5 citations · 6 across the 5 of their papers we have counts for
5 papers
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…
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…
-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations
Adam Dziedzic, Stephan Rabanser, Mohammad Yaghini +3
The lack of well-calibrated confidence estimates makes neural networks inadequate in safety-critical domains such as autonomous driving or healthcare. In these settings, having the…
Scalable Approximations for Generalized Linear Problems
Murat A. Erdogdu, Mohsen Bayati, Lee H. Dicker
In stochastic optimization, the population risk is generally approximated by the empirical risk. However, in the large-scale setting, minimization of the empirical risk may be comp…