activity
20222025
most citedShare Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning

6 citations · 23 across the 18 of their papers we have counts for

collaborators

12 papers

cs.LG20241 cited

Conformal Prediction with Learned Features

Shayan Kiyani, George Pappas, Hamed Hassani

In this paper, we focus on the problem of conformal prediction with conditional guarantees. Prior work has shown that it is impossible to construct nontrivial prediction sets with…

cs.LG20243 cited

Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling

Arman Adibi, Nicolo Dal Fabbro, Luca Schenato +5

Motivated by applications in large-scale and multi-agent reinforcement learning, we study the non-asymptotic performance of stochastic approximation (SA) schemes with delayed updat…

cs.CL20242 cited

Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing

Jiabao Ji, Bairu Hou, Alexander Robey +5

Aligned large language models (LLMs) are vulnerable to jailbreaking attacks, which bypass the safeguards of targeted LLMs and fool them into generating objectionable content. While…

cs.LG20241 cited

Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth

Kevin Kögler, Alexander Shevchenko, Hamed Hassani +1

Autoencoders are a prominent model in many empirical branches of machine learning and lossy data compression. However, basic theoretical questions remain unanswered even in a shall…

cs.LG2024

Generalization Properties of Adversarial Training for -Bounded Adversarial Attacks

Payam Delgosha, Hamed Hassani, Ramtin Pedarsani

We have widely observed that neural networks are vulnerable to small additive perturbations to the input causing misclassification. In this paper, we focus on the -bounded…

cs.LG20231 cited

Score-Based Methods for Discrete Optimization in Deep Learning

Eric Lei, Arman Adibi, Hamed Hassani

Discrete optimization problems often arise in deep learning tasks, despite the fact that neural networks typically operate on continuous data. One class of these problems involve o…