Publications
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
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1 - 15 of 10133 publications
Do Large Code Models Understand Programming Concepts? A Black Box Approach
Ashish Hooda
Aaron Wilson
Kassem Fawaz
Somesh Jha
(2024) (to appear)
Preview abstract
Large Language Models have been able to replicate their success from text generation to coding tasks. While a lot of work has made it clear that they have remarkable performance on tasks such as code completion and editing, it is still unclear as to why. We help bridge this gap by exploring to what degree do auto-regressive models understand the logical constructs of the underlying programs. We propose CAPP, a counterfactual testing framework to evaluate whether large code models understand programming concepts. With only black-box access to the model, we use CAPP to evaluate 10 popular large code models for 5 different programming concepts. Our findings suggest that current models lack understanding of concepts such as data flow and control flow.
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Factual and Personalized Recommendation Language Modeling with Reinforcement Learning
Jihwan Jeong
Mohammad Ghavamzadeh
Proceedings of the First Conference on Language Modeling (COLM-24), Philadelphia (2024)
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Recommender systems (RSs) play a central role in connecting users to products, content and services by matching candidate items to users based on their preferences. While existing RSs often rely on implicit user feedback on recommended items (e.g., clicks, watches, ratings), conversational recommender systems are interacting with users to provide tailored recommendations in natural language. In this work, we aim to develop a recommender language model (LM) that is capable of generating compelling endorsement presentations of relevant items to users, to better explain the details of the items, to connect the items with users’ preferences, and to enhance the likelihood of users accepting recommendations. Specifically, such an LLM-based recommender can understand users’ preferences from users’ RS embeddings summarizing feedback history, output corresponding responses that not only are factually-grounded, but also explain whether these items satisfy users’ preferences in a convincing manner. The pivotal question is how one can gauge the performance of such a LLM recommender. Equipped with a joint reward function that measures factual consistency, convincingness, and personalization, not only can we evaluate the efficacies of different recommender LMs, but we can also utilize this metric as a form of AI feedback to fine-tune our LLM agent via reinforcement learning (RL). Building upon the MovieLens movie recommendation benchmark, we developed a novel conversational recommender delivering personalized movie narratives to users. This work lays the groundwork for recommendation systems that prioritize individualized user experiences without compromising on transparency and integrity.
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DynaMITE-RL: A Dynamic Model for Improved Temporal Meta Reinforcement Learning
Anthony Liang
Erdem Biyik
Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS-24), Vancouver (2024)
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We introduce a meta-reinforcement learning (meta-RL) approach, called DynaMITE-RL, to perform approximate inference in environments where the latent information evolves slowly between subtrajectories called sessions.
We identify three key modifications to contemporary meta-RL methods: consistency of latent information during sessions, session masking, and prior latent conditioning.
We demonstrate the necessity of these modifications on various downstream applications from discrete Gridworld environments to continuous control and simulated robot assistive tasks and find that our approach significantly outperforms contemporary baselines.
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Measuring Developer Experience with a Longitudinal Survey
Jessica Lin
Jill Dicker
IEEE Software (2024)
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At Google, we’ve been running a quarterly large-scale survey with developers since 2018. In this article, we will discuss how we run EngSat, some of our key learnings over the past 6 years, and how we’ve evolved our approach to meet new needs and challenges.
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Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines
Aditya U Kale
Alastair Dennison
Alexander Martindale
An Wen Chan
Andrew Beam
Benjamin Ng
Cecilia S. Lee
Christopher Kelly
Christopher Yau
David Moher
Gary Collins
Lauren Oakden-Rayner
Lavinia Ferrante di Ruffano
Melanie Calvert
Melissa D McCradden
Pearse Keane
Robert Golub
Samantha Cruz Rivera
Victoria Ngai
Xiaoxuan Liu
Nature Communications (2024)
Preview abstract
The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77–94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines.
