5 citations · 14 across the 11 of their papers we have counts for
6 papers · 1 filter
Confidence Sets under Generalized Self-Concordance
Lang Liu, Zaid Harchaoui
This paper revisits a fundamental problem in statistical inference from a non-asymptotic theoretical viewpoint $\unicode{x2013}$ the construction of confidence sets. We establish 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…
MAUVE Scores for Generative Models: Theory and Practice
Krishna Pillutla, Lang Liu, John Thickstun +6
Generative artificial intelligence has made significant strides, producing text indistinguishable from human prose and remarkably photorealistic images. Automatically measuring how…
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…