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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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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 10100 publications
    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. View details
    Neural Speech and Audio Coding
    Minje Kim
    IEEE Signal Processing Magazine (2024) (to appear)
    Preview abstract This paper explores the integration of model-based and data-driven approaches within the realm of neural speech and audio coding systems. It highlights the challenges posed by the subjective evaluation processes of speech and audio codecs and discusses the limitations of purely data-driven approaches, which often require inefficiently large architectures to match the performance of model-based methods. The study presents hybrid systems as a viable solution, offering significant improvements to the performance of conventional codecs through meticulously chosen design enhancements. Specifically, it introduces a neural network-based signal enhancer designed to post-process existing codecs’ output, along with the autoencoder-based end-to-end models and LPCNet—hybrid systems that combine linear predictive coding (LPC) with neural networks. Furthermore, the paper delves into predictive models operating within custom feature spaces (TF-Codec) or predefined transform domains (MDCTNet) and examines the use of psychoacoustically calibrated loss functions to train end-to-end neural audio codecs. Through these investigations, the paper demonstrates the potential of hybrid systems to advance the field of speech and audio coding by bridging the gap between traditional model-based approaches and modern data-driven techniques. View details
    Preview abstract We present AvatarPopUp, a method for fast, high quality 3D human avatar generation from different input modalities, such as images and text prompts and with control over the generated pose and shape. The common theme is the use of diffusion-based image generation networks that are specialized for each particular task, followed by a 3D lifting network. We purposefully decouple the generation from the 3D modeling which allow us to leverage powerful image synthesis priors, trained on billions of text-image pairs. We fine-tune latent diffusion networks with additional image conditioning for image generation and back-view prediction, and to support qualitatively different multiple 3D hypotheses. Our partial fine-tuning approach allows to adapt the networks for each task without inducing catastrophic forgetting. In our experiments, we demonstrate that our method produces accurate, high-quality 3D avatars with diverse appearance that respect the multimodal text, image, and body control signals. Our approach can produce a 3D model in as few as 2 seconds, a four orders of magnitude speedup w.r.t. the vast majority of existing methods, most of which solve only a subset of our tasks, and with fewer controls. AvatarPopUp enables applications that require the controlled 3D generation of human avatars at scale. View details
    Generalized Power Attacks against Crypto Hardware using Long-Range Deep Learning
    Karel Král
    Marina Zhang
    Transactions on Cryptographic Hardware and Embedded Systems (TCHES), IACR (2024)
    Preview abstract To make cryptographic processors more resilient against side-channel attacks, engineers have developed various countermeasures. However, the effectiveness of these countermeasures is often uncertain, as it depends on the complex interplay between software and hardware. Assessing a countermeasure’s effectiveness using profiling techniques or machine learning so far requires significant expertise and effort to be adapted to new targets which makes those assessments expensive. We argue that including cost-effective automated attacks will help chip design teams to quickly evaluate their countermeasures during the development phase, paving the way to more secure chips.In this paper, we lay the foundations toward such automated system by proposing GPAM, the first deep-learning system for power side-channel analysis that generalizes across multiple cryptographic algorithms, implementations, and side-channel countermeasures without the need for manual tuning or trace preprocessing. We demonstrate GPAM’s capability by successfully attacking four hardened hardware-accelerated elliptic-curve digital-signature implementations. We showcase GPAM’s ability to generalize across multiple algorithms by attacking a protected AES implementation and achieving comparable performance to state-of-the-art attacks, but without manual trace curation and within a limited budget. We release our data and models as an open-source contribution to allow the community to independently replicate our results and build on them. View details
    Meta Lifelong-Learning With Selective and Task-Aware Adaptation
    Thanapapas Horsuwan
    Kasidis Kanwatchara
    Boonserm Kijsirikul
