27 citations · 115 across the 17 of their papers we have counts for
5 papers · 1 filter
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…
Federated Learning with Partial Model Personalization
Krishna Pillutla, Kshitiz Malik, Abdelrahman Mohamed +3
We consider two federated learning algorithms for training partially personalized models, where the shared and personal parameters are updated either simultaneously or alternately…
Superquantiles at Work: Machine Learning Applications and Efficient Subgradient Computation
Yassine Laguel, Krishna Pillutla, Jérôme Malick +1
R. Tyrell Rockafellar and collaborators introduced, in a series of works, new regression modeling methods based on the notion of superquantile (or conditional value-at-risk). These…