27 citations · 115 across the 17 of their papers we have counts for
17 papers
Distributionally Robust Optimization with Bias and Variance Reduction
Ronak Mehta, Vincent Roulet, Krishna Pillutla +1
We consider the distributionally robust optimization (DRO) problem with spectral risk-based uncertainty set and -divergence penalty. This formulation includes common risk-sensit…
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning
Zachary Charles, Nicole Mitchell, Krishna Pillutla +2
We introduce Dataset Grouper, a library to create large-scale group-structured (e.g., federated) datasets, enabling federated learning simulation at the scale of foundation models.…
Unleashing the Power of Randomization in Auditing Differentially Private ML
Krishna Pillutla, Galen Andrew, Peter Kairouz +3
We present a rigorous methodology for auditing differentially private machine learning algorithms by adding multiple carefully designed examples called canaries. We take a first pr…
Modified Gauss-Newton Algorithms under Noise
Krishna Pillutla, Vincent Roulet, Sham Kakade +1
Gauss-Newton methods and their stochastic version have been widely used in machine learning and signal processing. Their nonsmooth counterparts, modified Gauss-Newton or prox-linea…
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…
Federated Learning with Superquantile Aggregation for Heterogeneous Data
Krishna Pillutla, Yassine Laguel, Jérôme Malick +1
We present a federated learning framework that is designed to robustly deliver good predictive performance across individual clients with heterogeneous data. The proposed approach…
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…
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes
Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan +6
Learning binary representations of instances and classes is a classical problem with several high potential applications. In modern settings, the compression of high-dimensional ne…
MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers
Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers +4
As major progress is made in open-ended text generation, measuring how close machine-generated text is to human language remains a critical open problem. We introduce MAUVE, a comp…
Device Heterogeneity in Federated Learning: A Superquantile Approach
Yassine Laguel, Krishna Pillutla, Jérôme Malick +1
We propose a federated learning framework to handle heterogeneous client devices which do not conform to the population data distribution. The approach hinges upon a parameterized…
A Smoother Way to Train Structured Prediction Models
Krishna Pillutla, Vincent Roulet, Sham M. Kakade +1
We present a framework to train a structured prediction model by performing smoothing on the inference algorithm it builds upon. Smoothing overcomes the non-smoothness inherent to…
A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares)
Prateek Jain, Sham M. Kakade, Rahul Kidambi +3
This work provides a simplified proof of the statistical minimax optimality of (iterate averaged) stochastic gradient descent (SGD), for the special case of least squares. This res…
Data Driven Resource Allocation for Distributed Learning
Travis Dick, Mu Li, Venkata Krishna Pillutla +3
In distributed machine learning, data is dispatched to multiple machines for processing. Motivated by the fact that similar data points often belong to the same or similar classes,…