Pritish Kamath

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 89

Top Venues

CoRR

NeurIPS

Electron. Colloquium Comput. Complex.

ICML

Publications

Hidayet Aksu, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon, Avinash V. Varadarajan
Summary Reports Optimization in the Privacy Sandbox Attribution Reporting API. Proc. Priv. Enhancing Technol. 2024 (4) (2024)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang
On Convex Optimization with Semi-Sensitive Features. COLT (2024)
Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath, Raghu Meka
Learning Neural Networks with Sparse Activations. COLT (2024)
Lynn Chua, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Daogao Liu, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang
Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning. CoRR (2024)
Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang
How Private is DP-SGD? CoRR (2024)
Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath, Raghu Meka
Learning Neural Networks with Sparse Activations. CoRR (2024)
Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang
LabelDP-Pro: Learning with Label Differential Privacy via Projections. ICLR (2024)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang
On Convex Optimization with Semi-Sensitive Features. CoRR (2024)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon
Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization. CoRR (2024)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon
On Computing Pairwise Statistics with Local Differential Privacy. CoRR (2024)
Lynn Chua, Qiliang Cui, Badih Ghazi, Charlie Harrison, Pritish Kamath, Walid Krichene, Ravi Kumar, Pasin Manurangsi, Krishna Giri Narra, Amer Sinha, Avinash V. Varadarajan, Chiyuan Zhang
Training Differentially Private Ad Prediction Models with Semi-Sensitive Features. CoRR (2024)
Lynn Chua, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chulin Xie, Chiyuan Zhang
Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models. CoRR (2024)
Badih Ghazi, Cristóbal Guzmán, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Differentially Private Optimization with Sparse Gradients. CoRR (2024)
Badih Ghazi, Rahul Ilango, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Separating Computational and Statistical Differential Privacy (Under Plausible Assumptions). CoRR (2023)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Raghu Meka, Pasin Manurangsi, Chiyuan Zhang
On User-Level Private Convex Optimization. CoRR (2023)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang
Ticketed Learning-Unlearning Schemes. COLT (2023)
Ashwinkumar Badanidiyuru Varadaraja, Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash V. Varadarajan, Chiyuan Zhang
Optimal Unbiased Randomizers for Regression with Label Differential Privacy. NeurIPS (2023)
Carson Denison, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Krishna Giri Narra, Amer Sinha, Avinash V. Varadarajan, Chiyuan Zhang
Private Ad Modeling with DP-SGD. AdKDD@KDD (2023)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash Varadarajan, Chiyuan Zhang
Regression with Label Differential Privacy. ICLR (2023)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang
On User-Level Private Convex Optimization. ICML (2023)
Badih Ghazi, Rahul Ilango, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Towards Separating Computational and Statistical Differential Privacy. FOCS (2023)
Matthew Dawson, Badih Ghazi, Pritish Kamath, Kapil Kumar, Ravi Kumar, Bo Luan, Pasin Manurangsi, Nishanth Mundru, Harikesh Nair, Adam Sealfon, Shengyu Zhu
Optimizing Hierarchical Queries for the Attribution Reporting API. CoRR (2023)
Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang
Sparsity-Preserving Differentially Private Training of Large Embedding Models. NeurIPS (2023)
Matthew Dawson, Badih Ghazi, Pritish Kamath, Kapil Kumar, Ravi Kumar, Bo Luan, Pasin Manurangsi, Nishanth Mundru, Harikesh Nair, Adam Sealfon, Shengyu Zhu
Optimizing Hierarchical Queries for the Attribution Reporting API. AdKDD@KDD (2023)
Hidayet Aksu, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon, Avinash V. Varadarajan
Summary Reports Optimization in the Privacy Sandbox Attribution Reporting API. CoRR (2023)
Ankit Shah, Pritish Kamath, Shen Li, Patrick L. Craven, Kevin J. Landers, Kevin Oden, Julie Shah
Supervised Bayesian specification inference from demonstrations. Int. J. Robotics Res. 42 (14) (2023)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang
User-Level Differential Privacy With Few Examples Per User. CoRR (2023)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Kewen Wu
