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
Stable quantum-correlated many-body states through engineered dissipation
Xiao Mi
Alexios Michailidis
Sara Shabani
Jerome Lloyd
Rajeev Acharya
Igor Aleiner
Trond Andersen
Markus Ansmann
Frank Arute
Kunal Arya
Juan Atalaya
Gina Bortoli
Alexandre Bourassa
Leon Brill
Michael Broughton
Bob Buckley
Tim Burger
Nicholas Bushnell
Jimmy Chen
Benjamin Chiaro
Desmond Chik
Charina Chou
Josh Cogan
Roberto Collins
Paul Conner
William Courtney
Alex Crook
Ben Curtin
Alejo Grajales Dau
Dripto Debroy
Agustin Di Paolo
ILYA Drozdov
Andrew Dunsworth
Lara Faoro
Edward Farhi
Reza Fatemi
Vinicius Ferreira
Ebrahim Forati
Brooks Foxen
Élie Genois
William Giang
Dar Gilboa
Raja Gosula
Steve Habegger
Michael Hamilton
Monica Hansen
Sean Harrington
Paula Heu
Markus Hoffmann
Trent Huang
Ashley Huff
Bill Huggins
Sergei Isakov
Justin Iveland
Cody Jones
Pavol Juhas
Kostyantyn Kechedzhi
Marika Kieferova
Alexei Kitaev
Andrey Klots
Alexander Korotkov
Fedor Kostritsa
John Mark Kreikebaum
Dave Landhuis
Pavel Laptev
Kim Ming Lau
Lily Laws
Joonho Lee
Kenny Lee
Yuri Lensky
Alexander Lill
Wayne Liu
Orion Martin
Amanda Mieszala
Shirin Montazeri
Alexis Morvan
Ramis Movassagh
Wojtek Mruczkiewicz
Charles Neill
Ani Nersisyan
Michael Newman
JiunHow Ng
Murray Ich Nguyen
Tom O'Brien
Alex Opremcak
Andre Petukhov
Rebecca Potter
Leonid Pryadko
Charles Rocque
Negar Saei
Kannan Sankaragomathi
Henry Schurkus
Christopher Schuster
Mike Shearn
Aaron Shorter
Noah Shutty
Vladimir Shvarts
Jindra Skruzny
Clarke Smith
Rolando Somma
George Sterling
Doug Strain
Marco Szalay
Alfredo Torres
Guifre Vidal
Cheng Xing
Jamie Yao
Ping Yeh
Juhwan Yoo
Grayson Young
Yaxing Zhang
Ningfeng Zhu
Jeremy Hilton
Anthony Megrant
Yu Chen
Vadim Smelyanskiy
Dmitry Abanin
Science, 383 (2024), pp. 1332-1337
Preview abstract
Engineered dissipative reservoirs have the potential to steer many-body quantum systems toward correlated steady states useful for quantum simulation of high-temperature superconductivity or quantum magnetism. Using up to 49 superconducting qubits, we prepared low-energy states of the transverse-field Ising model through coupling to dissipative auxiliary qubits. In one dimension, we observed long-range quantum correlations and a ground-state fidelity of 0.86 for 18 qubits at the critical point. In two dimensions, we found mutual information that extends beyond nearest neighbors. Lastly, by coupling the system to auxiliaries emulating reservoirs with different chemical potentials, we explored transport in the quantum Heisenberg model. Our results establish engineered dissipation as a scalable alternative to unitary evolution for preparing entangled many-body states on noisy quantum processors.
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Using Early Readouts to Mediate Featural Bias in Distillation
Rishabh Tiwari
Durga Sivasubramanian
Anmol Mekala
Ganesh Ramakrishnan
WACV 2024 (2024)
Preview abstract
Deep networks tend to learn spurious feature-label correlations in real-world supervised learning tasks. This vulnerability is aggravated in distillation, where a (student) model may have less representational capacity than the corresponding teacher model. Often, knowledge of specific problem features is used to reweight instances & rebalance the learning process. We propose a novel early readout mechanism whereby we attempt to predict the label using representations from earlier network layers. We show that these early readouts automatically identify problem instances or groups in the form of confident, incorrect predictions. We improve group fairness measures across benchmark datasets by leveraging these signals to mediate between teacher logits and supervised label. We extend our results to the closely related but distinct problem of domain generalization, which also critically depends on the quality of learned features. We provide secondary analyses that bring insight into the role of feature learning in supervision and distillation.
