5 citations · 14 across the 11 of their papers we have counts for
8 papers · 1 filter
The Benefits of Balance: From Information Projections to Variance Reduction
Lang Liu, Ronak Mehta, Soumik Pal +1
Data balancing across multiple modalities and sources appears in various forms in foundation models in machine learning and AI, e.g. in CLIP and DINO. We show that data balancing a…
Stochastic Optimization for Spectral Risk Measures
Ronak Mehta, Vincent Roulet, Krishna Pillutla +2
Spectral risk objectives - also called -risks - allow for learning systems to interpolate between optimizing average-case performance (as in empirical risk minimization) and wor…
Statistical and Computational Guarantees for Influence Diagnostics
Jillian Fisher, Lang Liu, Krishna Pillutla +2
Influence diagnostics such as influence functions and approximate maximum influence perturbations are popular in machine learning and in AI domain applications. Influence diagnosti…
Orthogonal Statistical Learning with Self-Concordant Loss
Lang Liu, Carlos Cinelli, Zaid Harchaoui
Orthogonal statistical learning and double machine learning have emerged as general frameworks for two-stage statistical prediction in the presence of a nuisance component. We esta…
Distribution Embedding Networks for Generalization from a Diverse Set of Classification Tasks
Lang Liu, Mahdi Milani Fard, Sen Zhao
We propose Distribution Embedding Networks (DEN) for classification with small data. In the same spirit of meta-learning, DEN learns from a diverse set of training tasks with the g…
Entropy Regularized Optimal Transport Independence Criterion
Lang Liu, Soumik Pal, Zaid Harchaoui
We introduce an independence criterion based on entropy regularized optimal transport. Our criterion can be used to test for independence between two samples. We establish non-asym…
Score-Based Change Detection for Gradient-Based Learning Machines
Lang Liu, Joseph Salmon, Zaid Harchaoui
The widespread use of machine learning algorithms calls for automatic change detection algorithms to monitor their behavior over time. As a machine learning algorithm learns from a…
Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals
Lang Liu, Krishna Pillutla, Sean Welleck +3
The spectacular success of deep generative models calls for quantitative tools to measure their statistical performance. Divergence frontiers have recently been proposed as an eval…