Publications
Working Papers
MAUVE Scores for Generative Models: Theory and Practice.
Krishna Pillutla*, Lang Liu*, John Thickstun, Sean Welleck, Swabha Swayamdipta, Rowan Zellers, Sewoong Oh, Yejin Choi, Zaid Harchaoui.
Manuscript (2023).
PDF Pip-package Code
Modified Gauss-Newton Algorithms under Noise.
Krishna Pillutla, Vincent Roulet, Sham Kakade, Zaid Harchaoui.
Submitted (2022).
PDF
Conference and Journal Publications
Statistical and Computational Guarantees for Influence Diagnostics.
Jillian Fisher, Lang Liu, Krishna Pillutla, Yejin Choi, Zaid Harchaoui.
AISTATS (2023).
PDF Code
Stochastic Optimization for Spectral Risk Measures.
Ronak Mehta, Vincent Roulet, Krishna Pillutla, Lang Liu, Zaid Harchaoui.
AISTATS (2023).
PDF Code
Federated Learning with Superquantile Aggregation for Heterogeneous Data.
Krishna Pillutla*, Yassine Laguel*, Jérôme Malick, Zaid Harchaoui.
Machine Learning Journal (To Appear, 2022).
FL-NeurIPS ‘22, DistShift-NeurIPS ‘22 Spotlight.
PDF Code Slides Poster
From Enormous Structured Models to On-device Federated Learning: Robustness, Heterogeneity and Optimization.
Krishna Pillutla
PhD Dissertation (2022).
PDF Slides
Federated Learning with Partial Model Personalization.
Krishna Pillutla, Kshitiz Malik, Abdelrahman Mohamed, Michael Rabbat, Maziar Sanjabi, Lin Xiao.
ICML 2022.
PDF Code Slides (ICML Spotlight) Poster
Robust Aggregation for Federated Learning.
Krishna Pillutla, Sham Kakade, Zaid Harchaoui.
IEEE Transactions on Signal Processing (2022).
FL-ICML ‘20 Long Presentation.
PDF Code (TensorFlow) Code (PyTorch) Talk video
MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers.
Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, Zaid Harchaoui.
NeurIPS 2021. Outstanding Paper Award (Top 6 out of 9000 submissions).
PDF Pip-package Code Poster Press
Divergence Frontiers for Generative Models: Sample Complexity, Quantization Level, and Frontier Integral.
Lang Liu, Krishna Pillutla, Sean Welleck, Sewoong Oh, Yejin Choi, Zaid Harchaoui.
NeurIPS 2021.
PDF Code
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes.
Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan, Raghav Somani, Jae Sung Park, Krishna Pillutla, Prateek Jain, Sham Kakade, Ali Farhadi.
NeurIPS 2021.
PDF
Superquantiles at Work : Machine Learning Applications and Efficient (Sub)gradient Computation.
Yassine Laguel, Krishna Pillutla, Jérôme Malick, Zaid Harchaoui.
Set-Valued and Variational Analysis (2021).
PDF Publisher’s Page
A Superquantile Approach to Federated Learning with Heterogeneous Devices.
Yassine Laguel*, Krishna Pillutla*, Jérôme Malick, Zaid Harchaoui.
IEEE CISS 2021.
PDF Code
A Smoother Way to Train Structured Prediction Models.
Krishna Pillutla, Vincent Roulet, Sham Kakade, Zaid Harchaoui.
NeurIPS 2018.
PDF-long PDF-short Code Documentation
Poster Blog post Video summary
A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares).
Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Venkata Krishna Pillutla, Aaron Sidford.
FSTTCS 2017.
PDF
Data Driven Resource Allocation for Distributed Learning.
Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria-Florina Balcan, Alex Smola.
AISTATS 2017.
PDF-long PDF-short
On Skewed Multi-dimensional Distributions: the FusionRP Model, Algorithms, and Discoveries.
Venkata Krishna Pillutla*, Zhanpeng Fang*, Christos Faloutsos, Danai Koutra, Jie Tang.
SIAM International Conference on Data Mining (SDM) 2016.
PDF
Master’s Thesis: Data Driven Resource Allocation For Distributed Machine Learning.
Thesis Committee: Nina Balcan, Alex Smola, Christos Faloutsos
PDF Slides