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Model Swarms: Collaborative Search of Adapted LLM Experts via Swarm Intelligence
Shangbin Feng
Yike Wang
Ace Kulshrestha
Nathalie Rauschmayr
Yejin Choi
Yulia Tsvetkov
Preview abstract
We propose Model Swarms, a collaborative search algorithm to adapt LLM experts via swarm intelligence. Specifically, Model Swarms starts with a pool of LLM experts and a utility function. Guided by the best-found checkpoints across models, diverse LLM experts collaboratively move in the weight space and search for adapted models that optimize the utility function. Compared to existing model composition approaches, Model Swarms offers modularity, works in low-data regimes, and doesn't need assumptions about existing experts and how they should be composed. Extensive experiments demonstrate that Model Swarms could flexibly adapt LLM experts to a single dataset, multi-dataset domains, reward models, as well as diverse human preferences. Further analysis reveals that LLM experts discover previously unseen capabilities in the search process and that Model Swarms enable the weak-to-strong transition of experts through the collaborative search process.
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Preview abstract
Cloud computing architectures are more scalable and economical which is the main reason that has contributed to its popularity. However, they bring their own set of challenges when it comes to workload scheduling and resource utilization because virtual machines (VM) and applications have to share different types of resources like servers, storage, etc. Historically, other strategies for workload balancing and resource management include manual configuration or simplistic heuristics that do not provide effective optimizations of resource usage and performance. In this technical brief, we propose an approach built on the use of unsupervised learning techniques to detect usage patterns perceptively and improve resource utilization, which corresponds to both optimal performance and automatically balanced workload among VMs. We are making use of clustering algorithms to cluster similar workloads and then resource allocation for each group based on demand. The point of this step is to use the resources more effectively so we do not run into resource exhaustion. We also integrate anomaly detection methods within our system for identifying and handling abnormal behavior by both monitoring and placing resources. We experiment with region traces from production workloads to demonstrate the benefits of our approach, showing marked improvements in workload balancing and resource utilization over current practices.
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A data-centric perspective on the information needed for hydrological uncertainty predictions
Andreas Auer
Martin Gauch
Frederik Kratzert
Sepp Hochreiter
Daniel Klotz
Hydrology and Earth System Sciences (2024)
Preview abstract
Uncertainty estimates are fundamental to assess the reliability of predictive models in hydrology. We use the framework of conformal prediction to investigate the impact of temporal and spatial information on uncertainty estimates within hydrological predictions. Integrating recent information significantly enhances overall uncertainty predictions, even with substantial gaps between updates. While local information yields good results on average, it proves to be insufficient for peak-flow predictions. Incorporating global information improves the accuracy of peak-flow bounds, corroborating findings from related studies. Overall, the study underscores the importance of continuous data updates and the integration of global information for robust and efficient uncertainty estimation.
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TRINDs: Assessing the Diagnostic Capabilities of Large Language Models for Tropical and Infectious Diseases
Steve Adudans
Oluwatosin Akande
Chintan Ghate
Sylvanus Aitkins
Geoffrey Siwo
Lynda Osadebe
Nenad Tomašev
Eric Ndombi
Preview abstract
Neglected tropical diseases (NTDs) and infectious diseases disproportionately affect the poorest regions of the world. While large language models (LLMs) have shown promise for medical question answering, there is limited work focused on tropical and infectious disease-specific explorations. We introduce TRINDs, a dataset of 52 tropical and infectious diseases with demographic and semantic clinical and consumer augmentations. We evaluate various context and counterfactual locations to understand their influence on LLM performance. Results show that LLMs perform best when provided with contextual information such as demographics, location, and symptoms. We also develop TRINDs-LM, a tool that enables users to enter symptoms and contextual information to receive a most likely diagnosis. In addition to the LLM evaluations, we also conducted a human expert baseline study to assess the accuracy of human experts in diagnosing tropical and infectious diseases with 7 medical and public health experts. This work demonstrates methods for creating and evaluating datasets for testing and optimizing LLMs, and the use of a tool that could improve digital diagnosis and tracking of NTDs.
