Using Dominances for Solving the Protein Family Identification Problem. - Noël Malod-Dognin; Mathilde Le Boudic-Jamin; Pritish Kamath; Rumen Andonov (WABI, 2011)

Faster Algorithms for Alternating Refinement Relations. - Krishnendu Chatterjee; Siddhesh Chaubal; Pritish Kamath (CSL, 2012)

Preservation under Substructures modulo Bounded Cores. - Abhisekh Sankaran; Bharat Adsul; Vivek Madan; Pritish Kamath; Supratik Chakraborty (WoLLIC, 2012)

An exponential lower bound for homogeneous depth four arithmetic circuits with bounded bottom fanin. - Ankit Gupta 001; Pritish Kamath; Neeraj Kayal; Ramprasad Saptharishi (Electron. Colloquium Comput. Complex., 2012)

Arithmetic circuits: A chasm at depth three. - Ankit Gupta 001; Pritish Kamath; Neeraj Kayal; Ramprasad Saptharishi (Electron. Colloquium Comput. Complex., 2013)

Approaching the Chasm at Depth Four. - Ankit Gupta 001; Pritish Kamath; Neeraj Kayal; Ramprasad Saptharishi (J. ACM, 2014)

Communication with Partial Noiseless Feedback. - Bernhard Haeupler; Pritish Kamath; Ameya Velingker (APPROX-RANDOM, 2015)

Communication Complexity of Permutation-Invariant Functions. - Badih Ghazi; Pritish Kamath; Madhu Sudan 001 (Electron. Colloquium Comput. Complex., 2015)

The Optimality of Correlated Sampling. - Mohammad Bavarian; Badih Ghazi; Elad Haramaty; Pritish Kamath; Ronald L. Rivest; Madhu Sudan 001 (Electron. Colloquium Comput. Complex., 2016)

Decidability of Non-Interactive Simulation of Joint Distributions. - Badih Ghazi; Pritish Kamath; Madhu Sudan 001 (Electron. Colloquium Comput. Complex., 2016)

Arithmetic Circuits: A Chasm at Depth 3. - Ankit Gupta 001; Pritish Kamath; Neeraj Kayal; Ramprasad Saptharishi (SIAM J. Comput., 2016)

Compression in a Distributed Setting. - Badih Ghazi; Elad Haramaty; Pritish Kamath; Madhu Sudan 001 (ITCS, 2017)

Improved bounds for universal one-bit compressive sensing. - Jayadev Acharya; Arnab Bhattacharyya 001; Pritish Kamath (ISIT, 2017)

Unexpected power of low-depth arithmetic circuits. - Ankit Gupta 001; Pritish Kamath; Neeraj Kayal; Ramprasad Saptharishi (Commun. ACM, 2017)

Monotone Circuit Lower Bounds from Resolution. - Ankit Garg; Mika Göös; Pritish Kamath; Dmitry Sokolov 001 (Electron. Colloquium Comput. Complex., 2017)

Dimension Reduction for Polynomials over Gaussian Space and Applications. - Badih Ghazi; Pritish Kamath; Prasad Raghavendra (Electron. Colloquium Comput. Complex., 2017)

Bayesian Inference of Temporal Task Specifications from Demonstrations. - Ankit Shah 003; Pritish Kamath; Julie A. Shah; Shen Li 003 (NeurIPS, 2018)

Adventures in Monotone Complexity and TFNP. - Mika Göös; Pritish Kamath; Robert Robere; Dmitry Sokolov 001 (Electron. Colloquium Comput. Complex., 2018)

Query-to-Communication Lifting for P NP. - Mika Göös; Pritish Kamath; Toniann Pitassi; Thomas Watson 001 (Comput. Complex., 2019)

Correction to: Query-to-Communication Lifting for P NP. - Mika Göös; Pritish Kamath; Toniann Pitassi; Thomas Watson 001 (Comput. Complex., 2019)

On the Complexity of Modulo-q Arguments and the Chevalley - Warning Theorem. - Mika Göös; Pritish Kamath; Katerina Sotiraki; Manolis Zampetakis (CCC, 2020)

Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity. - Pritish Kamath; Omar Montasser; Nathan Srebro (COLT, 2020)

Optimality of Correlated Sampling Strategies. - Mohammad Bavarian; Badih Ghazi; Elad Haramaty; Pritish Kamath; Ronald L. Rivest; Madhu Sudan 001 (Theory Comput., 2020)