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Assessing Web Fingerprinting Risk
Robert Busa-Fekete
Antonio Sartori
Proceedings of the ACM Web Conference (WWW 2024)
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Modern Web APIs allow developers to provide extensively customized experiences for website visitors, but the richness of the device information they provide also make them vulnerable to being abused by malign actors to construct browser fingerprints, device-specific identifiers that enable covert tracking of users even when cookies are disabled.
Previous research has established entropy, a measure of information, as the key metric for quantifying fingerprinting risk. Earlier studies that estimated the entropy of Web APIs were based on data from a single website or were limited to an extremely small sample of clients. They also analyzed each Web API separately and then summed their entropies to quantify overall fingerprinting risk, an approach that can lead to gross overestimates.
We provide the first study of browser fingerprinting which addresses the limitations of prior work. Our study is based on actual visited pages and Web API function calls reported by tens of millions of real Chrome browsers in-the-wild. We accounted for the dependencies and correlations among Web APIs, which is crucial for obtaining more realistic entropy estimates. We also developed a novel experimental design that accurately estimates entropy while never observing too much information from any single user. Our results provide an understanding of the distribution of entropy for different website categories, confirm the utility of entropy as a fingerprinting proxy, and offer a method for evaluating browser enhancements which are intended to mitigate fingerprinting.
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V2Meow: Meowing to the Visual Beat via Video-to-Music Generation
Chris Donahue
Dima Kuzmin
Judith Li
Kun Su
Mauro Verzetti
Qingqing Huang
Yu Wang
Vol. 38 No. 5: AAAI-24 Technical Tracks 5, AAAI Press (2024), pp. 4952-4960
Preview abstract
Video-to-music generation demands both a temporally localized high-quality listening experience and globally aligned video-acoustic signatures. While recent music generation models excel at the former through advanced audio codecs, the exploration of video-acoustic signatures has been confined to specific visual scenarios. In contrast, our research confronts the challenge of learning globally aligned signatures between video and music directly from paired music and videos, without explicitly modeling domain-specific rhythmic or semantic relationships. We propose V2Meow, a video-to-music generation system capable of producing high-quality music audio for a diverse range of video input types using a multi-stage autoregressive model. Trained on 5k hours of music audio clips paired with video frames mined from in-the-wild music videos, V2Meow is competitive with previous domain-specific models when evaluated in a zero-shot manner. It synthesizes high-fidelity music audio waveforms solely by conditioning on pre-trained general purpose visual features extracted from video frames, with optional style control via text prompts. Through both qualitative and quantitative evaluations, we demonstrate that our model outperforms various existing music generation systems in terms of visual-audio correspondence and audio quality. Music samples are available at tinyurl.com/v2meow.
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Batch Calibration: Rethinking Calibration For In-Context Learning And Prompt Engineering
Lev Proleev
Diana Mincu
International Conference on Learning Representations (ICLR) (2024)
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Prompting and in-context learning (ICL) have become efficient learning paradigms for large language models (LLMs). However, LLMs suffer from prompt brittleness and various bias factors in the prompt, including but not limited to the formatting, the choice verbalizers, and the ICL examples. To address this problem that results in unexpected performance degradation, calibration methods have been developed to mitigate the effects of these biases while recovering LLM performance. In this work, we first conduct a systematic analysis of the existing calibration methods, where we both provide a unified view and reveal the failure cases. Inspired by these analyses, we propose Batch Calibration (BC), a simple yet intuitive method that controls the contextual bias from the batched input, unifies various prior approaches, and effectively addresses the aforementioned issues. BC is zero-shot, inference-only, and incurs negligible additional costs. In the few-shot setup, we further extend BC to allow it to learn the contextual bias from labeled data. We validate the effectiveness of BC with PaLM 2-(S, M, L) and CLIP models and demonstrate state-of-the-art performance over previous calibration baselines across more than 10 natural language understanding and image classification tasks.
