5 citations · 7 across the 2 of their papers we have counts for
5 papers · 1 filter
Low-Rank Correction for Quantized LLMs
Meyer Scetbon, James Hensman
We consider the problem of model compression for Large Language Models (LLMs) at post-training time, where the task is to compress a well-trained model using only a small set of ca…
Robust Linear Regression: Phase-Transitions and Precise Tradeoffs for General Norms
Elvis Dohmatob, Meyer Scetbon
In this paper, we investigate the impact of test-time adversarial attacks on linear regression models and determine the optimal level of robustness that any model can reach while m…
Robust Linear Regression: Gradient-descent, Early-stopping, and Beyond
Meyer Scetbon, Elvis Dohmatob
In this work we study the robustness to adversarial attacks, of early-stopping strategies on gradient-descent (GD) methods for linear regression. More precisely, we show that early…
Triangular Flows for Generative Modeling: Statistical Consistency, Smoothness Classes, and Fast Rates
Nicholas J. Irons, Meyer Scetbon, Soumik Pal +1
Triangular flows, also known as Knöthe-Rosenblatt measure couplings, comprise an important building block of normalizing flow models for generative modeling and density estimation,…
Comparing distributions: geometry improves kernel two-sample testing
M. Scetbon, G. Varoquaux
Are two sets of observations drawn from the same distribution? This problem is a two-sample test. Kernel methods lead to many appealing properties. Indeed state-of-the-art approach…