enable long-horizon, mobile manipulation tasks through SayCan. SayCan works by grounding language models in robotic affordances, and leveraging few-shot prompting to break down a long-horizon task expressed in natural language into a sequence of low-level skills.

SayCan tasks present an ideal evaluation setting to test various features:

1. Long-horizon task success falls exponentially with task length, so high manipulation success is important.
2. Mobile manipulation tasks require multiple handoffs between navigation and manipulation, so the robustness to variations in initial policy conditions (e.g., base position) is essential.
3. The number of possible high-level instructions increases combinatorially with skill-breadth of the manipulation primitive.

We evaluate SayCan with RT-1 and two other baselines (SayCan with Gato and SayCan with BC-Z) in two real kitchens. Below, “Kitchen2” constitutes a much more challenging generalization scene than “Kitchen1”. The mock kitchen used to gather most of the training data was modeled after Kitchen1.

SayCan with RT-1 achieves a 67% execution success rate in Kitchen1, outperforming other baselines. Due to the generalization difficulty presented by the new unseen kitchen, the performance of SayCan with Gato and SayCan with BCZ shapely falls, while RT-1 does not show a visible drop.

The following video shows a few example PaLM-SayCan-RT1 executions of long-horizon tasks in multiple real kitchens.

Conclusion

The RT-1 Robotics Transformer is a simple and scalable action-generation model for real-world robotics tasks. It tokenizes all inputs and outputs, and uses a pre-trained EfficientNet model with early language fusion, and a token learner for compression. RT-1 shows strong performance across hundreds of tasks, and extensive generalization abilities and robustness in real-world settings.

As we explore future directions for this work, we hope to scale the number of robot skills faster by developing methods that allow non-experts to train the robot with directed data collection and model prompting. We also look forward to improving robotics transformers’ reaction speeds and context retention with scalable attention and memory. To learn more, check out the paper, open-sourced RT-1 code, and the project website.

Acknowledgements

This work was done in collaboration with Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Tomas Jackson, Sally Jesmonth, Nikhil Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Isabel Leal, Kuang-Huei Lee, Sergey Levine, Yao Lu, Utsav Malla, Deeksha Manjunath, Igor Mordatch, Ofir Nachum, Carolina Parada, Jodilyn Peralta, Emily Perez, Karl Pertsch, Jornell Quiambao, Kanishka Rao, Michael Ryoo, Grecia Salazar, Pannag Sanketi, Kevin Sayed, Jaspiar Singh, Sumedh Sontakke, Austin Stone, Clayton Tan, Huong Tran, Vincent Vanhoucke, Steve Vega, Quan Vuong, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Tianhe Yu, and Brianna Zitkovich.

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Posted by Alexis Morvan and Trond Andersen, Research Scientists, Google Quantum AI

When quantum computers were first proposed, they were hoped to be a way to better understand the quantum world. With a so-called “quantum simulator,” one could engineer a quantum computer to investigate how various quantum phenomena arise, including those that are intractable to simulate with a classical computer.

But making a useful quantum simulator has been a challenge. Until now, quantum simulations with superconducting qubits have predominantly been used to verify pre-existing theoretical predictions and have rarely explored or discovered new phenomena. Only a few experiments with trapped ions or cold atoms have revealed new insights. Superconducting qubits, even though they are one of the main candidates for universal quantum computing and have demonstrated computational capabilities beyond classical reach, have so far not delivered on their potential for discovery.

In “Formation of Robust Bound States of Interacting Photons”, published in Nature, we describe a previously unpredicted phenomenon first discovered through experimental investigation. First, we present the experimental confirmation of the theoretical prediction of the existence of a composite particle of interacting photons, or a bound state, using the Google Sycamore quantum processor. Second, while studying this system, we discovered that even though one might guess the bound states to be fragile, they remain robust to perturbations that we expected to have otherwise destroyed them. Not only does this open the possibility of designing systems that leverage interactions between photons, it also marks a step forward in the use of superconducting quantum processors to make new scientific discoveries by simulating non-equilibrium quantum dynamics.

Overview

Photons, or quanta of electromagnetic radiation like light and microwaves, typically don’t interact. For example, two intersecting flashlight beams will pass through one another undisturbed. In many applications, like telecommunications, the weak interactions of photons is a valuable feature. For other applications, such as computers based on light, the lack of interactions between photons is a shortcoming.