Does Invariant Risk Minimization Capture Invariance? - Pritish Kamath; Akilesh Tangella; Danica J. Sutherland; Nathan Srebro (AISTATS, 2021)

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

On the Power of Differentiable Learning versus PAC and SQ Learning. - Emmanuel Abbe; Pritish Kamath; Eran Malach; Colin Sandon; Nathan Srebro (NeurIPS, 2021)

Eluder Dimension and Generalized Rank. - Gene Li; Pritish Kamath; Dylan J. Foster; Nathan Srebro (CoRR, 2021)

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 001; Pasin Manurangsi (ICML, 2022)

Anonymized Histograms in Intermediate Privacy Models. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi (NeurIPS, 2022)

Private Isotonic Regression. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi (NeurIPS, 2022)

Understanding the Eluder Dimension. - Gene Li; Pritish Kamath; Dylan J. Foster; Nati Srebro (NeurIPS, 2022)

Circuits Resilient to Short-Circuit Errors. - Klim Efremenko; Bernhard Haeupler; Yael Kalai; Pritish Kamath; Gillat Kol; Nicolas Resch; Raghuvansh Saxena (Electron. Colloquium Comput. Complex., 2022)

Limits on the Efficiency of (Ring) LWE-Based Non-interactive Key Exchange. - Siyao Guo; Pritish Kamath; Alon Rosen; Katerina Sotiraki (J. Cryptol., 2022)

Connect the Dots: Tighter Discrete Approximations of Privacy Loss Distributions. - Vadym Doroshenko; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi (Proc. Priv. Enhancing Technol., 2022)

Optimizing Hierarchical Queries for the Attribution Reporting API. - Matthew Dawson; Badih Ghazi; Pritish Kamath; Kapil Kumar; Ravi Kumar 001; Bo Luan; Pasin Manurangsi; Nishanth Mundru; Harikesh Nair; Adam Sealfon; Shengyu Zhu 004 (AdKDD@KDD, 2023)

Private Ad Modeling with DP-SGD. - Carson Denison; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Krishna Giri Narra; Amer Sinha; Avinash V. Varadarajan; Chiyuan Zhang (AdKDD@KDD, 2023)

Ticketed Learning-Unlearning Schemes. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Ayush Sekhari; Chiyuan Zhang (COLT, 2023)

Towards Separating Computational and Statistical Differential Privacy. - Badih Ghazi; Rahul Ilango; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi (FOCS, 2023)

On Differentially Private Counting on Trees. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Kewen Wu 001 (ICALP, 2023)

Regression with Label Differential Privacy. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Ethan Leeman; Pasin Manurangsi; Avinash V. Varadarajan; Chiyuan Zhang (ICLR, 2023)

On User-Level Private Convex Optimization. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Raghu Meka; Chiyuan Zhang (ICML, 2023)

Sparsity-Preserving Differentially Private Training of Large Embedding Models. - Badih Ghazi; Yangsibo Huang; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (NeurIPS, 2023)

User-Level Differential Privacy With Few Examples Per User. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Raghu Meka; Chiyuan Zhang (NeurIPS, 2023)

On Computing Pairwise Statistics with Local Differential Privacy. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Adam Sealfon (NeurIPS, 2023)

Optimal Unbiased Randomizers for Regression with Label Differential Privacy. - Ashwinkumar Badanidiyuru Varadaraja; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Ethan Leeman; Pasin Manurangsi; Avinash V. Varadarajan; Chiyuan Zhang (NeurIPS, 2023)

Separating Computational and Statistical Differential Privacy (Under Plausible Assumptions). - Badih Ghazi; Rahul Ilango; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi (CoRR, 2023)

Supervised Bayesian specification inference from demonstrations. - Ankit Shah 003; Pritish Kamath; Shen Li 003; Patrick L. Craven; Kevin J. Landers; Kevin Oden; Julie Shah (Int. J. Robotics Res., 2023)

Training Differentially Private Ad Prediction Models With Semi-Sensitive Features. - Lynn Chua; Qiliang Cui; Badih Ghazi; Charlie Harrison; Pritish Kamath; Walid Krichene; Ravi Kumar 001; Pasin Manurangsi; Nicolas Mayoraz; Hema Venkata Krishna Giri Narra; Steffen Rendle; Amer Sinha; et al. (AdKDD@KDD, 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 001; Pasin Manurangsi; Raghu Meka; Chiyuan Zhang (COLT, 2024)

