Suresh, Ananda Theertha

60 publications

ICLR 2025 Block Verification Accelerates Speculative Decoding Ziteng Sun, Uri Mendlovic, Yaniv Leviathan, Asaf Aharoni, Jae Hun Ro, Ahmad Beirami, Ananda Theertha Suresh

UAI 2025 Concept Forgetting via Label Annealing Subhodip Panda, Ananda Theertha Suresh, Atri Guha, Prathosh Ap

AISTATS 2025 Efficient and Asymptotically Unbiased Constrained Decoding for Large Language Models Haotian Ye, Himanshu Jain, Chong You, Ananda Theertha Suresh, Haowei Lin, James Zou, Felix Yu

AISTATS 2025 General Staircase Mechanisms for Optimal Differential Privacy Alex Kulesza, Ananda Theertha Suresh, Yuyan Wang

NeurIPS 2025 Hierarchical Retrieval: The Geometry and a Pretrain-Finetune Recipe Chong You, Rajesh Jayaram, Ananda Theertha Suresh, Robin Nittka, Felix X. Yu, Sanjiv Kumar

ICML 2025 InfAlign: Inference-Aware Language Model Alignment Ananth Balashankar, Ziteng Sun, Jonathan Berant, Jacob Eisenstein, Michael Collins, Adrian Hutter, Jong Lee, Chirag Nagpal, Flavien Prost, Aradhana Sinha, Ananda Theertha Suresh, Ahmad Beirami

NeurIPS 2025 Private Set Union with Multiple Contributions Travis Dick, Haim Kaplan, Alex Kulesza, Uri Stemmer, Ziteng Sun, Ananda Theertha Suresh

AISTATS 2025 Rate of Model Collapse in Recursive Training Ananda Theertha Suresh, Andrew Thangaraj, Aditya Nanda Kishore Khandavally

ICML 2025 Theoretical Guarantees on the Best-of-N Alignment Policy Ahmad Beirami, Alekh Agarwal, Jonathan Berant, Alexander D’Amour, Jacob Eisenstein, Chirag Nagpal, Ananda Theertha Suresh

NeurIPS 2024 Accelerating Blockwise Parallel Language Models with Draft Refinement Taehyeon Kim, Ananda Theertha Suresh, Kishore Papineni, Michael Riley, Sanjiv Kumar, Adrian Benton

ICMLW 2024 Block Verification Accelerates Speculative Decoding Ziteng Sun, Uri Mendlovic, Yaniv Leviathan, Asaf Aharoni, Ahmad Beirami, Jae Hun Ro, Ananda Theertha Suresh

ICLRW 2024 Efficient Language Model Architectures for Differentially Private Federated Learning Jae Hun Ro, Srinadh Bhojanapalli, Zheng Xu, Yanxiang Zhang, Ananda Theertha Suresh

ICMLW 2024 Exploring and Improving Drafts in Blockwise Parallel Decoding Taehyeon Kim, Ananda Theertha Suresh, Kishore A Papineni, Michael Riley, Sanjiv Kumar, Adrian Benton

ICML 2024 Mean Estimation in the Add-Remove Model of Differential Privacy Alex Kulesza, Ananda Theertha Suresh, Yuyan Wang

ICLR 2024 The Importance of Feature Preprocessing for Differentially Private Linear Optimization Ziteng Sun, Ananda Theertha Suresh, Aditya Krishna Menon

ICML 2023 Algorithms for Bounding Contribution for Histogram Estimation Under User-Level Privacy Yuhan Liu, Ananda Theertha Suresh, Wennan Zhu, Peter Kairouz, Marco Gruteser

ICML 2023 Federated Heavy Hitter Recovery Under Linear Sketching Adria Gascon, Peter Kairouz, Ziteng Sun, Ananda Theertha Suresh

ICMLW 2023 Federated Heavy Hitter Recovery Under Linear Sketching Adria Gascon, Peter Kairouz, Ziteng Sun, Ananda Theertha Suresh

AISTATS 2023 Principled Approaches for Private Adaptation from a Public Source Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh

NeurIPSW 2023 SpecTr++: Improved Transport Plans for Speculative Decoding of Large Language Models Kwangjun Ahn, Ahmad Beirami, Ziteng Sun, Ananda Theertha Suresh

NeurIPS 2023 SpecTr: Fast Speculative Decoding via Optimal Transport Ziteng Sun, Ananda Theertha Suresh, Jae Hun Ro, Ahmad Beirami, Himanshu Jain, Felix Yu

ICMLW 2023 SpecTr: Fast Speculative Decoding via Optimal Transport Ziteng Sun, Ananda Theertha Suresh, Jae Hun Ro, Ahmad Beirami, Himanshu Jain, Felix Yu, Michael Riley, Sanjiv Kumar

ICML 2023 Subset-Based Instance Optimality in Private Estimation Travis Dick, Alex Kulesza, Ziteng Sun, Ananda Theertha Suresh

ICML 2022 Correlated Quantization for Distributed Mean Estimation and Optimization Ananda Theertha Suresh, Ziteng Sun, Jae Ro, Felix Yu

NeurIPS 2022 Differentially Private Learning with Margin Guarantees Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh

ICLR 2022 On the Benefits of Maximum Likelihood Estimation for Regression and Forecasting Pranjal Awasthi, Abhimanyu Das, Rajat Sen, Ananda Theertha Suresh

COLT 2022 Open Problem: Better Differentially Private Learning Algorithms with Margin Guarantees Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh

COLT 2022 Robust Estimation for Random Graphs Jayadev Acharya, Ayush Jain, Gautam Kamath, Ananda Theertha Suresh, Huanyu Zhang

ICML 2022 The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning Wei-Ning Chen, Christopher A Choquette Choo, Peter Kairouz, Ananda Theertha Suresh

ICML 2021 A Discriminative Technique for Multiple-Source Adaptation Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh, Ningshan Zhang

FnTML 2021 Advances and Open Problems in Federated Learning Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao

NeurIPS 2021 Boosting with Multiple Sources Corinna Cortes, Mehryar Mohri, Dmitry Storcheus, Ananda Theertha Suresh

NeurIPS 2021 Breaking the Centralized Barrier for Cross-Device Federated Learning Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian U Stich, Ananda Theertha Suresh

NeurIPS 2021 Learning with User-Level Privacy Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh

ICML 2021 Relative Deviation Margin Bounds Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh

NeurIPS 2021 Remember What You Want to Forget: Algorithms for Machine Unlearning Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh

ICML 2020 FedBoost: A Communication-Efficient Algorithm for Federated Learning Jenny Hamer, Mehryar Mohri, Ananda Theertha Suresh

NeurIPS 2020 Learning Discrete Distributions: User vs Item-Level Privacy Yuhan Liu, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, Michael Riley

ALT 2020 Optimal Multiclass Overfitting by Sequence Reconstruction from Hamming Queries Jayadev Acharya, Ananda Theertha Suresh

ICML 2020 SCAFFOLD: Stochastic Controlled Averaging for Federated Learning Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, Ananda Theertha Suresh

ICML 2019 Agnostic Federated Learning Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh

NeurIPS 2019 Differentially Private Anonymized Histograms Ananda Theertha Suresh

NeurIPS 2019 Sampled SoftMax with Random Fourier Features Ankit Singh Rawat, Jiecao Chen, Felix Xinnan X Yu, Ananda Theertha Suresh, Sanjiv Kumar

NeurIPS 2018 Data Amplification: A Unified and Competitive Approach to Property Estimation Yi Hao, Alon Orlitsky, Ananda Theertha Suresh, Yihong Wu

JMLR 2018 Maximum Selection and Sorting with Adversarial Comparators Jayadev Acharya, Moein Falahatgar, Ashkan Jafarpour, Alon Orlitsky, Ananda Theertha Suresh

NeurIPS 2018 cpSGD: Communication-Efficient and Differentially-Private Distributed SGD Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, Brendan McMahan

ICML 2017 A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of Discrete Distributions Jayadev Acharya, Hirakendu Das, Alon Orlitsky, Ananda Theertha Suresh

ICML 2017 Distributed Mean Estimation with Limited Communication Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan

ICML 2017 Maximum Selection and Ranking Under Noisy Comparisons Moein Falahatgar, Alon Orlitsky, Venkatadheeraj Pichapati, Ananda Theertha Suresh

NeurIPS 2017 Model-Powered Conditional Independence Test Rajat Sen, Ananda Theertha Suresh, Karthikeyan Shanmugam, Alexandros G Dimakis, Sanjay Shakkottai

NeurIPS 2017 Multiscale Quantization for Fast Similarity Search Xiang Wu, Ruiqi Guo, Ananda Theertha Suresh, Sanjiv Kumar, Daniel N Holtmann-Rice, David Simcha, Felix Yu

COLT 2017 Sample Complexity of Population Recovery Yury Polyanskiy, Ananda Theertha Suresh, Yihong Wu

NeurIPS 2016 Orthogonal Random Features Felix Xinnan X Yu, Ananda Theertha Suresh, Krzysztof M Choromanski, Daniel N Holtmann-Rice, Sanjiv Kumar

NeurIPS 2015 Competitive Distribution Estimation: Why Is Good-Turing Good Alon Orlitsky, Ananda Theertha Suresh

COLT 2015 Faster Algorithms for Testing Under Conditional Sampling Moein Falahatgar, Ashkan Jafarpour, Alon Orlitsky, Venkatadheeraj Pichapati, Ananda Theertha Suresh

COLT 2015 On Learning Distributions from Their Samples Sudeep Kamath, Alon Orlitsky, Dheeraj Pichapati, Ananda Theertha Suresh

AISTATS 2015 Sparse Solutions to Nonnegative Linear Systems and Applications Aditya Bhaskara, Ananda Theertha Suresh, Morteza Zadimoghaddam

NeurIPS 2014 Near-Optimal-Sample Estimators for Spherical Gaussian Mixtures Ananda Theertha Suresh, Alon Orlitsky, Jayadev Acharya, Ashkan Jafarpour

AISTATS 2013 A Competitive Test for Uniformity of Monotone Distributions Jayadev Acharya, Ashkan Jafarpour, Alon Orlitsky, Ananda Theertha Suresh

COLT 2013 Optimal Probability Estimation with Applications to Prediction and Classification Jayadev Acharya, Ashkan Jafarpour, Alon Orlitsky, Ananda Theertha Suresh