In a quantum processor, the qubits host microwave photons, which can be made to interact through two-qubit operations. This allows us to simulate the XXZ model, which describes the behavior of interacting photons. Importantly, this is one of the few examples of integrable models, i.e., one with a high degree of symmetry, which greatly reduces its complexity. When we implement the XXZ model on the Sycamore processor, we observe something striking: the interactions force the photons into bundles known as bound states.

Using this well-understood model as a starting point, we then push the study into a less-understood regime. We break the high level of symmetries displayed in the XXZ model by adding extra sites that can be occupied by the photons, making the system no longer integrable. While this nonintegrable regime is expected to exhibit chaotic behavior where bound states dissolve into their usual, solitary selves, we instead find that they survive!

Bound Photons

To engineer a system that can support the formation of bound states, we study a ring of superconducting qubits that host microwave photons. If a photon is present, the value of the qubit is “1”, and if not, the value is “0”. Through the so-called “fSim” quantum gate, we connect neighboring sites, allowing the photons to hop around and interact with other photons on the nearest-neighboring sites.

Superconducting qubits can be occupied or unoccupied with microwave photons. The “fSim” gate operation allows photons to hop and interact with each other. The corresponding unitary evolution has a hopping term between two sites (orange) and an interaction term corresponding to an added phase when two adjacent sites are occupied by a photon.

We implement the fSim gate between neighboring qubits (left) to effectively form a ring of 24 interconnected qubits on which we simulate the behavior of the interacting photons (right).

The interactions between the photons affect their so-called “phase.” This phase keeps track of the oscillation of the photon’s wavefunction. When the photons are non-interacting, their phase accumulation is rather uninteresting. Like a well-rehearsed choir, they’re all in sync with one another. In this case, a photon that was initially next to another photon can hop away from its neighbor without getting out of sync. Just as every person in the choir contributes to the song, every possible path the photon can take contributes to the photon’s overall wavefunction. A group of photons initially clustered on neighboring sites will evolve into a superposition of all possible paths each photon might have taken.

When photons interact with their neighbors, this is no longer the case. If one photon hops away from its neighbor, its rate of phase accumulation changes, becoming out of sync with its neighbors. All paths in which the photons split apart overlap, leading to destructive interference. It would be like each choir member singing at their own pace — the song itself gets washed out, becoming impossible to discern through the din of the individual singers. Among all the possible configuration paths, the only possible scenario that survives is the configuration in which all photons remain clustered together in a bound state. This is why interaction can enhance and lead to the formation of a bound state: by suppressing all other possibilities in which photons are not bound together.

Left: Evolution of interacting photons forming a bound state. Right: Time goes from left to right, each path represents one of the paths that can break the 2-photon bonded state. Due to interactions, these paths interfere destructively, preventing the photons from splitting apart.

Occupation probability versus gate cycle, or discrete time step, for n-photon bound states. We prepare bound states of varying sizes and watch them evolve. We observe that the majority of the photons (darker colors) remain bound together.

In our processor, we start by putting two to five photons on adjacent sites (i.e., initializing two to five adjacent qubits in “1”, and the remaining qubits in “0”), and then study how they propagate. First, we notice that in the theoretically predicted parameter regime, they remain stuck together. Next, we find that the larger bound states move more slowly around the ring, consistent with the fact that they are “heavier”. This can be seen in the plot above where the lattice sites closest to Site 12, the initial position of the photons, remain darker than the others with increasing number of photons (n_ph) in the bound state, indicating that with more photons bound together there is less propagation around the ring.

Bound States Behave Like Single Composite Particles

To more rigorously show that the bound states indeed behave as single particles with well-defined physical properties, we devise a method to measure how the energy of the particles changes with momentum, i.e., the energy-momentum dispersion relation.

To measure the energy of the bound state, we use the fact that the energy difference between two states determines how fast their relative phase grows with time. Hence, we prepare the bound state in a superposition with the state that has no photons, and measure their phase difference as a function of time and space. Then, to convert the result of this measurement to a dispersion relation, we utilize a Fourier transform, which translates position and time into momentum and energy, respectively. We’re left with the familiar energy-momentum relationship of excitations in a lattice.

Spectroscopy of bound states. We compare the phase accumulation of an n-photon bound state with that of the vacuum (no photons) as a function of lattice site and time. A 2D Fourier transform yields the dispersion relation of the bound-state quasiparticle.