LabelDP-Pro: Learning with Label Differential Privacy via Projections. - Badih Ghazi; Yangsibo Huang; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Chiyuan Zhang (ICLR, 2024)

How Private are DP-SGD Implementations? - Lynn Chua; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (ICML, 2024)

Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Adam Sealfon (ICML, 2024)

Scalable DP-SGD: Shuffling vs. Poisson Subsampling. - Lynn Chua; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (NeurIPS, 2024)

Differentially Private Optimization with Sparse Gradients. - Badih Ghazi; Cristóbal Guzmán; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi (NeurIPS, 2024)

Training Differentially Private Ad Prediction Models with Semi-Sensitive Features. - Lynn Chua; Qiliang Cui; Badih Ghazi; Charlie Harrison; Pritish Kamath; Walid Krichene; Ravi Kumar 001; Pasin Manurangsi; Krishna Giri Narra; Amer Sinha; Avinash V. Varadarajan; Chiyuan Zhang (CoRR, 2024)

How Private is DP-SGD? - Lynn Chua; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (CoRR, 2024)

Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning. - Lynn Chua; Badih Ghazi; Yangsibo Huang; Pritish Kamath; Daogao Liu; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (CoRR, 2024)

Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models. - Lynn Chua; Badih Ghazi; Yangsibo Huang; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Amer Sinha; Chulin Xie; Chiyuan Zhang (CoRR, 2024)

On the Differential Privacy and Interactivity of Privacy Sandbox Reports. - Badih Ghazi; Charlie Harrison; Arpana Hosabettu; Pritish Kamath; Alexander Knop; S. Ravi Kumar 001; Ethan Leeman; Pasin Manurangsi; Vikas Sahu (CoRR, 2024)

Summary Reports Optimization in the Privacy Sandbox Attribution Reporting API. - Hidayet Aksu; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Adam Sealfon; Avinash V. Varadarajan (Proc. Priv. Enhancing Technol., 2024)

Balls-and-Bins Sampling for DP-SGD. - Lynn Chua; Badih Ghazi; Charlie Harrison; Pritish Kamath; Ravi Kumar 001; Ethan Leeman; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (AISTATS, 2025)

PREM: Privately Answering Statistical Queries with Relative Error. - Badih Ghazi; Cristóbal Guzmán; Pritish Kamath; Alexander Knop; Ravi Kumar 001; Pasin Manurangsi; Sushant Sachdeva (COLT, 2025)

Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy. - Yangsibo Huang; Daogao Liu; Lynn Chua; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Milad Nasr; Amer Sinha; Chiyuan Zhang (ICLR, 2025)

How Unique is Whose Web Browser? The role of demographics in browser fingerprinting among US users. - Alex Berke; Badih Ghazi; Enrico Bacis; Pritish Kamath; Ravi Kumar 001; Robin Lassonde; Pasin Manurangsi; Umar Syed (Proc. Priv. Enhancing Technol., 2025)

On the Differential Privacy and Interactivity of Privacy Sandbox Reports. - Badih Ghazi; Charlie Harrison; Arpana Hosabettu; Pritish Kamath; Alexander Knop; Ravi Kumar 001; Ethan Leeman; Pasin Manurangsi; Mariana Raykova 001; Vikas Sahu; Phillipp Schoppmann (Proc. Priv. Enhancing Technol., 2025)

Scaling Embedding Layers in Language Models. - Da Yu; Edith Cohen; Badih Ghazi; Yangsibo Huang; Pritish Kamath; Ravi Kumar 001; Daogao Liu; Chiyuan Zhang (CoRR, 2025)

Empirical Privacy Variance. - Yuzheng Hu; Fan Wu 011; Ruicheng Xian; Yuhang Liu; Lydia Zakynthinou; Pritish Kamath; Chiyuan Zhang; David A. Forsyth (CoRR, 2025)

Urania: Differentially Private Insights into AI Use. - Daogao Liu; Edith Cohen; Badih Ghazi; Peter Kairouz; Pritish Kamath; Alexander Knop; Ravi Kumar 001; Pasin Manurangsi; Adam Sealfon; Da Yu; Chiyuan Zhang (CoRR, 2025)

Private Hyperparameter Tuning with Ex-Post Guarantee. - Badih Ghazi; Pritish Kamath; Alexander Knop; Ravi Kumar 001; Pasin Manurangsi; Chiyuan Zhang (CoRR, 2025)