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FaceFolds: Meshed Radiance Manifolds for Efficient Volumetric Rendering of Dynamic Faces
Safa C. Medin
Gengyan Li
Stephan Garbin
Philip Davidson
Gregory W. Wornell
Thabo Beeler
Abhimitra Meka
Proceedings of the ACM on Computer Graphics and Interactive Techniques, 7 (2024), pp. 1-17
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3D rendering of dynamic face captures is a challenging problem, and it demands improvements on several fronts---photorealism, efficiency, compatibility, and configurability. We present a novel representation that enables high-quality volumetric rendering of an actor's dynamic facial performances with minimal compute and memory footprint. It runs natively on commodity graphics soft- and hardware, and allows for a graceful trade-off between quality and efficiency. Our method utilizes recent advances in neural rendering, particularly learning discrete radiance manifolds to sparsely sample the scene to model volumetric effects. We achieve efficient modeling by learning a single set of manifolds for the entire dynamic sequence, while implicitly modeling appearance changes as temporal canonical texture. We export a single layered mesh and view-independent RGBA texture video that is compatible with legacy graphics renderers without additional ML integration. We demonstrate our method by rendering dynamic face captures of real actors in a game engine, at comparable photorealism to state-of-the-art neural rendering techniques at previously unseen frame rates.
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We present Prequal (\emph{Probing to Reduce Queuing and Latency}), a load balancer
for distributed multi-tenant systems. Prequal aims to minimize
real-time request latency in the presence of heterogeneous server
capacities and non-uniform, time-varying antagonist load. It actively probes
server load to leverage the \emph{power of $d$ choices}
paradigm, extending it with asynchronous and reusable probes. Cutting
against received wisdom, Prequal does not balance CPU load, but instead
selects servers according to estimated latency and active requests-in-flight
(RIF). We explore its major design features on a testbed system
and evaluate it on YouTube, where it has been deployed for more than two years. Prequal has dramatically decreased tail latency, error rates, and resource use, enabling YouTube and
other production systems at Google to run at much higher utilization.
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Experiencing Thing2Reality: Transforming 2D Content into Conditioned Multiviews and 3D Gaussian Objects for XR Communication
Erzhen Hu
Mingyi Li
Seongkook Heo
Adjunct Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology, ACM (2024)
Preview abstract
During remote communication, participants share both digital and physical content, such as product designs, digital assets, and environments, to enhance mutual understanding. Recent advances in augmented communication have facilitated users to swiftly create and share digital 2D copies of physical objects from video feeds into a shared space. However, the conventional 2D representation of digital objects restricts users’ ability to spatially reference items in a shared immersive environment. To address these challenges, we propose Thing2Reality, an Extended Reality (XR) communication platform designed to enhance spontaneous discussions regard-ing both digital and physical items during remote sessions. WithThing2Reality, users can quickly materialize ideas or physical objects in immersive environments and share them as conditioned multiview renderings or 3D Gaussians. Our system enables users to interact with remote objects or discuss concepts in a collaborative manner.
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Mindful Breathing as an Effective Technique in the Management of Hypertension
Aravind Natarajan
Hulya Emir-Farinas
Hao-Wei Su
Frontiers in Physiology, N/A (2024), N/A
Preview abstract
Introduction: Hypertension is one of the most important, modifiable risk factors for cardiovascular disease. The popularity of wearable devices provides an opportunity to test whether device guided slow mindful breathing may serve as a non-pharmacological treatment in the management of hypertension.
Methods: Fitbit Versa-3 and Sense devices were used for this study. In addition, participants were required to own an FDA or Health Canada approved blood pressure measuring device. Advertisements were shown to 655,910 Fitbit users, of which 7,365 individuals expressed interest and filled out the initial survey. A total of 1,918 participants entered their blood pressure readings on at least 1 day and were considered enrolled in the study. Participants were instructed to download a guided mindful breathing app on their smartwatch device, and to engage with the app once a day prior to sleep. Participants measured their systolic and diastolic blood pressure prior to starting each mindful breathing session, and again after completion. All measurements were self reported. Participants were located in the United States or Canada.