Breaking Integrability

The above system is “integrable,” meaning that it has a sufficient number of conserved quantities that its dynamics are constrained to a small part of the available computational space. In such integrable regimes, the appearance of bound states is not that surprising. In fact, bound states in similar systems were predicted in 2012, then observed in 2013. However, these bound states are fragile and their existence is usually thought to derive from integrability. For more complex systems, there is less symmetry and integrability is quickly lost. Our initial idea was to probe how these bound states disappear as we break integrability to better understand their rigidity.

To break integrability, we modify which qubits are connected with fSim gates. We add qubits so that at alternating sites, in addition to hopping to each of its two nearest-neighboring sites, a photon can also hop to a third site oriented radially outward from the ring.

While a bound state is constrained to a very small part of phase space, we expected that the chaotic behavior associated with integrability breaking would allow the system to explore the phase space more freely. This would cause the bound states to break apart. We find that this is not the case. Even when the integrability breaking is so strong that the photons are equally likely to hop to the third site as they are to hop to either of the two adjacent ring sites, the bound state remains intact, up to the decoherence effect that makes them slowly decay (see paper for details).

Top: New geometry to break integrability. Alternating sites are connected to a third site oriented radially outward. This increases the complexity of the system, and allows for potentially chaotic behavior. Bottom: Despite this added complexity pushing the system beyond integrability, we find that the 3-photon bound state remains stable even for a relatively large perturbation. The probability of remaining bound decreases slowly due to decoherence (see paper).

Conclusion

We don’t yet have a satisfying explanation for this unexpected resilience. We speculate that it may be related to a phenomenon called prethermalization, where incommensurate energy scales in the system can prevent a system from reaching thermal equilibrium as quickly as it otherwise would. We believe further investigations will hopefully lead to new insights into many-body quantum physics, including the interplay of prethermalization and integrability.

Acknowledgements

We would like to thank our Quantum Science Communicator Katherine McCormick for her help writing this blog post.

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Posted by Krishna Giri Narra, Software Engineer, Google, and Chiyuan Zhang, Research Scientist, Google Research

Ad technology providers widely use machine learning (ML) models to predict and present users with the most relevant ads, and to measure the effectiveness of those ads. With increasing focus on online privacy, there’s an opportunity to identify ML algorithms that have better privacy-utility trade-offs. Differential privacy (DP) has emerged as a popular framework for developing ML algorithms responsibly with provable privacy guarantees. It has been extensively studied in the privacy literature, deployed in industrial applications and employed by the U.S. Census. Intuitively, the DP framework enables ML models to learn population-wide properties, while protecting user-level information.

When training ML models, algorithms take a dataset as their input and produce a trained model as their output. Stochastic gradient descent (SGD) is a commonly used non-private training algorithm that computes the average gradient from a random subset of examples (called a mini-batch), and uses it to indicate the direction towards which the model should move to fit that mini-batch. The most widely used DP training algorithm in deep learning is an extension of SGD called DP stochastic gradient descent (DP-SGD).

DP-SGD includes two additional steps: 1) before averaging, the gradient of each example is norm-clipped if the L2 norm of the gradient exceeds a predefined threshold; and 2) Gaussian noise is added to the average gradient before updating the model. DP-SGD can be adapted to any existing deep learning pipeline with minimal changes by replacing the optimizer, such as SGD or Adam, with their DP variants. However, applying DP-SGD in practice could lead to a significant loss of model utility (i.e., accuracy) with large computational overheads. As a result, various research attempts to apply DP-SGD training on more practical, large-scale deep learning problems. Recent studies have also shown promising DP training results on computer vision and natural language processing problems.

In “Private Ad Modeling with DP-SGD”, we present a systematic study of DP-SGD training on ads modeling problems, which pose unique challenges compared to vision and language tasks. Ads datasets often have a high imbalance between data classes, and consist of categorical features with large numbers of unique values, leading to models that have large embedding layers and highly sparse gradient updates. With this study, we demonstrate that DP-SGD allows ad prediction models to be trained privately with a much smaller utility gap than previously expected, even in the high privacy regime. Moreover, we demonstrate that with proper implementation, the computation and memory overhead of DP-SGD training can be significantly reduced.

