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 10129 publications
Rapid initial state preparation for the quantum simulation of strongly correlated molecules and materials
Dominic Berry
Yu Tong
Alec White
Tae In Kim
Lin Lin
Seunghoon Lee
Garnet Chan
arXiv:2409.11748 (2024)
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Studies on quantum algorithms for ground state energy estimation often assume perfect ground state preparation; however, in reality the initial state will have imperfect overlap with the true ground state. Here we address that problem in two ways: by faster preparation of matrix product state (MPS) approximations, and more efficient filtering of the prepared state to find the ground state energy. We show how to achieve unitary synthesis with a Toffoli complexity about $7 \times$ lower than that in prior work, and use that to derive a more efficient MPS preparation method. For filtering we present two different approaches: sampling and binary search. For both we use the theory of window functions to avoid large phase errors and minimise the complexity. We find that the binary search approach provides better scaling with the overlap at the cost of a larger constant factor, such that it will be preferred for overlaps less than about 0.003. Finally, we estimate the total resources to perform ground state energy estimation of FeMoco and Iron cluster systems by estimating ground state overlap on an MPS initial state through extrapolation. With a modest bond dimension of 4000 we estimate a 0.96 overlap squared value producing total resources of $7.5 \times 10^{10}$ Toffoli gates; validating naive estimates where we assume perfect ground state overlap. These extrapolations allay practical concerns of exponential overlap decay in challenging-to-compute chemical systems.
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Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation
Shreyas Havaldar
The Twelfth International Conference on Learning Representations (ICLR) (2024)
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Learning from Label Proportions (LLP) is a learning problem where only aggregate level labels are available for groups of instances, called bags, during training, and the aim is to get the best performance at the instance-level on the test data. This setting arises in domains like advertising and medicine due to privacy considerations. We propose a novel algorithmic framework for this problem that iteratively performs two main steps. For the first step (Pseudo Labeling) in every iteration, we define a Gibbs distribution over binary instance labels that incorporates a) covariate information through the constraint that instances with similar covariates should have similar labels and b) the bag level aggregated label. We then use Belief Propagation (BP) to marginalize the Gibbs distribution to obtain pseudo labels. In the second step (Embedding Refinement), we use the pseudo labels to provide supervision for a learner that yields a better embedding. Further, we iterate on the two steps again by using the second step's embeddings as new covariates for the next iteration. In the final iteration, a classifier is trained using the pseudo labels. Our algorithm displays strong gains against several SOTA baselines for the LLP Binary Classification problem on various dataset types - Small Tabular, Large Tabular and Images. We achieve these improvements with minimal computational overhead above standard supervised learning due to Belief Propagation, for large bag sizes, even for a million samples.
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Practical Performance Guarantees for Pipelined DNN Inference
Kuikui Liu
Proceedings of the 41st International Conference on Machine Learning (2024), pp. 1655-1671
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This work optimizes pipeline parallelism of machine learning model inference by
partitioning computation graphs into $k$ stages and minimizing the running time of the bottleneck stage.
We design practical algorithms for this NP-complete problem
and prove they are nearly optimal in practice by comparing against lower bounds
obtained from solving novel mixed-integer programming (MIP) formulations.
We apply these algorithms and lower-bound techniques
to production models to achieve substantial improvements in the approximation guarantees,
compared to simple combinatorial lower bounds.
For example, our new MIP formulations improve the lower bounds enough to
drop the geometric mean approximation ratio from $2.175$ to $1.082$ across
production data with $k=16$ pipeline stages.
This work shows that while bottleneck partitioning is theoretically hard,
in practice we have a handle on the algorithmic side of the problem and
much of the remaining challenge is in developing more accurate cost models
to give to the partitioning algorithms.
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Conformal Language Modeling
Victor Quach
Adam Fisch
Adam Yala
Jae Ho Sohn
Tommi Jaakkola
Regina Barzilay
ICLR (2024)
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In this paper, we propose a novel approach to conformal prediction (CP) that is adapted to generative, large language models (LLMs). Conformal prediction is a popular technique for deriving prediction sets from machine learning models that have rigorous, statistical performance guarantees. We extend conformal techniques to a broad class of language models that sample from a conditional distribution over the combinatorial, unbounded space of possible text outputs, given some input prompt. Specifically, we translate the process of constructing prediction sets into calibrating a \emph{stopping rule}, under which we draw diverse samples from our model until we are confident that the growing set of candidate answers includes at least one high-quality response. At the same time, we calibrate a \emph{rejection rule} to selectively discard low-quality or redundant responses to reduce sample noise. Under minimal assumptions, we theoretically prove that our resulting output sets contain at least one high-quality answer with some desired probability that a user can set (such as $90\%$), while still remaining empirically precise on average. Furthermore, within this set of sampled candidate answers, we show that we can also accurately identify subsets of individual components (e.g., phrases or sentences) that are each independently correct (e.g., that are not ``hallucinations'')---again, with provably high probability. We demonstrate the effectiveness of our approach on multiple types of large language models applied to tasks in open-domain question answering, text summarization, and radiology report generation.