Results: Values of systolic and diastolic blood pressure were reduced following mindful breathing. There was also a decrease in resting systolic and diastolic measurements when measured over several days. For participants with a systolic pressure ≥ 130 mmHg, there was a decrease of 9.7 mmHg following 15 min of mindful breathing at 6 breaths per minute. When measured over several days, the resting systolic pressure decreased by an average of 4.3 mmHg.
Discussion: Mindful breathing for 15 min a day, at a rate of 6 breaths per minute is effective in lowering blood pressure, and has both an immediate, and a short term effect (over several days). This large scale study demonstrates that device guided mindful breathing with a consumer wearable for 15 min a day is effective in lowering blood pressure, and a helpful complement to the standard of care.
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Expressing and Analyzing Quantum Algorithms with Qualtran
Charles Yuan
Anurudh Peduri
arXiv::2409.04643 (2024)
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Quantum computing's transition from theory to reality has spurred the need for novel software tools to manage the increasing complexity, sophistication, toil, and chance for error of quantum algorithm development. We present Qualtran, an open-source library for representing and analyzing quantum algorithms. Using carefully chosen abstractions and data structures, we can simulate and test algorithms, automatically generate information-rich diagrams, and tabulate resource requirements. Qualtran offers a \emph{standard library} of algorithmic building blocks that are essential for modern cost-minimizing compilations. Its capabilities are showcased through the re-analysis of key algorithms in Hamiltonian simulation, chemistry, and cryptography. The resulting architecture-independent resource counts can be forwarded to our implementation of cost models to estimate physical costs like wall-clock time and number of physical qubits assuming a surface-code architecture. Qualtran provides a foundation for explicit constructions and reproducible analysis, fostering greater collaboration within the quantum algorithm development community. We believe tools like Qualtran will accelerate progress in the field.
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The InterPlanetary File System (IPFS) is on its way to becoming the backbone of the next generation of the web. However, it suffers from several performance bottlenecks, particularly on the content retrieval path, which are often difficult to debug. This is because content retrieval involves multiple peers on the decentralized network and the issue could lie anywhere in the network. Traditional debugging tools are insufficient to help web developers who face the challenge of slow loading websites and detrimental user experience. This limits the adoption and future scalability of IPFS.
In this paper, we aim to gain valuable insights into how content retrieval requests propagate within the IPFS network as well as identify potential performance bottlenecks which could lead to opportunities for improvement. We propose a custom tracing framework that generates and manages traces for crucial events that take place on each peer during content retrieval. The framework leverages event semantics to build a timeline of each protocol involved in the retrieval, helping developers pinpoint problems. Additionally, it is resilient to malicious behaviors of the peers in the decentralized environment.
We have implemented this framework on top of an existing IPFS implementation written in Java called Nabu. Our evaluation shows that the framework can identify network delays and issues with each peer involved in content retrieval requests at a very low overhead.
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Efficient Location Sampling Algorithms for Road Networks
Vivek Kumar
Ameya Velingker
Santhoshini Velusamy
WebConf (2024)
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Many geographic information systems applications rely on the data provided by user devices in the road network. Such applications include traffic monitoring, driving navigation, detecting road closures or the construction of new roads, etc. This signal is collected by sampling locations from the user trajectories and is a critical process for all such systems. Yet, it has not been sufficiently studied in the literature. The most natural way to sample a trajectory is perhaps using a frequency based algorithm, e.g., sample every $x$ seconds. However, as we argue in this paper, such a simple strategy can be very wasteful in terms of resources (e.g., server-side processing, user battery) and in terms of the amount of user data that it maintains. In this work we conduct a horizontal study of various location sampling algorithms (including frequency-based, road geography-based, reservoir-sampling based, etc.) and extract their trade-offs in terms of various metrics of interest, such as, the size of the stored data and the induced quality of training for prediction tasks (e.g., predicting speeds) using the road network of New York City.
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