Publications
Publishing our work allows us to share ideas and work collaboratively to advance the field of computer science.
Publications
Publishing our work allows us to share ideas and work collaboratively to advance the field of computer science.
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1 - 15 of 15400 publications
StreamVC: Real-Time Low-Latency Voice Conversion
Jiuqiang Tang
Xing Li
ICASSP 2024 (2024)
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We present StreamVC, a streaming voice conversion solution that preserves the content and prosody of any source speech while matching the voice timbre from any target speech. Unlike previous approaches, StreamVC produces the resulting waveform at low latency from the input signal even on a mobile platform, making it applicable to real-time communication scenarios like calls and video conferencing, and addressing use cases such as voice anonymization in these scenarios. Our design leverages the architecture and training strategy of the SoundStream neural audio codec for lightweight high-quality speech synthesis. We demonstrate the feasibility of learning soft speech units causally, as well as the effectiveness of supplying whitened fundamental frequency information to improve pitch stability without leaking the source timbre information.
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Electric vehicle (EV) adoption in long-distance logistics faces challenges like range anxiety and uneven distribution of charging stations. Two pivotal questions emerge: How can EVs be efficiently routed in a charging network considering range limits, charging speeds and prices And, can the existing charging infrastructure sustain the increasing demand for EVs in long-distance logistics? This paper addresses these questions by introducing a novel theoretical and computational framework to study the EV network flow problems. We present an EV network flow model that incorporates range restrictions and nonlinear charging rates, and identify conditions under which polynomial-time solutions can be obtained for optimal single EV routing, maximum flow, and minimum cost flow problems. We develop efficient computational methods for computing the optimal routing and flow vector using a novel graph augmentation technique. Our findings provide insights for optimizing EV routing in logistics, ensuring an efficient and sustainable future.
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Generative AI in Creative Practice: ML-Artist Folk Theories of T2I Use, Harm, and Harm-Reduction
Shalaleh Rismani
Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '24), Association for Computing Machinery (2024), pp. 1-17 (to appear)
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Understanding how communities experience algorithms is necessary to mitigate potential harmful impacts. This paper presents folk theories of text-to-image (T2I) models to enrich understanding of how artist communities experience creative machine learning (ML) systems. This research draws on data collected from a workshop with 15 artists from 10 countries who incorporate T2I models in their creative practice. Through reflexive thematic analysis of workshop data, we highlight theorization of T2I use, harm, and harm-reduction. Folk theories of use envision T2I models as an artistic medium, a mundane tool, and locate true creativity as rising above model affordances. Theories of harm articulate T2I models as harmed by engineering efforts to eliminate glitches and product policy efforts to limit functionality. Theories of harm-reduction orient towards protecting T2I models for creative practice through transparency and distributed governance. We examine how these theories relate, and conclude by discussing how folk theorization informs responsible AI efforts.
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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, vol. N/A (2024), N/A
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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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Predictive uncertainty-a model's self awareness regarding its accuracy on an input-is key for both building robust models via training interventions and for test-time applications such as selective classification. We propose a novel instance-conditioned reweighting approach that captures predictive uncertainty using an auxiliary network and unifies these train- and test-time applications. The auxiliary network is trained using a meta-objective in a bilevel optimization framework. A key contribution of our proposal is the meta-objective of minimizing the dropout variance, an approximation of Bayesian Predictive uncertainty. We show in controlled experiments that we effectively capture the diverse specific notions of uncertainty through this meta-objective, while previous approaches only capture certain aspects. These results translate to significant gains in real-world settings-selective classification, label noise, domain adaptation, calibration-and across datasets-Imagenet, Cifar100, diabetic retinopathy, Camelyon, WILDs, Imagenet-C,-A,-R, Clothing1M, etc. For Diabetic Retinopathy, we see upto 3.4%/3.3% accuracy and AUC gains over SOTA in selective classification. We also improve upon large-scale pretrained models such as PLEX.
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Large Scale Self-Supervised Pretraining for Active Speaker Detection
Alice Chuang
Keith Johnson
Tony (Tuấn) Nguyễn
Wei Xia
Yunfan Ye
ICASSP 2024 (2024) (to appear)
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In this work we investigate the impact of a large-scale self-supervised pretraining strategy for active speaker detection (ASD) on an unlabeled dataset consisting of over 125k hours of YouTube videos. When compared to a baseline trained from scratch on much smaller in-domain labeled datasets we show that with pretraining we not only have a more stable supervised training due to better audio-visual features used for initialization, but also improve the ASD mean average precision by 23\% on a challenging dataset collected with Google Nest Hub Max devices capturing real user interactions.