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SEMQA: Semi-Extractive Multi-Source Question Answering
Haitian Sun
NAACL (2024) (to appear)
Preview abstract
Recently proposed long-form question answering (QA) systems, supported by large language models (LLMs), have shown promising capabilities. Yet, attributing and verifying their generated abstractive answers can be difficult, and automatically evaluating their accuracy remains an ongoing challenge.
In this paper, we introduce a new QA task for answering multi-answer questions by summarizing multiple diverse sources in a semi-extractive fashion. Specifically, Semi-extractive Multi-source QA (SEMQA) requires models to output a comprehensive answer while mixing between factual quoted spans---copied verbatim from given input sources---and non-factual free-text connectors that glue these spans together into a single cohesive passage. This setting bridges the gap between the outputs of well-grounded but constrained extractive QA systems and more fluent but harder to attribute fully abstractive answers. Particularly, it enables a new mode for language models that leverages their advanced language generation capabilities, while also producing fine in-line attributions by-design that are easy to verify, interpret, and evaluate. To study this task, we create the first dataset of this kind with human-written semi-extractive answers to natural and generated questions, and define text-based evaluation metrics. Experimenting with several LLMs in various settings, we find this task to be surprisingly challenging, demonstrating the importance of our work for developing and studying such consolidation capabilities.
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Its All Relative! -- A Synthetic Query Generation Approach for Improving Zero-Shot Relevance Prediction
Findings of the Association for Computational Linguistics: NAACL 2024
Preview abstract
Recent developments in large language models (LLMs) have shown promise in their ability to generate synthetic query-document pairs by prompting LLMs with as few as 8 demonstrations \cite{dai2022promptagator}.
This has enabled building better IR models especially for tasks which have no training data readily available.
Typically, such synthetic query generation (QGen) approaches condition on an input context (e.g. document) and generate a query that is relevant to that context or condition the QGen model additionally on the relevance label (e.g. relevant vs irrelevant) to generate queries across relevance buckets.
However, we find that such QGen approaches are sub-optimal as it requires the model to reason about the desired label and the input from only a handful of examples, which is not trivial, especially when the relevance buckets are nuanced.
In this work, we propose to reduce this burden of LLMs by generating queries simultaneously for different labels (e.g. relevance buckets).
We hypothesize that instead of asking the model to generate, say, an irrelevant query given an input context, asking the model to generate an irrelevant query with respect to a relevant query is a much simpler task setup for the model to reason about.
Extensive experimentation across seven IR datasets shows that synthetic queries generated in such a fashion translates to a better downstream performance, suggesting that the generated queries are indeed of higher quality.
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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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Augmented Object Intelligence with XR-Objects
Mustafa Doga Dogan
Karan Ahuja
Andrea Colaco
Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology (UIST), ACM (2024), pp. 1-15
Preview abstract
Seamless integration of physical objects as interactive digital entities remains a challenge for spatial computing. This paper explores Augmented Object Intelligence (AOI) in the context of XR, an interaction paradigm that aims to blur the lines between digital and physical by equipping real-world objects with the ability to interact as if they were digital, where every object has the potential to serve as a portal to digital functionalities. Our approach utilizes real-time object segmentation and classification, combined with the power of Multimodal Large Language Models (MLLMs), to facilitate these interactions without the need for object pre-registration. We implement the AOI concept in the form of XR-Objects, an open-source prototype system that provides a platform for users to engage with their physical environment in contextually relevant ways using object-based context menus. This system enables analog objects to not only convey information but also to initiate digital actions, such as querying for details or executing tasks. Our contributions are threefold: (1) we define the AOI concept and detail its advantages over traditional AI assistants, (2) detail the XR-Objects system’s open-source design and implementation, and (3) show its versatility through various use cases and a user study.
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Rich Human Feedback for Text to Image Generation
Katherine Collins
Nicholas Carolan
Youwei Liang
Peizhao Li
Dj Dvijotham
Gang Li
Sarah Young
Jiao Sun
Arseniy Klimovskiy
Preview abstract
Recent Text-to-Image (T2I) generation models such as Stable Diffusion and Imagen have made significant progress in generating high-resolution images based on text descriptions. However, many generated images still suffer from issues such as artifacts/implausibility, misalignment with text descriptions, and low aesthetic quality.
Inspired by the success of Reinforcement Learning with Human Feedback (RLHF) for large language models, prior work collected human-provided scores as feedback on generated images and trained a reward model to improve the T2I generation.