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GASS: GPU Automated Sharing at Scale
Dragos Sbirlea
Jiafan Zhu
Konstantinos Menychtas
Yuang Liu
Zhijing Gene Qin
The IEEE International Conference on Cloud Computing (CLOUD) 2024 (2024)
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General-purpose GPUs, with their powerful numerical computing capacity, are popular platforms for accelerating machine-learning workloads. However, our experience with a large scale production deployment shows that typical GPU work-loads often fail to keep the GPU pipeline fully occupied, resulting in low overall resource utilization. To address this inefficiency, we have designed and implemented GPU Automated Sharing at Scale (GASS). GASS relies on fine-grained time-multiplexing to let GPU compute resources be shared among different tasks, and on-demand paging to let GPU memory be shared among them. GASS mitigates sharing performance anomalies by using real-time performance monitoring to drive adaptive rescheduling. Our cluster level evaluation shows the aggregated GPU throughput is increased by 50% under GASS and that sharing enables the cluster to support 19% more GPU jobs.
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Beyond Yes and No: Improving Zero-Shot Pointwise LLM Rankers via Scoring Fine-Grained Relevance Labels
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)
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Zero-shot text rankers powered by recent LLMs achieve remarkable ranking performance by simply prompting. Existing prompts for pointwise LLM rankers mostly ask the model to choose from binary relevance labels like "Yes" and "No". However, the lack of intermediate relevance label options may cause the LLM to provide noisy or biased answers for documents that are partially relevant to the query. We propose to incorporate fine-grained relevance labels into the prompt for LLM rankers, enabling them to better differentiate among documents with different levels of relevance to the query and thus derive a more accurate ranking. We study two variants of the prompt template, coupled with different numbers of relevance levels. Our experiments on 8 BEIR data sets show that adding fine-grained relevance labels significantly improves the performance of LLM rankers.
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We're roughly 10 years into the OpenConfig journey. We have implementations in hand from various vendors, and we've gained significant operational experience in the domains of Streaming Telemetry and in Developing Configuration Systems to leverage the developed models. What have we learned? Are the abstractions we've generated the right ones? If not, why? Were we too influenced by the tools and inertia of the time when we made some critical decisions? How do we need to evolve going forward? This discussion is part retrospective/introspective, a candid look at where we've been and what we need to think about as we evolve the next generation of our management (and control) planes. What should we be thinking about as network engineers who write software?
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Rambler: Supporting Writing With Speech via LLM-Assisted Gist Manipulation
Susan Lin
Jeremy Warner
J.D. Zamfirescu-Pereira
Matthew G Lee
Sauhard Jain
Michael Xuelin Huang
Bjoern Hartmann
Can Liu
Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, New York, NY, USA
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Dictation enables efficient text input on mobile devices. However, writing with speech can produce disfluent, wordy, and incoherent text and thus requires heavy post-processing. This paper presents Rambler, an LLM-powered graphical user interface that supports gist-level manipulation of dictated text with two main sets of functions: gist extraction and macro revision. Gist extraction generates keywords and summaries as anchors to support the review and interaction with spoken text. LLM-assisted macro revisions allow users to respeak, split, merge, and transform dictated text without specifying precise editing locations. Together they pave the way for interactive dictation and revision that help close gaps between spontaneously spoken words and well-structured writing. In a comparative study with 12 participants performing verbal composition tasks, Rambler outperformed the baseline of a speech-to-text editor + ChatGPT, as it better facilitates iterative revisions with enhanced user control over the content while supporting surprisingly diverse user strategies.
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Now You See Me, Now You Don't: 'Poverty of the Stimulus' Problems and Arbitrary Correspondences in End-to-End Speech Models
Proceedings of the Second Workshop on Computation and Written Language (CAWL) 2024
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End-to-end models for speech recognition and speech synthesis have many benefits, but we argue they also face a unique set of challenges not encountered in conventional multi-stage hybrid systems, which relied on the explicit injection of linguistic knowledge through resources such as phonemic dictionaries and verbalization grammars. These challenges include handling words with unusual grapheme-to-phoneme correspondences, converting between written forms like ‘12’ and spoken forms such as ‘twelve’, and contextual disambiguation of homophones or homographs. We describe the mitigation strategies that have been used for these problems in end-to-end systems, either implicitly or explicitly, and call out that the most commonly used mitigation techniques are likely incompatible with newly emerging approaches that use minimal amounts of supervised audio training data. We review best-of-both-world approaches that allow the use of end-to-end models combined with traditional linguistic resources, which we show are increasingly straightforward to create at scale, and close with an optimistic outlook for bringing speech technologies to many more languages by combining these strands of research.
