336 citations · 1.1k across the 77 of their papers we have counts for
80 papers
Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning
Zebang Shen, Jiayuan Ye, Anmin Kang +2
Repeated parameter sharing in federated learning causes significant information leakage about private data, thus defeating its main purpose: data privacy. Mitigating the risk of th…
Min-Max Optimization under Delays
Arman Adibi, Aritra Mitra, Hamed Hassani
Delays and asynchrony are inevitable in large-scale machine-learning problems where communication plays a key role. As such, several works have extensively analyzed stochastic opti…
Text + Sketch: Image Compression at Ultra Low Rates
Eric Lei, Yiğit Berkay Uslu, Hamed Hassani +1
Recent advances in text-to-image generative models provide the ability to generate high-quality images from short text descriptions. These foundation models, when pre-trained on bi…
Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks
Liam Collins, Hamed Hassani, Mahdi Soltanolkotabi +2
An increasingly popular machine learning paradigm is to pretrain a neural network (NN) on many tasks offline, then adapt it to downstream tasks, often by re-training only the last…
On a Relation Between the Rate-Distortion Function and Optimal Transport
Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti
We discuss a relationship between rate-distortion and optimal transport (OT) theory, even though they seem to be unrelated at first glance. In particular, we show that a function d…
Adversarial Training Should Be Cast as a Non-Zero-Sum Game
Alexander Robey, Fabian Latorre, George J. Pappas +2
One prominent approach toward resolving the adversarial vulnerability of deep neural networks is the two-player zero-sum paradigm of adversarial training, in which predictors are t…
Optimal Multitask Linear Regression and Contextual Bandits under Sparse Heterogeneity
Xinmeng Huang, Kan Xu, Donghwan Lee +3
Large and complex datasets are often collected from several, possibly heterogeneous sources. Multitask learning methods improve efficiency by leveraging commonalities across datase…
Federated Neural Compression Under Heterogeneous Data
Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti
We discuss a federated learned compression problem, where the goal is to learn a compressor from real-world data which is scattered across clients and may be statistically heteroge…
Performance-Robustness Tradeoffs in Adversarially Robust Control and Estimation
Bruce D. Lee, Thomas T. C. K. Zhang, Hamed Hassani +1
While methods can introduce robustness against worst-case perturbations, their nominal performance under conventional stochastic disturbances is often drastica…
Demystifying Disagreement-on-the-Line in High Dimensions
Donghwan Lee, Behrad Moniri, Xinmeng Huang +2
Evaluating the performance of machine learning models under distribution shift is challenging, especially when we only have unlabeled data from the shifted (target) domain, along w…
Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneity
Han Wang, Aritra Mitra, Hamed Hassani +2
We initiate the study of federated reinforcement learning under environmental heterogeneity by considering a policy evaluation problem. Our setup involves agents interacting wi…
Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning
Aritra Mitra, George J. Pappas, Hamed Hassani
In large-scale distributed machine learning, recent works have studied the effects of compressing gradients in stochastic optimization to alleviate the communication bottleneck. Th…
Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods
Alexander Shevchenko, Kevin Kögler, Hamed Hassani +1
Autoencoders are a popular model in many branches of machine learning and lossy data compression. However, their fundamental limits, the performance of gradient methods and the fea…
Conditional Gradient Methods
Gábor Braun, Alejandro Carderera, Cyrille W. Combettes +4
The purpose of this survey is to serve both as a gentle introduction and a coherent overview of state-of-the-art Frank--Wolfe algorithms, also called conditional gradient algorithm…
Probable Domain Generalization via Quantile Risk Minimization
Cian Eastwood, Alexander Robey, Shashank Singh +4
Domain generalization (DG) seeks predictors which perform well on unseen test distributions by leveraging data drawn from multiple related training distributions or domains. To ach…
Self-Consistency of the Fokker-Planck Equation
Zebang Shen, Zhenfu Wang, Satyen Kale +3
The Fokker-Planck equation (FPE) is the partial differential equation that governs the density evolution of the Itô process and is of great importance to the literature of statisti…
Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds
Aritra Mitra, Arman Adibi, George J. Pappas +1
We consider a linear stochastic bandit problem involving agents that can collaborate via a central server to minimize regret. A fraction of these agents are adversarial and…
Straggler-Resilient Personalized Federated Learning
Isidoros Tziotis, Zebang Shen, Ramtin Pedarsani +2
Federated Learning is an emerging learning paradigm that allows training models from samples distributed across a large network of clients while respecting privacy and communicatio…
Toward Certified Robustness Against Real-World Distribution Shifts
Haoze Wu, Teruhiro Tagomori, Alexander Robey +6
We consider the problem of certifying the robustness of deep neural networks against real-world distribution shifts. To do so, we bridge the gap between hand-crafted specifications…
Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints
Xinmeng Huang, Donghwan Lee, Edgar Dobriban +1
In modern machine learning, users often have to collaborate to learn the distribution of the data. Communication can be a significant bottleneck. Prior work has studied homogeneous…
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning
Liam Collins, Hamed Hassani, Aryan Mokhtari +1
The Federated Averaging (FedAvg) algorithm, which consists of alternating between a few local stochastic gradient updates at client nodes, followed by a model averaging update at t…
Distributed Statistical Min-Max Learning in the Presence of Byzantine Agents
Arman Adibi, Aritra Mitra, George J. Pappas +1
Recent years have witnessed a growing interest in the topic of min-max optimization, owing to its relevance in the context of generative adversarial networks (GANs), robust control…
Neural Estimation of the Rate-Distortion Function With Applications to Operational Source Coding
Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti
A fundamental question in designing lossy data compression schemes is how well one can do in comparison with the rate-distortion function, which describes the known theoretical lim…
Chordal Sparsity for Lipschitz Constant Estimation of Deep Neural Networks
Anton Xue, Lars Lindemann, Alexander Robey +3
Lipschitz constants of neural networks allow for guarantees of robustness in image classification, safety in controller design, and generalizability beyond the training data. As ca…