Evaluation

We evaluate private training using three ads prediction tasks: (1) predicting the click-through rate (pCTR) for an ad, (2) predicting the conversion rate (pCVR) for an ad after a click, and 3) predicting the expected number of conversions (pConvs) after an ad click. For pCTR, we use the Criteo dataset, which is a widely used public benchmark for pCTR models. We evaluate pCVR and pConvs using internal Google datasets. pCTR and pCVR are binary classification problems trained with the binary cross entropy loss and we report the test AUC loss (i.e., 1 – AUC). pConvs is a regression problem trained with Poisson log loss (PLL) and we report the test PLL.

For each task, we evaluate the privacy-utility trade-off of DP-SGD by the relative increase in the loss of privately trained models under various privacy budgets (i.e., privacy loss). The privacy budget is characterized by a scalar ε, where a lower ε indicates higher privacy. To measure the utility gap between private and non-private training, we compute the relative increase in loss compared to the non-private model (equivalent to ε = ∞). Our main observation is that on all three common ad prediction tasks, the relative loss increase could be made much smaller than previously expected, even for very high privacy (e.g., ε <= 1) regimes.

DP-SGD results on three ads prediction tasks. The relative increase in loss is computed against the non-private baseline (i.e., ε = ∞) model of each task.

Improved Privacy Accounting

Privacy accounting estimates the privacy budget (ε) for a DP-SGD trained model, given the Gaussian noise multiplier and other training hyperparameters. Rényi Differential Privacy (RDP) accounting has been the most widely used approach in DP-SGD since the original paper. We explore the latest advances in accounting methods to provide tighter estimates. Specifically, we use connect-the-dots for accounting based on the privacy loss distribution (PLD). The following figure compares this improved accounting with the classical RDP accounting and demonstrates that PLD accounting improves the AUC on the pCTR dataset for all privacy budgets (ε).

Large Batch Training

Batch size is a hyperparameter that affects different aspects of DP-SGD training. For instance, increasing the batch size could reduce the amount of noise added during training under the same privacy guarantee, which reduces the training variance. The batch size also affects the privacy guarantee via other parameters, such as the subsampling probability and training steps. There is no simple formula to quantify the impact of batch sizes. However, the relationship between batch size and the noise scale is quantified using privacy accounting, which calculates the required noise scale (measured in terms of the standard deviation) under a given privacy budget (ε) when using a particular batch size. The figure below plots such relations in two different scenarios. The first scenario uses fixed epochs, where we fix the number of passes over the training dataset. In this case, the number of training steps is reduced as the batch size increases, which could result in undertraining the model. The second, more straightforward scenario uses fixed training steps (fixed steps).

The relationship between batch size and noise scales. Privacy accounting requires a noise standard deviation, which decreases as the batch size increases, to meet a given privacy budget. As a result, by using much larger batch sizes than the non-private baseline (indicated by the vertical dotted line), the scale of Gaussian noise added by DP-SGD can be significantly reduced.

In addition to allowing a smaller noise scale, larger batch sizes also allow us to use a larger threshold of norm clipping each per-example gradient as required by DP-SGD. Since the norm clipping step introduces biases in the average gradient estimation, this relaxation mitigates such biases. The table below compares the results on the Criteo dataset for pCTR with a standard batch size (1,024 examples) and a large batch size (16,384 examples), combined with large clipping and increased training epochs. We observe that large batch training significantly improves the model utility. Note that large clipping is only possible with large batch sizes. Large batch training was also found to be essential for DP-SGD training in Language and Computer Vision domains.

The effects of large batch training. For three different privacy budgets (ε), we observe that when training the pCTR models with large batch size (16,384), the AUC is significantly higher than with regular batch size (1,024).

Fast per-example Gradient Norm Computation

The per-example gradient norm calculation used for DP-SGD often causes computational and memory overhead. This calculation removes the efficiency of standard backpropagation on accelerators (like GPUs) that compute the average gradient for a batch without materializing each per-example gradient. However, for certain neural network layer types, an efficient gradient norm computation algorithm allows the per-example gradient norm to be computed without the need to materialize the per-example gradient vector. We also note that this algorithm can efficiently handle neural network models that rely on embedding layers and fully connected layers for solving ads prediction problems. Combining the two observations, we use this algorithm to implement a fast version of the DP-SGD algorithm. We show that Fast-DP-SGD on pCTR can handle a similar number of training examples and the same maximum batch size on a single GPU core as a non-private baseline.

