336 citations · 1.1k across the 77 of their papers we have counts for
23 papers · 1 filter
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…
Performance-Robustness Tradeoffs in Adversarially Robust Linear-Quadratic Control
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…
Do Deep Networks Transfer Invariances Across Classes?
Allan Zhou, Fahim Tajwar, Alexander Robey +4
To generalize well, classifiers must learn to be invariant to nuisance transformations that do not alter an input's class. Many problems have "class-agnostic" nuisance transformati…
Binary Classification Under Attacks for General Noise Distribution
Payam Delgosha, Hamed Hassani, Ramtin Pedarsani
Adversarial examples have recently drawn considerable attention in the field of machine learning due to the fact that small perturbations in the data can result in major performanc…
Linear Stochastic Bandits over a Bit-Constrained Channel
Aritra Mitra, Hamed Hassani, George J. Pappas
One of the primary challenges in large-scale distributed learning stems from stringent communication constraints. While several recent works address this challenge for static optim…
T-Cal: An optimal test for the calibration of predictive models
Donghwan Lee, Xinmeng Huang, Hamed Hassani +1
The prediction accuracy of machine learning methods is steadily increasing, but the calibration of their uncertainty predictions poses a significant challenge. Numerous works focus…
Provably Private Distributed Averaging Consensus: An Information-Theoretic Approach
Mohammad Fereydounian, Aryan Mokhtari, Ramtin Pedarsani +1
In this work, we focus on solving a decentralized consensus problem in a private manner. Specifically, we consider a setting in which a group of nodes, connected through a network,…
Low-Complexity Decoding of a Class of Reed-Muller Subcodes for Low-Capacity Channels
Mohammad Vahid Jamali, Mohammad Fereydounian, Hessam Mahdavifar +1
We present a low-complexity and low-latency decoding algorithm for a class of Reed-Muller (RM) subcodes that are defined based on the product of smaller RM codes. More specifically…
What Functions Can Graph Neural Networks Generate?
Mohammad Fereydounian, Hamed Hassani, Amin Karbasi
In this paper, we fully answer the above question through a key algebraic condition on graph functions, called \textit{permutation compatibility}, that relates permutations of weig…
Probabilistically Robust Learning: Balancing Average- and Worst-case Performance
Alexander Robey, Luiz F. O. Chamon, George J. Pappas +1
Many of the successes of machine learning are based on minimizing an averaged loss function. However, it is well-known that this paradigm suffers from robustness issues that hinder…
Efficient and Robust Classification for Sparse Attacks
Mark Beliaev, Payam Delgosha, Hamed Hassani +1
In the past two decades we have seen the popularity of neural networks increase in conjunction with their classification accuracy. Parallel to this, we have also witnessed how frag…
The curse of overparametrization in adversarial training: Precise analysis of robust generalization for random features regression
Hamed Hassani, Adel Javanmard
Successful deep learning models often involve training neural network architectures that contain more parameters than the number of training samples. Such overparametrized models h…