Working Papers

Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy.
Krishnamurthy Dvijotham, H. Brendan McMahan, Krishna Pillutla, Thomas Steinke, Abhradeep Thakurta.
Submitted (2024).

User Inference Attacks on Large Language Models.
Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher A. Choquette-Choo, Zheng Xu.
Submitted (2023).

Conference and Journal Publications

Correlated Noise Provably Beats Independent Noise for Differentially Private Learning.
Christopher Choquette-Choo*, Krishnamurthy Dvijotham*, Krishna Pillutla*, Arun Ganesh, Thomas Steinke, Abhradeep Thakurta.
ICLR (2024).
Also presented at FL@FM-NeurIPS (2023)
PDF   Slides   Poster  

Distributionally Robust Optimization with Bias and Variance Reduction.
Ronak Mehta, Vincent Roulet, Krishna Pillutla, Zaid Harchaoui.
ICLR (2024) Spotlight.
Also presented at DP4ML-ICML (2023)
PDF   Code  

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.
JMLR (2023).
Also presented at DeepMath (2023)
PDF   Project Page   Pip-package   Code   Poster  

Unleashing the Power of Randomization in Auditing Differentially Private ML.
Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh.
NeurIPS (2023).
Also presented at FL@ICML (2023), TPDP (2023)
PDF   Poster   Code  

Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning.
Zachary Charles*, Nicole Mitchell*, Krishna Pillutla*, Michael Reneer, Zachary Garrett.
NeurIPS Datasets and Benchmarks (2023).
PDF   Software   Poster   Slides  

Modified Gauss-Newton Algorithms under Noise.
Krishna Pillutla, Vincent Roulet, Sham Kakade, Zaid Harchaoui.
IEEE SSP (2023), HOO-NeurIPS (2022).
PDF   Poster  

Statistical and Computational Guarantees for Influence Diagnostics.
Jillian Fisher, Lang Liu, Krishna Pillutla, Yejin Choi, Zaid Harchaoui.
AISTATS (2023).
ASA Student Paper Award 2023 Honorable Mention (Section on Statistical Learning & Data Science)
PDF   Code  

Stochastic Optimization for Spectral Risk Measures.
Ronak Mehta, Vincent Roulet, Krishna Pillutla, Lang Liu, Zaid Harchaoui.
AISTATS (2023).
ASA Student Paper Award 2023 Honorable Mention (Risk Analysis Section)
PDF   Code  

Federated Learning with Superquantile Aggregation for Heterogeneous Data.
Krishna Pillutla*, Yassine Laguel*, Jérôme Malick, Zaid Harchaoui.
Machine Learning Journal (2023).
FL-NeurIPS ‘22, DistShift-NeurIPS ‘22 Spotlight.
PDF   Publisher’s Page   Project Page  
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 2020 Long Oral Presentation, ICASSP 2023
PDF   Publisher’s Page   Code (TensorFlow)   Code (PyTorch)   Talk video   Poster

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   Poster  

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.

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.
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.

Data Driven Resource Allocation for Distributed Learning.
Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria-Florina Balcan, Alex Smola.
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.

Master’s Thesis: Data Driven Resource Allocation For Distributed Machine Learning.
Thesis Committee: Nina Balcan, Alex Smola, Christos Faloutsos
PDF Slides