The computation efficiency of our fast implementation (Fast-DP-SGD) on pCTR.

Compared to the non-private baseline, the training throughput is similar, except with very small batch sizes. We also compare it with an implementation utilizing the JAX Just-in-Time (JIT) compilation, which is already much faster than vanilla DP-SGD implementations. Our implementation is not only faster, but it is also more memory efficient. The JIT-based implementation cannot handle batch sizes larger than 64, while our implementation can handle batch sizes up to 500,000. Memory efficiency is important for enabling large-batch training, which was shown above to be important for improving utility.

Conclusion

We have shown that it is possible to train private ads prediction models using DP-SGD that have a small utility gap compared to non-private baselines, with minimum overhead for both computation and memory consumption. We believe there is room for even further reduction of the utility gap through techniques such as pre-training. Please see the paper for full details of the experiments.

Acknowledgements

This work was carried out in collaboration with Carson Denison, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, and Avinash Varadarajan. We thank Silvano Bonacina and Samuel Ieong for many useful discussions.

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Posted by Malaya Jules, Program Manager, Google

This week, the premier conference on Empirical Methods in Natural Language Processing (EMNLP 2022) is being held in Abu Dhabi, United Arab Emirates. We are proud to be a Diamond Sponsor of EMNLP 2022, with Google researchers contributing at all levels. This year we are presenting over 50 papers and are actively involved in 10 different workshops and tutorials.

If you’re registered for EMNLP 2022, we hope you’ll visit the Google booth to learn more about the exciting work across various topics, including language interactions, causal inference, question answering and more. Take a look below to learn more about the Google research being presented at EMNLP 2022 (Google affiliations in bold).

Committees

Organizing Committee includes: Eunsol Choi, Imed Zitouni

Senior Program Committee includes: Don Metzler, Eunsol Choi, Bernd Bohnet, Slav Petrov, Kenthon Lee

Papers

Transforming Sequence Tagging Into A Seq2Seq Task
Karthik Raman, Iftekhar Naim, Jiecao Chen, Kazuma Hashimoto, Kiran Yalasangi, Krishna Srinivasan

On the Limitations of Reference-Free Evaluations of Generated Text
Daniel Deutsch, Rotem Dror, Dan Roth

Chunk-based Nearest Neighbor Machine Translation
Pedro Henrique Martins, Zita Marinho, André F. T. Martins

Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing
Linlu Qiu*, Peter Shaw, Panupong Pasupat, Tianze Shi, Jonathan Herzig, Emily Pitler, Fei Sha, Kristina Toutanova

MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition
David Ifeoluwa Adelani, Graham Neubig, Sebastian Ruder, Shruti Rijhwani, Michael Beukman, Chester Palen-Michel, Constantine Lignos, Jesujoba O. Alabi, Shamsuddeen H. Muhammad, Peter Nabende, Cheikh M. Bamba Dione, Andiswa Bukula, Rooweither Mabuya, Bonaventure F. P. Dossou, Blessing Sibanda, Happy Buzaaba, Jonathan Mukiibi, Godson Kalipe, Derguene Mbaye, Amelia Taylor, Fatoumata Kabore, Chris Chinenye Emezue, Anuoluwapo Aremu, Perez Ogayo, Catherine Gitau, Edwin Munkoh-Buabeng, Victoire M. Koagne, Allahsera Auguste Tapo, Tebogo Macucwa, Vukosi Marivate, Elvis Mboning, Tajuddeen Gwadabe, Tosin Adewumi, Orevaoghene Ahia, Joyce Nakatumba-Nabende, Neo L. Mokono, Ignatius Ezeani, Chiamaka Chukwuneke, Mofetoluwa Adeyemi, Gilles Q. Hacheme, Idris Abdulmumin, Odunayo Ogundepo, Oreen Yousuf, Tatiana Moteu Ngoli, Dietrich Klakow

T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation
Anubhav Jangra, Preksha Nema, Aravindan Raghuveer

Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature
Katherine Thai, Marzena Karpinska, Kalpesh Krishna, Bill Ray, Moira Inghilleri, John Wieting, Mohit Iyyer

ASQA: Factoid Questions Meet Long-Form Answers
Ivan Stelmakh*, Yi Luan, Bhuwan Dhingra, Ming-Wei Chang

Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization
Nishant Yadav, Nicholas Monath, Rico Angell, Manzil Zaheer, Andrew McCallum

CPL: Counterfactual Prompt Learning for Vision and Language Models
Xuehai He, Diji Yang, Weixi Feng, Tsu-Jui Fu, Arjun Akula, Varun Jampani, Pradyumna Narayana, Sugato Basu, William Yang Wang, Xin Eric Wang

Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infilling
Vidhisha Balachandran, Hannaneh Hajishirzi, William Cohen, Yulia Tsvetkov

Dungeons and Dragons as a Dialog Challenge for Artificial Intelligence
Chris Callison-Burch, Gaurav Singh Tomar, Lara J Martin, Daphne Ippolito, Suma Bailis, David Reitter

Exploring Dual Encoder Architectures for Question Answering
Zhe Dong, Jianmo Ni, Daniel M. Bikel, Enrique Alfonseca, Yuan Wang, Chen Qu, Imed Zitouni

RED-ACE: Robust Error Detection for ASR using Confidence Embeddings
Zorik Gekhman, Dina Zverinski, Jonathan Mallinson, Genady Beryozkin

Improving Passage Retrieval with Zero-Shot Question Generation
Devendra Sachan, Mike Lewis, Mandar Joshi, Armen Aghajanyan, Wen-tau Yih, Joelle Pineau, Luke Zettlemoyer

MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text
Wenhu Chen, Hexiang Hu, Xi Chen, Pat Verga, William Cohen

Decoding a Neural Retriever’s Latent Space for Query Suggestion
Leonard Adolphs, Michelle Chen Huebscher, Christian Buck, Sertan Girgin, Olivier Bachem, Massimiliano Ciaramita, Thomas Hofmann

Hyper-X: A Unified Hypernetwork for Multi-Task Multilingual Transfer
Ahmet Üstün, Arianna Bisazza, Gosse Bouma, Gertjan van Noord, Sebastian Ruder

Offer a Different Perspective: Modeling the Belief Alignment of Arguments in Multi-party Debates
Suzanna Sia, Kokil Jaidka, Hansin Ahuja, Niyati Chhaya, Kevin Duh

Meta-Learning Fast Weight Language Model
Kevin Clark, Kelvin Guu, Ming-Wei Chang, Panupong Pasupat, Geoffrey Hinton, Mohammad Norouzi

Large Dual Encoders Are Generalizable Retrievers
Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández Ábrego, Vincent Y. Zhao, Yi Luan, Keith B. Hall, Ming-Wei Chang, Yinfei Yang

CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning
Zeqiu Wu*, Yi Luan, Hannah Rashkin, David Reitter, Hannaneh Hajishirzi, Mari Ostendorf, Gaurav Singh Tomar

Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation
Tu Vu*, Aditya Barua, Brian Lester, Daniel Cer, Mohit Iyyer, Noah Constant

RankGen: Improving Text Generation with Large Ranking Models
Kalpesh Krishna, Yapei Chang, John Wieting, Mohit Iyyer

UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer and Tao Yu

M2D2: A Massively Multi-domain Language Modeling Dataset
Machel Reid, Victor Zhong, Suchin Gururangan, Luke Zettlemoyer

Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation
Jannis Bulian, Christian Buck, Wojciech Gajewski, Benjamin Boerschinger, Tal Schuster

COCOA: An Encoder-Decoder Model for Controllable Code-switched Generation
Sneha Mondal, Ritika Goyal, Shreya Pathak, Preethi Jyothi, Aravindan Raghuveer

Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset (see blog post)
Ashish V. Thapliyal, Jordi Pont-Tuset, Xi Chen, Radu Soricut

“Will You Find These Shortcuts?” A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classification (see blog post)
Jasmijn Bastings, Sebastian Ebert, Polina Zablotskaia, Anders Sandholm, Katja Filippova

Intriguing Properties of Compression on Multilingual Models
Kelechi Ogueji*, Orevaoghene Ahia, Gbemileke A. Onilude, Sebastian Gehrmann, Sara Hooker, Julia Kreutzer

FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue
Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi, Connor Pryor, Luke Yoffe, Deepak Ramachandran, Lise Getoor, Jay Pujara, William Yang Wang