    Peerapon Vateekul
    IEEE Access, 12 (2024), pp. 34099-34115
    Preview abstract Meta-learning has been applied to lifelong language learning due to its ability to find an optimal model for efficient adaptation to any learned tasks. Generally, meta lifelong-learning partially stores samples from seen tasks in a memory and selects some of them to train the model, refresh the knowledge, and adapt the model for inference. However, the sample selection for these steps was usually done in a sub-optimal manner in existing work. Hence, we propose MeLSTA (Meta Lifelong-Learning with Selective and Task-Aware Adaptation) to effectively select the samples based on task identifiers and the adaptation scores reflecting the model behavior after adaptation. The results show that MeLSTA enhances the accuracy by 1.2% over the state-of-the-art while significantly shrinking the training duration by over 6 times. Additionally, our in-depth analysis reveals the strengths and limitations of MeLSTA and existing work, providing useful insights for future designs of meta lifelong-learning for NLP. View details
    RewriteLM: An Instruction-Tuned Large LanguageModel for Text Rewriting
    Yun Zhu
    Simon Tong
    Lei Meng
    Proceedings of the AAAI Conference on Artificial Intelligence, 38(17), 18970-18980 (2024)
    Preview abstract In recent years, Large Language Models (LLMs) have demonstrated impressive zero-shot capabilities in text generation tasks expressed through natural language instructions. However, text rewriting is a challenging task, and unintended modifications can negatively impact the system's performance. To address this challenge, we introduce a novel benchmark for text rewriting that covers a wide variety of rewriting types expressed through natural language instructions. Unlike previous benchmarks, which were primarily focused on limited rewrite styles and sentence-level rewriting, our benchmark is specifically designed to facilitate open-ended rewriting of long-form text. Additionally, we present a strong baseline model, RewriteLM, which is an instruction-tuned large language model for text rewriting. The model is trained using supervised fine-tuning, reward training, and reinforcement learning. To minimize human intervention in the data collection process, we develop new data generation strategies: (1) utilizing high-quality, long-form edits from Wikipedia as our primary natural training data source, (2) generating a synthetic dataset that includes diverse edit types and non-Wiki domains using chain-of-thoughts and the capabilities of LLMs, and (3) employing human-designed heuristic rankers to generate preference data. Our experiments demonstrate the effectiveness of our proposed benchmark and baseline model, as well as the benefits of our data collection strategies in minimizing human intervention. View details
    Preview abstract Sequence labeling is a core task in text understanding for IE/IR systems. Text generation models have increasingly become the go-to solution for such tasks (e.g., entity extraction and dialog slot filling). While most research has focused on the labeling accuracy, a key aspect -- of vital practical importance -- has slipped through the cracks: understanding model confidence. More specifically, we lack a principled understanding of how to reliably gauge the confidence of a model in its predictions for each labeled span. This paper aims to provide some empirical insights on estimating model confidence for generative sequence labeling. Most notably, we find that simply using the decoder's output probabilities is not the best in realizing well-calibrated confidence estimates. As verified over six public datasets of different tasks, we show that our proposed approach -- which leverages statistics from top-k predictions by a beam search -- significantly reduces calibration errors of the predictions of a generative sequence labeling model. View details
    Preview abstract This is the seventh installment of the Developer Productivity for Humans column. This installment focuses on software quality: what it means, how developers see it, how we break it down into 4 types of quality, and the impact these have on each other. View details
    Technical Note: The divide and measure nonconformity – how metrics can mislead when we evaluate on different data partitions
    Daniel Klotz
    Martin Gauch
    Frederik Kratzert
    Jakob Zscheischler
    Hydrology and Earth System Sciences (2024)
    Preview abstract The evaluation of model performance is an essential part of hydrological modeling. However, leveraging the full information that performance criteria provide requires a deep understanding of their properties. This Technical Note focuses on a rather counterintuitive aspect of the perhaps most widely used hydrological metric, the Nash–Sutcliffe efficiency (NSE). Specifically, we demonstrate that the overall NSE of a dataset is not bounded by the NSEs of all its partitions. We term this phenomenon the “divide and measure nonconformity”. It follows naturally from the definition of the NSE, yet because modelers often subdivide datasets in a non-random way, the resulting behavior can have unintended consequences in practice. In this note we therefore discuss the implications of the divide and measure nonconformity, examine its empirical and theoretical properties, and provide recommendations for modelers to avoid drawing misleading conclusions. View details