On Differentially Private Counting on Trees. ICALP (2023)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon
On Computing Pairwise Statistics with Local Differential Privacy. NeurIPS (2023)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang
Ticketed Learning-Unlearning Schemes. CoRR (2023)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang
User-Level Differential Privacy With Few Examples Per User. NeurIPS (2023)
Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang
Sparsity-Preserving Differentially Private Training of Large Embedding Models. CoRR (2023)
Ashwinkumar Badanidiyuru, Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash V. Varadarajan, Chiyuan Zhang
Optimal Unbiased Randomizers for Regression with Label Differential Privacy. CoRR (2023)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Faster Privacy Accounting via Evolving Discretization. ICML (2022)
Vadym Doroshenko, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Connect the Dots: Tighter Discrete Approximations of Privacy Loss Distributions. CoRR (2022)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Anonymized Histograms in Intermediate Privacy Models. CoRR (2022)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Faster Privacy Accounting via Evolving Discretization. CoRR (2022)
Siyao Guo, Pritish Kamath, Alon Rosen, Katerina Sotiraki
Limits on the Efficiency of (Ring) LWE-Based Non-interactive Key Exchange. J. Cryptol. 35 (1) (2022)
Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath
Do More Negative Samples Necessarily Hurt In Contrastive Learning? ICML (2022)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Kewen Wu
On Differentially Private Counting on Trees. CoRR (2022)
Klim Efremenko, Bernhard Haeupler, Yael Kalai, Pritish Kamath, Gillat Kol, Nicolas Resch, Raghuvansh Saxena
Circuits Resilient to Short-Circuit Errors. Electron. Colloquium Comput. Complex. (2022)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash Varadarajan, Chiyuan Zhang
Regression with Label Differential Privacy. CoRR (2022)
Vadym Doroshenko, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Connect the Dots: Tighter Discrete Approximations of Privacy Loss Distributions. Proc. Priv. Enhancing Technol. 2022 (4) (2022)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Private Isotonic Regression. NeurIPS (2022)
Carson Denison, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Krishna Giri Narra, Amer Sinha, Avinash Varadarajan, Chiyuan Zhang
Private Ad Modeling with DP-SGD. CoRR (2022)
Klim Efremenko, Bernhard Haeupler, Yael Tauman Kalai, Pritish Kamath, Gillat Kol, Nicolas Resch, Raghuvansh R. Saxena
Circuits resilient to short-circuit errors. STOC (2022)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Anonymized Histograms in Intermediate Privacy Models. NeurIPS (2022)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Private Isotonic Regression. CoRR (2022)
Gene Li, Pritish Kamath, Dylan J. Foster, Nati Srebro
Understanding the Eluder Dimension. NeurIPS (2022)
Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath
Do More Negative Samples Necessarily Hurt in Contrastive Learning? CoRR (2022)
Ankit J. Shah, Pritish Kamath, Shen Li, Patrick L. Craven, Kevin J. Landers, Kevin Oden, Julie Shah
Supervised Bayesian Specification Inference from Demonstrations. CoRR (2021)
Eran Malach, Pritish Kamath, Emmanuel Abbe, Nathan Srebro
Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels. CoRR (2021)
Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, Nathan Srebro
Does Invariant Risk Minimization Capture Invariance? AISTATS (2021)
Emmanuel Abbe, Pritish Kamath, Eran Malach, Colin Sandon, Nathan Srebro
On the Power of Differentiable Learning versus PAC and SQ Learning. CoRR (2021)
Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, Nathan Srebro
Does Invariant Risk Minimization Capture Invariance? CoRR (2021)
Emmanuel Abbe, Pritish Kamath, Eran Malach, Colin Sandon, Nathan Srebro
On the Power of Differentiable Learning versus PAC and SQ Learning. NeurIPS (2021)
Eran Malach, Pritish Kamath, Emmanuel Abbe, Nathan Srebro
Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels. ICML (2021)
Gene Li, Pritish Kamath, Dylan J. Foster, Nathan Srebro
Eluder Dimension and Generalized Rank. CoRR (2021)
Ankit Garg, Mika Göös, Pritish Kamath, Dmitry Sokolov
Monotone Circuit Lower Bounds from Resolution. Theory Comput. 16 (2020)
Mika Göös, Pritish Kamath, Katerina Sotiraki, Manolis Zampetakis
On the Complexity of Modulo-q Arguments and the Chevalley - Warning Theorem. Computational Complexity Conference (2020)
