← Authors

Ananda Theertha Suresh

55 papers · 2013–2025 · 10 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (10) 🌈 Renaissance Researcher (8) 🐝 Cross-Pollinator (12) πŸƒ Academic Marathon (12) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (12) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌟 Keyword Trendsetter Combo (3) πŸ“› The Namer 🀝 Dynamic Duo (13) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (14) πŸ† Keyword Champion (4) πŸ—ƒοΈ Keyword Collector (181) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (13) πŸ’Ž Century Club (55) ⚑ Prolific Year (11)

Conferences

NIPS (17) ICML (16) AISTATS (8) COLT (6) ICLR (3) ALT (1) CONLL (1) INTERSPEECH (1) JMLR (1) UAI (1)

Top co-authors

Mehryar Mohri (13) Alon Orlitsky (9) Ziteng Sun (8) Jayadev Acharya (7) Sanjiv Kumar (7) Peter Kairouz (4) Alex Kulesza (4) Ashkan Jafarpour (4) Felix Xinnan X Yu (4) Felix Yu (4)

Research topics

Statistics (3) Differential Privacy (2)

Keywords

differential privacy (12) federated learning (11) communication efficiency (6) discrete distribution (5) sample complexity (5) statistical estimation (4) distribution estimation (4) mean estimation (4) statistical learning (3) user-level privacy (3) distributed optimization (3) generalization guarantee (3) secure aggregation (2) rademacher complexity (2) domain adaptation (2) transfer learning (2) stochastic optimization (2) kl divergence (2) model selection (2) density estimation (2)

Papers

InfAlign: Inference-aware language model alignment ICML 2025
Block Verification Accelerates Speculative Decoding ICLR 2025
General Staircase Mechanisms for Optimal Differential Privacy AISTATS 2025
Efficient and Asymptotically Unbiased Constrained Decoding for Large Language Models AISTATS 2025
Concept Forgetting via Label Annealing UAI 2025
Rate of Model Collapse in Recursive Training AISTATS 2025
Theoretical guarantees on the best-of-n alignment policy ICML 2025
The importance of feature preprocessing for differentially private linear optimization ICLR 2024
Mean Estimation in the Add-Remove Model of Differential Privacy ICML 2024
Accelerating Blockwise Parallel Language Models with Draft Refinement NIPS 2024
Subset-Based Instance Optimality in Private Estimation ICML 2023
Algorithms for bounding contribution for histogram estimation under user-level privacy ICML 2023
Federated Heavy Hitter Recovery under Linear Sketching ICML 2023
SpecTr: Fast Speculative Decoding via Optimal Transport NIPS 2023
Principled Approaches for Private Adaptation from a Public Source AISTATS 2023
On the benefits of maximum likelihood estimation for Regression and Forecasting ICLR 2022
Differentially Private Learning with Margin Guarantees NIPS 2022
Robust Estimation for Random Graphs COLT 2022
Open Problem: Better Differentially Private Learning Algorithms with Margin Guarantees COLT 2022
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning ICML 2022
Correlated Quantization for Distributed Mean Estimation and Optimization ICML 2022
Breaking the centralized barrier for cross-device federated learning NIPS 2021
Remember What You Want to Forget: Algorithms for Machine Unlearning NIPS 2021
Boosting with Multiple Sources NIPS 2021
Learning with User-Level Privacy NIPS 2021
Communication-Efficient Agnostic Federated Averaging INTERSPEECH 2021
Relative Deviation Margin Bounds ICML 2021
A Discriminative Technique for Multiple-Source Adaptation ICML 2021
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data AISTATS 2021
Shuffled Model of Differential Privacy in Federated Learning AISTATS 2021
Robust hypothesis testing and distribution estimation in Hellinger distance AISTATS 2021
Wyner-Ziv Estimators: Efficient Distributed Mean Estimation with Side-Information AISTATS 2021
FedBoost: A Communication-Efficient Algorithm for Federated Learning ICML 2020
Learning discrete distributions: user vs item-level privacy NIPS 2020
Optimal multiclass overfitting by sequence reconstruction from Hamming queries ALT 2020
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning ICML 2020
Agnostic Federated Learning ICML 2019
Federated Learning of N-Gram Language Models CONLL 2019
Sampled Softmax with Random Fourier Features NIPS 2019
Differentially Private Anonymized Histograms NIPS 2019
cpSGD: Communication-efficient and differentially-private distributed SGD NIPS 2018
Maximum Selection and Sorting with Adversarial Comparators JMLR 2018
Data Amplification: A Unified and Competitive Approach to Property Estimation NIPS 2018
Model-Powered Conditional Independence Test NIPS 2017
Maximum Selection and Ranking under Noisy Comparisons ICML 2017
A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of Discrete Distributions ICML 2017
Sample complexity of population recovery COLT 2017
Distributed Mean Estimation with Limited Communication ICML 2017
Multiscale Quantization for Fast Similarity Search NIPS 2017
Orthogonal Random Features NIPS 2016
Competitive Distribution Estimation: Why is Good-Turing Good NIPS 2015
Faster Algorithms for Testing under Conditional Sampling COLT 2015
On Learning Distributions from their Samples COLT 2015
Near-Optimal-Sample Estimators for Spherical Gaussian Mixtures NIPS 2014
Optimal Probability Estimation with Applications to Prediction and Classification COLT 2013