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Automatic Histograms: Leveraging Language Models for Text Dataset Exploration
Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA '24), ACM, Honolulu, HI, USA (2024), pp. 9
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Making sense of unstructured text datasets is perennially difficult, yet increasingly relevant with Large Language Models. Data practitioners often rely on dataset summaries, especially distributions of various derived features. Some features, like toxicity or topics, are relevant to many datasets, but many interesting features are domain specific, e.g., instruments and genres for a music dataset, or diseases and symptoms for a medical dataset. Accordingly, data practitioners often run custom analyses for each dataset, which is cumbersome and difficult, or use unsupervised methods. We present AutoHistograms, a visualization tool leveraging LLMs. AutoHistograms automatically identifies relevant entity-based features, visualizes their distributions, and allows the user to interactively query the dataset for new categories of entities. In a user study with (n=10) data practitioners, we observe that participants were able to quickly onboard to AutoHistograms, use the tool to identify actionable insights, and conceptualize a broad range of applicable use cases. We also describe a variety of usage scenarios from different types of users to highlight how this app can provide value in many different contexts. Finally, we present a quantitative evaluation of the tool. Together, this tool and user study contribute to the growing field of LLM-assisted sensemaking tools.
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A lexicographic maximum of a set $X \subseteq R^n$ is a vector in $X$ whose smallest component is as large as possible, and subject to that requirement, whose second smallest component is as large as possible, and so on for the third smallest component, etc. Lexicographic maximization has numerous practical and theoretical applications, including fair resource allocation, analyzing the implicit regularization of learning algorithms, and characterizing refinements of game-theoretic equilibria. We prove that a minimizer in $X$ of the exponential loss function $L_c(x) = \sum_i \exp(-c x_i)$ converges to a lexicographic maximum of $X$ as $c \rightarrow \infty$, provided that $X$ is stable in the sense that a well-known iterative method for finding a lexicographic maximum of $X$ cannot be made to fail simply by reducing the required quality of each iterate by an arbitrarily tiny degree. Our result holds for both near and exact minimizers of the exponential loss, while earlier convergence results made much stronger assumptions about the set $X$ and only held for the exact minimizer. We are aware of no previous results showing a connection between the iterative method for computing a lexicographic maximum and exponential loss minimization. We show that every convex polytope is stable, but that there exist compact, convex sets that are not stable. We also provide the first analysis of the convergence rate of an exponential loss minimizer (near or exact) and discover a curious dichotomy: While the two smallest components of the vector converge to the lexicographically maximum values very quickly (at roughly the rate $(\log n)/c$), all other components can converge arbitrarily slowly.
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Website Data Transparency in the Browser
Sebastian Zimmeck
Daniel Goldelman
Owen Kaplan
Logan Brown
Justin Casler
Judeley Jean-Charles
Joe Champeau
24th Privacy Enhancing Technologies Symposium (PETS 2024), PETS (to appear)
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Data collection by websites and their integrated third parties is often not transparent. We design privacy interfaces for the browser to help people understand who is collecting which data from them. In a proof of concept browser extension, Privacy Pioneer, we implement a privacy popup, a privacy history interface, and a watchlist to notify people when their data is collected. For detecting location data collection, we develop a machine learning model based on TinyBERT, which reaches an average F1 score of 0.94. We supplement our model with deterministic methods to detect trackers, collection of personal data, and other monetization techniques. In a usability study with 100 participants 82% found Privacy Pioneer easy to understand and 90% found it useful indicating the value of privacy interfaces directly integrated in the browser.
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There is a potential future where the content created by a human and an AI are indistinguishable. In this future, if you can’t tell the difference, does it matter? We conducted a 3 (Assigned creator: human, human with AI assistance, AI) by 4 (Context: news, travel, health, and jokes) mixed-design experiment where participants evaluated human-written content that was presented as created by a human, a human with AI assistance, or an AI. We found that participants felt more negatively about the content creator and were less satisfied when they thought AI was used, but assigned creator had no effect on content judgments. We also identified five interpretations for how participants thought AI use affected the content creation process. Our work suggests that informing users about AI use may not have the intended effect of helping consumers make content judgments and may instead damage the relationship
between creators and followers.
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Hovering Over the Key to Text Input in XR
Diar Abdlkarim
Arpit Bhatia
Stuart Macgregor
Jason Fotso-Puepi
Hasti Seifi
Massimiliano Di Luca
Karan Ahuja
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Virtual, Mixed, and Augmented Reality (XR) technologies hold immense potential for transforming productivity beyond PC. Therefore there is a critical need for improved text input solutions for XR. However, achieving efficient text input in these environments remains a significant challenge. This paper examines the current landscape of XR text input techniques, focusing on the importance of keyboards (both physical and virtual) as essential tools. We discuss the unique challenges and opportunities presented by XR, synthesizing key trends from existing solutions.
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Motivated by recent advances in large language models for NLP, we design a time-series foundation model for forecasting whose out-of-the-box zero-shot performance on a variety of datasets, matches the accuracy of state-of-the-art supervised forecasting models for each individual dataset. Our model is based on pretraining a patched-decoder style attention model on a large time series dataset, and can work well across different forecasting history lengths, prediction lengths and temporal granularities.
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