483 citations · 760 across the 48 of their papers we have counts for
7 papers · 1 filter
Optimal Excess Risk Bounds for Empirical Risk Minimization on -Norm Linear Regression
Ayoub El Hanchi, Murat A. Erdogdu
We study the performance of empirical risk minimization on the -norm linear regression problem for . We show that, in the realizable case, under no moment ass…
Beyond Labeling Oracles: What does it mean to steal ML models?
Avital Shafran, Ilia Shumailov, Murat A. Erdogdu +1
Model extraction attacks are designed to steal trained models with only query access, as is often provided through APIs that ML-as-a-Service providers offer. Machine Learning (ML)…
Gradient-Based Feature Learning under Structured Data
Alireza Mousavi-Hosseini, Denny Wu, Taiji Suzuki +1
Recent works have demonstrated that the sample complexity of gradient-based learning of single index models, i.e. functions that depend on a 1-dimensional projection of the input d…
Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning
Tyler Kastner, Murat A. Erdogdu, Amir-massoud Farahmand
We consider the problem of learning models for risk-sensitive reinforcement learning. We theoretically demonstrate that proper value equivalence, a method of learning models which…
Towards a Complete Analysis of Langevin Monte Carlo: Beyond Poincaré Inequality
Alireza Mousavi-Hosseini, Tyler Farghly, Ye He +2
Langevin diffusions are rapidly convergent under appropriate functional inequality assumptions. Hence, it is natural to expect that with additional smoothness conditions to handle…
Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling
Ye He, Tyler Farghly, Krishnakumar Balasubramanian +1
We analyze the complexity of sampling from a class of heavy-tailed distributions by discretizing a natural class of Itô diffusions associated with weighted Poincaré inequalities. B…
Improved Discretization Analysis for Underdamped Langevin Monte Carlo
Matthew Zhang, Sinho Chewi, Mufan Bill Li +2
Underdamped Langevin Monte Carlo (ULMC) is an algorithm used to sample from unnormalized densities by leveraging the momentum of a particle moving in a potential well. We provide a…