Publications/Manuscripts
Federated Learning with Partial Model Personalization.
Krishna Pillutla, Kshitiz Malik, Abdelrahman Mohamed, Michael Rabbat, Maziar Sanjabi, Lin Xiao.
ICML 2022.
PDF
Robust Aggregation for Federated Learning.
Krishna Pillutla, Sham Kakade, Zaid Harchaoui.
IEEE Transactions on Signal Processing (2022).
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
Federated Learning with Heterogeneous Devices: A Superquantile Optimization Approach.
Krishna Pillutla*, Yassine Laguel*, Jérôme Malick, Zaid Harchaoui.
Submitted.
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.
CISS 2021.
PDF PDF-arXiv(old) 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.
SDM 2016.
Master’s Thesis: Data Driven Resource Allocation For Distributed Machine Learning.
Thesis Committee: Nina Balcan, Alex Smola, Christos Faloutsos
PDF Slides