2 citations · 2 across the 2 of their papers we have counts for
6 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…
Low-Rank Sinkhorn Factorization
Meyer Scetbon, Marco Cuturi, Gabriel Peyré
Several recent applications of optimal transport (OT) theory to machine learning have relied on regularization, notably entropy and the Sinkhorn algorithm. Because matrix-vector pr…
Equitable and Optimal Transport with Multiple Agents
Meyer Scetbon, Laurent Meunier, Jamal Atif +1
We introduce an extension of the Optimal Transport problem when multiple costs are involved. Considering each cost as an agent, we aim to share equally between agents the work of t…
Linear Time Sinkhorn Divergences using Positive Features
Meyer Scetbon, Marco Cuturi
Although Sinkhorn divergences are now routinely used in data sciences to compare probability distributions, the computational effort required to compute them remains expensive, gro…
Harmonic Decompositions of Convolutional Networks
Meyer Scetbon, Zaid Harchaoui
We present a description of the function space and the smoothness class associated with a convolutional network using the machinery of reproducing kernel Hilbert spaces. We show th…
A Spectral Analysis of Dot-product Kernels
Meyer Scetbon, Zaid Harchaoui
We present eigenvalue decay estimates of integral operators associated with compositional dot-product kernels. The estimates improve on previous ones established for power series k…