    Preview abstract Misgendering is the act of referring to someone in way that does not reflect their gender identity. Translation systems, including foundation models capable of translation, can produce errors that result in misgendering harms. To measure the extent of such potential harms when translating into and out of English, we introduce a dataset, MiTTenS, covering 26 languages. The dataset is constructed with handcrafted passages that target known failure patterns, longer synthetically generated passages, and natural passages sourced from multiple domains. We demonstrate the usefulness of the dataset by evaluating both dedicated neural machine translation systems and foundation models, and show that all systems exhibit errors resulting in misgendering harms, even in high resource languages. View details
    50 Shades of Support: A Device-Centric Analysis of Android Security Updates
    Abbas Acar
    Esteban Luques
    Harun Oz
    Ahmet Aris
    Selcuk Uluagac
    Network and Distributed System Security (NDSS) Symposium (2024)
    Preview abstract Android is by far the most popular OS with over three billion active mobile devices. As in any software, uncovering vulnerabilities on Android devices and applying timely patches are both critical. Android Open Source Project (AOSP) has initiated efforts to improve the traceability of security updates through Security Patch Levels (SPLs) assigned to devices. While this initiative provided better traceability for the vulnerabilities, it has not entirely resolved the issues related to the timeliness and availability of security updates for end users. Recent studies on Android security updates have focused on the issue of delay during the security update roll-out, largely attributing this to factors related to fragmentation. However, these studies fail to capture the entire Android ecosystem as they primarily examine flagship devices or do not paint a comprehensive picture of the Android devices’ lifecycle due to the datasets spanning over a short timeframe. To address this gap in the literature, we utilize a device-centric approach to analyze the security update behavior of Android devices. Our approach aims to understand the security update distribution behavior of OEMs (e.g., Samsung) by using a representative set of devices from each OEM and characterize the complete lifecycle of an average Android device. We obtained 367K official security update records from public sources, span- ning from 2014 to 2023. Our dataset contains 599 unique devices from four major OEMs that are used in 97 countries and are associated with 109 carriers. We identify significant differences in the roll-out of security updates across different OEMs, device models/types, and geographical regions across the world. Our findings show that the reasons for the delay in the roll-out of security updates are not limited to fragmentation but also involve OEM-specific factors. Our analysis also uncovers certain key issues that can be readily addressed as well as exemplary practices that can be immediately adopted by OEMs in practice. View details
    Neural general circulation models for weather and climate
    Dmitrii Kochkov
    Janni Yuval
    Jamie Smith
    Griffin Mooers
    Milan Kloewer
    James Lottes
    Peter Dueben
    Samuel Hatfield
    Peter Battaglia
    Alvaro Sanchez
    Matthew Willson
    Nature, 632 (2024), pp. 1060-1066
    Preview abstract General circulation models (GCMs) are the foundation of weather and climate prediction. GCMs are physics-based simulators that combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes such as cloud formation. Recently, machine-learning models trained on reanalysis data have achieved comparable or better skill than GCMs for deterministic weather forecasting. However, these models have not demonstrated improved ensemble forecasts, or shown sufficient stability for long-term weather and climate simulations. Here we present a GCM that combines a differentiable solver for atmospheric dynamics with machine-learning components and show that it can generate forecasts of deterministic weather, ensemble weather and climate on par with the best machine-learning and physics-based methods. NeuralGCM is competitive with machine-learning models for one- to ten-day forecasts, and with the European Centre for Medium-Range Weather Forecasts ensemble prediction for one- to fifteen-day forecasts. With prescribed sea surface temperature, NeuralGCM can accurately track climate metrics for multiple decades, and climate forecasts with 140-kilometre resolution show emergent phenomena such as realistic frequency and trajectories of tropical cyclones. For both weather and climate, our approach offers orders of magnitude computational savings over conventional GCMs, although our model does not extrapolate to substantially different future climates. Our results show that end-to-end deep learning is compatible with tasks performed by conventional GCMs and can enhance the large-scale physical simulations that are essential for understanding and predicting the Earth system. View details