In this paper, we enrich the feedback signal by (i) marking image regions that are implausible or misaligned with the text, and (ii) annotating which keywords in the text prompt are not represented in the image.
We collect such rich human feedback on 18K generated images and train a multimodal transformer to predict these rich feedback automatically.
We show that the predicted rich human feedback can be leveraged to improve image generation, for example, by selecting high-quality training data to finetune and improve the generative models, or by creating masks with predicted heatmaps to inpaint the problematic regions.
Notably, the improvements generalize to models (Muse) beyond those used to generate the images on which human feedback data were collected (Stable Diffusion variants).
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PRISM: A New Lens for Improved Color Understanding
Garima Pruthi
Inderjit Dhillon
Varun Jampani
EMNLP (2024)
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While image-text pre-trained models, such as CLIP, have demonstrated impressive capabilities in learning robust text and image representations, a critical area for substantial improvement remains—precise color understanding. In this paper, we address this limitation by introducing PRISM, a simple yet highly effective method that extends CLIP's capability to grasp the nuances of precise colors. PRISM seamlessly adapts to both recognized HTML colors and out-of-vocabulary RGB inputs through the utilization of our curated dataset of 100 image-text pairs, which can be effortlessly repurposed for fine-tuning with any desired color. Importantly, PRISM achieves these enhancements without compromising CLIP's performance on established benchmarks. During the fine-tuning process, PRISM encourages the disentanglement of color-relevant information from color-irrelevant details. Furthermore, we introduce a novel evaluation framework, ColorLens, featuring both seen and unseen test sets that can be readily repurposed to assess a model's precision in understanding precise colors. Our comprehensive evaluation and results demonstrate significant improvements over baseline models.
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Preview abstract
Learned reweighting (LRW) approaches to supervised learning use an optimization criterion to assign weights for training instances, in order to maximize performance on a representative validation dataset. We pose and formalize the problem of optimized selection of the validation set used in LRW training, to improve classifier generalization. In particular, we show that using hard-to-classify instances in the validation set has both a theoretical connection to, and strong empirical evidence of generalization. We provide an efficient algorithm for training this meta-optimized model, as well as a simple train-twice heuristic for careful comparative study. We demonstrate that LRW with easy validation data performs consistently worse than LRW with hard validation data, establishing the validity of our meta-optimization problem. Our proposed algorithm outperforms a wide range of baselines on a range of datasets and domain shift challenges (Imagenet-1K, CIFAR-100, Clothing-1M, CAMELYON, WILDS, etc.), with ~1% gains using VIT-B on Imagenet. We also show that using naturally hard examples for validation (Imagenet-R / Imagenet-A) in LRW training for Imagenet improves performance on both clean and naturally hard test instances by 1-2%. Secondary analyses show that using hard validation data in an LRW framework improves margins on test data, hinting at the mechanism underlying our empirical gains. We believe this work opens up new research directions for the meta-optimization of meta-learning in a supervised learning context.
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With Great Power Comes Great Responsibility: Security and Privacy Issues of Modern Browser APIs
Harun Oz
Daniele Cono D’Elia
Abbas Acar
Riccardo Lazzeretti
Selcuk Uluagac
IEEE Security and Privacy (2024)
Preview abstract
This paper discusses security and privacy issues in modern Browser
APIs by categorizing them based on their functionality. With this study, we aim to
alert the community about these issues and motivate further research into
analyzing the security and privacy concerns within modern Browser APIs.
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Preview abstract
Graphs are a powerful tool for representing and analyzing complex relationships in real-world applications such as social networks, recommender systems, and computational finance. Reasoning on graphs is essential for drawing inferences about the relationships between entities in a complex system, and to identify hidden patterns and trends. Despite the remarkable progress in automated reasoning with natural text, reasoning on graphs with large language models (LLMs) remains an understudied problem. In this work, we perform the first comprehensive study of encoding graph-structured data as text for consumption by LLMs. We show that LLM performance on graph reasoning tasks varies on three fundamental levels: (1) the graph encoding method, (2) the nature of the graph task itself, and (3) interestingly, the very structure of the graph considered. These novel results provide valuable insight on strategies for encoding graphs as text. Using these insights we illustrate how the correct choice of encoders can boost performance on graph reasoning tasks inside LLMs by 4.8% to 61.8%, depending on the task.
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