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Conformal Language Modeling
Victor Quach
Adam Fisch
Adam Yala
Jae Ho Sohn
Tommi Jaakkola
Regina Barzilay
ICLR (2024) (to appear)
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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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Fairness under Covariate Shift: Improving Fairness-Accuracy tradeoff with few Unlabeled Test Samples
Jatin Chauhan
Jay Nandy
The 38th Annual AAAI Conference on Artificial Intelligence (2024)
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Covariate shift in the test data is a common practical phenomena that can significantly downgrade both the accuracy and the fairness performance of the model. Ensuring fairness across different sensitive groups under covariate shift is of paramount importance due to societal implications like criminal justice. We operate in the unsupervised regime where only a small set of unlabeled test samples along with a labeled training set is available. Towards improving fairness under this highly challenging yet realistic scenario, we make three contributions. First is a novel composite weighted entropy based objective for prediction accuracy which is optimized along with a representation matching loss for fairness. We experimentally verify that optimizing with our loss formulation outperforms a number of state-of-the-art baselines in the pareto sense with respect to the fairness-accuracy tradeoff on several standard datasets. Our second contribution is a new setting we term Asymmetric Covariate Shift that, to the best of our knowledge, has not been studied before. Asymmetric covariate shift occurs when distribution of covariates of one group shifts significantly compared to the other groups and this happens when a dominant group is over-represented. While this setting is extremely challenging for current baselines, We show that our proposed method significantly outperforms them. Our third contribution is theoretical, where we show that our weighted entropy term along with prediction loss on the training set approximates test loss under covariate shift. Empirically and through formal sample complexity bounds, we show that this approximation to the unseen test loss does not depend on importance sampling variance which affects many other baselines.
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Chain-of-Table: Evolves Tables in the LLM Reasoning Chain for Table Understanding
Zilong Wang
Hao Zhang
Chun-Liang Li
Jingbo Shang
ICLR (2024)
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Table-based reasoning with large language models (LLMs) is a promising direction to tackle many table understanding tasks, such as table-based question answering and fact verification. Compared with generic reasoning, table-based reasoning requires the extraction of underlying semantics from both free-form questions and semi-structured tabular data. Chain-of-Thought and its similar approaches incorporate the reasoning chain in the form of textual context, but it is still an open question how to effectively leverage tabular data in the reasoning chain. We propose the Chain-of-Table framework, where tabular data is explicitly used in the reasoning chain as a proxy for intermediate thoughts. Specifically, we guide LLMs using in-context learning to iteratively generate operations and update the table to represent a tabular reasoning chain. LLMs can therefore dynamically plan the next operation based on the results of the previous ones. This continuous evolution of the table forms a chain, showing the reasoning process for a given tabular problem. The chain carries structured information of the intermediate results, enabling more accurate and reliable predictions. Chain-of-Table achieves new state-of-the-art performance on WikiTQ, FeTaQA, and TabFact benchmarks across multiple LLM choices.
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Rust is a general-purpose programming language designed for performance and safety. Unrecoverable errors (e.g., Divide by Zero) in Rust programs are critical, as they signal bad program states and terminate programs abruptly. Previous work has contributed to utilizing KLEE, a dynamic symbolic test engine, to verify the program would not panic. However, it is difficult for engineers who lack domain expertise to write test code correctly. Besides, the effectiveness of KLEE in finding panics in production Rust code has not been evaluated. We created an approach, called PanicCheck, to hide the complexity of verifying Rust programs with KLEE. Using PanicCheck, engineers only need to annotate the function-to-verify with #[panic_check]. The annotation guides PanicCheck to generate test code, compile the function together with tests, and execute KLEE for verification. After applying PanicCheck to 21 open-source and 2 closed-source projects, we found 61 test inputs that triggered panics; 60 of the 61 panics have been addressed by developers so far. Our research shows promising verification results by KLEE, while revealing technical challenges in using KLEE. Our experience will shed light on future practice and research in program verification.
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We propose a neural network model that can separate target speech sources from interfering sources at different angular regions using two microphones. The model is trained with simulated room impulse responses (RIRs) using omni-directional microphones without needing to collect real RIRs. By relying on specific angular regions and multiple room simulations, the model utilizes consistent time difference of arrival (TDOA) cues, or what we call delay contrast, to separate target and interference sources while remaining robust in various reverberation environments. We demonstrate the model is not only generalizable to a commercially available device with a slightly different microphone geometry, but also outperforms our previous work which uses one additional microphone on the same device. The model runs in real-time on-device and is suitable for low-latency streaming applications such as telephony and video conferencing.
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Dynamic Inference of Likely Symbolic Tensor Shapes in Python Machine Learning Programs
Koushik Sen
International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) (2024) (to appear)
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In machine learning programs, it is often tedious to annotate the dimensions of shapes of various tensors that get created during execution. We present a dynamic likely tensor shape inference analysis that annotates the dimensions of shapes of tensor expressions with symbolic dimension values. Such annotations can be used for understanding the machine learning code written in popular frameworks, such as TensorFlow, PyTorch, JAX, and for finding bugs related to tensor shape mismatch.
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Algorithms for the computation of alternative routes in road networks power many geographic navigation systems. A good set of alternative routes offers meaningful options to the user of the system and can support applications such as routing that is robust to failures (e.g., road closures, extreme traffic congestion, etc.) and routing with diverse preferences and objective functions. Algorithmic techniques for alternative route computation include the penalty method, via-node type algorithms (which deploy bidirectional search and finding plateaus), and, more recently, electrical-circuit based algorithms. In this work we focus on the practically important family of via-node type algorithms and we aim to produce high quality alternative routes for road netowrks. We study alternative route computation in the presence of a fast routing infrastructure that relies on hierarchical routing (namely, CRP). We propose new approaches that rely on deep learning methods. Our training methodology utilizes the hierarchical partition of the graph and builds models to predict which boundary road segments in the partition should be crossed by the alternative routes. We describe our methods in detail and evaluate them against the previously studied architectures, as well as against a stronger baseline that we define in this work, showing improvements in quality in the road networks of Seattle, Paris, and Bangalore.
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Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation
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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