    Locality-Aware Graph Rewiring in GNNs
    Federico Barbero
    Ameya Velingker
    Amin Saberi
    Michael Bronstein
    Francesco Di Giovanni
    ICLR 2024
    Preview abstract Graph Neural Networks (GNNs) are popular models for machine learning on graphs that typically follow the message-passing paradigm, whereby the feature of a node is updated recursively upon aggregating information over its neighbors. While exchanging messages over the input graph endows GNNs with a strong inductive bias, it can also make GNNs susceptible to \emph{over-squashing}, thereby preventing them from capturing long-range interactions in the given graph. To rectify this issue, {\em graph rewiring} techniques have been proposed as a means of improving information flow by altering the graph connectivity. In this work, we identify three desiderata for graph-rewiring: (i) reduce over-squashing, (ii) respect the locality of the graph, and (iii) preserve the sparsity of the graph. We highlight fundamental trade-offs that occur between {\em spatial} and {\em spectral} rewiring techniques; while the former often satisfy (i) and (ii) but not (iii), the latter generally satisfy (i) and (iii) at the expense of (ii). We propose a novel rewiring framework that satisfies all of (i)--(iii) through a locality-aware sequence of rewiring operations. We then discuss a specific instance of such rewiring framework and validate its effectiveness on several real-world benchmarks, showing that it either matches or significantly outperforms existing rewiring approaches. View details
    Help and The Social Construction of Access: A Case-Study from India
    Vaishnav Kameswaran
    Jerry Young Robinson
    Nithya Sambasivan
    Gaurav Aggarwal
    Proceedings of ASSETS 2024, ACM (2024)
    Preview abstract A goal of accessible technology (AT) design is often to increase independence, i.e., to enable people with disabilities to accomplish tasks on their own without help. Recent work uses "interdependence" to challenge this view, a framing that recognizes mutual dependencies as critical to addressing the access needs of people with disabilities. However, empirical evidence examining interdependence is limited to the Global North; we address this gap, using interdependence as an analytical frame to understand how people with visual impairments (PVI) in India navigate indoor environments. Using interviews with PVI and their companions and a video-diary study we find that help is a central way of working for PVI to circumvent issues of social and structural inaccess and necessitates work. We uncover three kinds of interdependencies 1) self-initiated, 2) serendipitous, and 3) obligatory and discuss the implications these interdependencies have for AT design in the Global South. View details
    First Passage Percolation with Queried Hints
    Kritkorn Karntikoon
    Aaron Schild
    Yiheng Shen
    Ali Sinop
    AISTATS (2024)
    Preview abstract Optimization problems are ubiquitous throughout the modern world. In many of these applications, the input is inherently noisy and it is expensive to probe all of the noise in the input before solving the relevant optimization problem. In this work, we study how much of that noise needs to be queried in order to obtain an approximately optimal solution to the relevant problem. We focus on the shortest path problem in graphs, where one may think of the noise as coming from real-time traffic. We consider the following model: start with a weighted base graph $G$ and multiply each edge weight by an independently chosen, uniformly random number in $[1,2]$ to obtain a random graph $G'$. This model is called \emph{first passage percolation}. Mathematicians have studied this model extensively when $G$ is a $d$-dimensional grid graph, but the behavior of shortest paths in this model is still poorly understood in general graphs. We make progress in this direction for a class of graphs that resembles real-world road networks. Specifically, we prove that if the geometric realization of $G$ has constant doubling dimension, then for a given $s-t$ pair, we only need to probe the weights on $((\log n) / \epsilon)^{O(1)}$ edges in $G'$ in order to obtain a $(1 + \epsilon)$-approximation to the $s-t$ distance in $G'$. We also demonstrate experimentally that this result is pessimistic -- one can even obtain a short path in $G'$ with a small number of probes to $G'$. View details