11 citations · 21 across the 10 of their papers we have counts for
7 papers · 1 filter
Convergence of Uncertainty Sampling for Active Learning
Anant Raj, Francis Bach
Uncertainty sampling in active learning is heavily used in practice to reduce the annotation cost. However, there has been no wide consensus on the function to be used for uncertai…
Non-stationary Online Regression
Anant Raj, Pierre Gaillard, Christophe Saad
Online forecasting under a changing environment has been a problem of increasing importance in many real-world applications. In this paper, we consider the meta-algorithm presented…
Model-specific Data Subsampling with Influence Functions
Anant Raj, Cameron Musco, Lester Mackey +1
Model selection requires repeatedly evaluating models on a given dataset and measuring their relative performances. In modern applications of machine learning, the models being con…
Sobolev Descent
Youssef Mroueh, Tom Sercu, Anant Raj
We study a simplification of GAN training: the problem of transporting particles from a source to a target distribution. Starting from the Sobolev GAN critic, part of the gradient…
Sobolev GAN
Youssef Mroueh, Chun-Liang Li, Tom Sercu +2
We propose a new Integral Probability Metric (IPM) between distributions: the Sobolev IPM. The Sobolev IPM compares the mean discrepancy of two distributions for functions (critic)…
Safe Adaptive Importance Sampling
Sebastian U. Stich, Anant Raj, Martin Jaggi
Importance sampling has become an indispensable strategy to speed up optimization algorithms for large-scale applications. Improved adaptive variants - using importance values defi…