Kamath, Pritish

31 publications

AISTATS 2025 Balls-and-Bins Sampling for DP-SGD Lynn Chua, Badih Ghazi, Charlie Harrison, Pritish Kamath, Ravi Kumar, Ethan Jacob Leeman, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang

ICML 2025 Empirical Privacy Variance Yuzheng Hu, Fan Wu, Ruicheng Xian, Yuhang Liu, Lydia Zakynthinou, Pritish Kamath, Chiyuan Zhang, David Forsyth

COLT 2025 PREM: Privately Answering Statistical Queries with Relative Error Badih Ghazi, Cristóbal Guzmán, Pritish Kamath, Alexander Knop, Ravi Kumar, Pasin Manurangsi, Sushant Sachdeva

NeurIPS 2025 Private Hyperparameter Tuning with Ex-Post Guarantee Badih Ghazi, Pritish Kamath, Alexander Knop, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang

NeurIPS 2025 Scaling Embedding Layers in Language Models Da Yu, Edith Cohen, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Daogao Liu, Chiyuan Zhang

ICLR 2025 Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy Yangsibo Huang, Daogao Liu, Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Milad Nasr, Amer Sinha, Chiyuan Zhang

NeurIPSW 2024 Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models Lynn Chua, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chulin Xie, Chiyuan Zhang

NeurIPS 2024 Differentially Private Optimization with Sparse Gradients Badih Ghazi, Cristóbal Guzmán, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

ICML 2024 How Private Are DP-SGD Implementations? Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang

ICML 2024 Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon

ICLR 2024 LabelDP-Pro: Learning with Label Differential Privacy via Projections Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang

COLT 2024 Learning Neural Networks with Sparse Activations Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath, Raghu Meka

COLT 2024 On Convex Optimization with Semi-Sensitive Features Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang

NeurIPS 2024 Scalable DP-SGD: Shuffling vs. Poisson Subsampling Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang

NeurIPS 2023 On Computing Pairwise Statistics with Local Differential Privacy Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon

ICML 2023 On User-Level Private Convex Optimization Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang

NeurIPS 2023 Optimal Unbiased Randomizers for Regression with Label Differential Privacy Ashwinkumar Badanidiyuru Varadaraja, Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash V Varadarajan, Chiyuan Zhang

ICLR 2023 Regression with Label Differential Privacy Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash Varadarajan, Chiyuan Zhang

NeurIPS 2023 Sparsity-Preserving Differentially Private Training of Large Embedding Models Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang

COLT 2023 Ticketed Learning–Unlearning Schemes Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang

NeurIPS 2023 User-Level Differential Privacy with Few Examples per User Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang

NeurIPS 2022 Anonymized Histograms in Intermediate Privacy Models Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

ICML 2022 Do More Negative Samples Necessarily Hurt in Contrastive Learning? Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath

ICML 2022 Faster Privacy Accounting via Evolving Discretization Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

NeurIPS 2022 Private Isotonic Regression Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

NeurIPS 2022 Understanding the Eluder Dimension Gene Li, Pritish Kamath, Dylan J Foster, Nati Srebro

AISTATS 2021 Does Invariant Risk Minimization Capture Invariance? Pritish Kamath, Akilesh Tangella, Danica Sutherland, Nathan Srebro

NeurIPS 2021 On the Power of Differentiable Learning Versus PAC and SQ Learning Emmanuel Abbe, Pritish Kamath, Eran Malach, Colin Sandon, Nathan Srebro

ICML 2021 Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels Eran Malach, Pritish Kamath, Emmanuel Abbe, Nathan Srebro

COLT 2020 Approximate Is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity Pritish Kamath, Omar Montasser, Nathan Srebro

NeurIPS 2018 Bayesian Inference of Temporal Task Specifications from Demonstrations Ankit Shah, Pritish Kamath, Julie A Shah, Shen Li