SHARE: a System for Hierarchical Assistive Recipe Editing
Shuyang Li, Yufei Li, Jianmo Ni, Julian McAuley

Context Matters for Image Descriptions for Accessibility: Challenges for Referenceless Evaluation Metrics
Elisa Kreiss, Cynthia Bennett, Shayan Hooshmand, Eric Zelikman, Meredith Ringel Morris, Christopher Potts

Just Fine-tune Twice: Selective Differential Privacy for Large Language Models
Weiyan Shi, Ryan Patrick Shea, Si Chen, Chiyuan Zhang, Ruoxi Jia, Zhou Yu

Findings of EMNLP

Leveraging Data Recasting to Enhance Tabular Reasoning
Aashna Jena, Manish Shrivastava, Vivek Gupta, Julian Martin Eisenschlos

QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation
Krishna Srinivasan, Karthik Raman, Anupam Samanta, Lingrui Liao, Luca Bertelli, Michael Bendersky

Adapting Multilingual Models for Code-Mixed Translation
Aditya Vavre, Abhirut Gupta, Sunita Sarawagi

Table-To-Text generation and pre-training with TABT5
Ewa Andrejczuk, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Yasemin Altun

Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusters
Tal Schuster, Sihao Chen, Senaka Buthpitiya, Alex Fabrikant, Donald Metzler

Knowledge-grounded Dialog State Tracking
Dian Yu*, Mingqiu Wang, Yuan Cao, Izhak Shafran, Laurent El Shafey, Hagen Soltau

Sparse Mixers: Combining MoE and Mixing to Build a More Efficient BERT
James Lee-Thorp, Joshua Ainslie

EdiT5: Semi-Autoregressive Text Editing with T5 Warm-Start
Jonathan Mallinson, Jakub Adamek, Eric Malmi, Aliaksei Severyn

Autoregressive Structured Prediction with Language Models
Tianyu Liu, Yuchen Eleanor Jiang, Nicholas Monath, Ryan Cotterell and Mrinmaya Sachan

Faithful to the Document or to the World? Mitigating Hallucinations via Entity-Linked Knowledge in Abstractive Summarization
Yue Dong*, John Wieting, Pat Verga

Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers
Jieyu Zhao*, Xuezhi Wang, Yao Qin, Jilin Chen, Kai-Wei Chang

Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation
Dongha Lee, Jiaming Shen, Seonghyeon Lee, Susik Yoon, Hwanjo Yu, Jiawei Han

Benchmarking Language Models for Code Syntax Understanding
Da Shen, Xinyun Chen, Chenguang Wang, Koushik Sen, Dawn Song

Large-Scale Differentially Private BERT
Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, Pasin Manurangsi

Towards Tracing Knowledge in Language Models Back to the Training Data
Ekin Akyurek, Tolga Bolukbasi, Frederick Liu, Binbin Xiong, Ian Tenney, Jacob Andreas, Kelvin Guu

Predicting Long-Term Citations from Short-Term Linguistic Influence
Sandeep Soni, David Bamman, Jacob Eisenstein

Workshops

Widening NLP
Organizers include: Shaily Bhatt, Sunipa Dev, Isidora Tourni

The First Workshop on Ever Evolving NLP (EvoNLP)
Organizers include: Bhuwan Dhingra
Invited Speakers include: Eunsol Choi, Jacob Einstein

Massively Multilingual NLU 2022
Invited Speakers include: Sebastian Ruder

Second Workshop on NLP for Positive Impact
Invited Speakers include: Milind Tambe

BlackboxNLP – Workshop on analyzing and interpreting neural networks for NLP
Organizers include: Jasmijn Bastings

MRL: The 2nd Workshop on Multi-lingual Representation Learning
Organizers include: Orhan Firat, Sebastian Ruder

Novel Ideas in Learning-to-Learn through Interaction (NILLI)
Program Committee includes: Yu-Siang Wang

Tutorials

Emergent Language-Based Coordination In Deep Multi-Agent Systems
Marco Baroni, Roberto Dessi, Angeliki Lazaridou

Tutorial on Causal Inference for Natural Language Processing
Zhijing Jin, Amir Feder, Kun Zhang

Modular and Parameter-Efficient Fine-Tuning for NLP Models
Sebastian Ruder, Jonas Pfeiffer, Ivan Vulic

* Work done while at Google

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