Siyao Guo, Pritish Kamath, Alon Rosen, Katerina Sotiraki
Limits on the Efficiency of (Ring) LWE based Non-Interactive Key Exchange. IACR Cryptol. ePrint Arch. 2020 (2020)
Pritish Kamath, Omar Montasser, Nathan Srebro
Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity. CoRR (2020)
Mohammad Bavarian, Badih Ghazi, Elad Haramaty, Pritish Kamath, Ronald L. Rivest, Madhu Sudan
Optimality of Correlated Sampling Strategies. Theory Comput. 16 (2020)
Siyao Guo, Pritish Kamath, Alon Rosen, Katerina Sotiraki
Limits on the Efficiency of (Ring) LWE Based Non-interactive Key Exchange. Public Key Cryptography (1) (2020)
Pritish Kamath, Omar Montasser, Nathan Srebro
Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity. COLT (2020)
Mika Göös, Pritish Kamath, Katerina Sotiraki, Manolis Zampetakis
On the Complexity of Modulo-q Arguments and the Chevalley-Warning Theorem. CoRR (2019)
Mika Göös, Pritish Kamath, Toniann Pitassi, Thomas Watson
Correction to: Query-to-Communication Lifting for P NP. Comput. Complex. 28 (3) (2019)
Mika Göös, Pritish Kamath, Robert Robere, Dmitry Sokolov
Adventures in Monotone Complexity and TFNP. ITCS (2019)
Mika Göös, Pritish Kamath, Toniann Pitassi, Thomas Watson
Query-to-Communication Lifting for P NP. Comput. Complex. 28 (1) (2019)
Ankit Shah, Pritish Kamath, Julie A. Shah, Shen Li
Bayesian Inference of Temporal Task Specifications from Demonstrations. NeurIPS (2018)
Badih Ghazi, Pritish Kamath, Prasad Raghavendra
Dimension Reduction for Polynomials over Gaussian Space and Applications. Computational Complexity Conference (2018)
Mika Göös, Pritish Kamath, Robert Robere, Dmitry Sokolov
Adventures in Monotone Complexity and TFNP. Electron. Colloquium Comput. Complex. 25 (2018)
Ankit Garg, Mika Göös, Pritish Kamath, Dmitry Sokolov
Monotone circuit lower bounds from resolution. STOC (2018)
Mika Göös, Pritish Kamath, Toniann Pitassi, Thomas Watson
Query-to-Communication Lifting for P^NP. Electron. Colloquium Comput. Complex. 24 (2017)
Badih Ghazi, Pritish Kamath, Prasad Raghavendra
Dimension Reduction for Polynomials over Gaussian Space and Applications. Electron. Colloquium Comput. Complex. 24 (2017)
Mika Göös, Pritish Kamath, Toniann Pitassi, Thomas Watson
Query-to-Communication Lifting for P^NP. Computational Complexity Conference (2017)
Jayadev Acharya, Arnab Bhattacharyya, Pritish Kamath
Improved bounds for universal one-bit compressive sensing. ISIT (2017)
Badih Ghazi, Elad Haramaty, Pritish Kamath, Madhu Sudan
Compression in a Distributed Setting. ITCS (2017)
Ankit Garg, Mika Göös, Pritish Kamath, Dmitry Sokolov
Monotone Circuit Lower Bounds from Resolution. Electron. Colloquium Comput. Complex. 24 (2017)
Badih Ghazi, Pritish Kamath, Prasad Raghavendra
Dimension Reduction for Polynomials over Gaussian Space and Applications. CoRR (2017)
Ankit Gupta, Pritish Kamath, Neeraj Kayal, Ramprasad Saptharishi
Unexpected power of low-depth arithmetic circuits. Commun. ACM 60 (6) (2017)
Jayadev Acharya, Arnab Bhattacharyya, Pritish Kamath
Improved Bounds for Universal One-Bit Compressive Sensing. CoRR (2017)
Badih Ghazi, Pritish Kamath, Madhu Sudan
Communication Complexity of Permutation-Invariant Functions. SODA (2016)
Ankit Gupta, Pritish Kamath, Neeraj Kayal, Ramprasad Saptharishi
Arithmetic Circuits: A Chasm at Depth 3. SIAM J. Comput. 45 (3) (2016)
Mohammad Bavarian, Badih Ghazi, Elad Haramaty, Pritish Kamath, Ronald L. Rivest, Madhu Sudan
The Optimality of Correlated Sampling. CoRR (2016)
Badih Ghazi, Pritish Kamath, Madhu Sudan
Decidability of Non-Interactive Simulation of Joint Distributions. Electron. Colloquium Comput. Complex. 23 (2016)
Badih Ghazi, Pritish Kamath, Madhu Sudan
Decidability of Non-interactive Simulation of Joint Distributions. FOCS (2016)
Mohammad Bavarian, Badih Ghazi, Elad Haramaty, Pritish Kamath, Ronald L. Rivest, Madhu Sudan
The Optimality of Correlated Sampling. Electron. Colloquium Comput. Complex. 23 (2016)
Badih Ghazi, Pritish Kamath, Madhu Sudan
Decidability of Non-Interactive Simulation of Joint Distributions. CoRR (2016)
Badih Ghazi, Pritish Kamath, Madhu Sudan
Communication Complexity of Permutation-Invariant Functions. Electron. Colloquium Comput. Complex. 22 (2015)
Badih Ghazi, Pritish Kamath, Madhu Sudan
Communication Complexity of Permutation-Invariant Functions. CoRR (2015)
Bernhard Haeupler, Pritish Kamath, Ameya Velingker
Communication with Partial Noiseless Feedback. APPROX-RANDOM (2015)
Ankit Gupta, Pritish Kamath, Neeraj Kayal, Ramprasad Saptharishi
Approaching the Chasm at Depth Four. J. ACM 61 (6) (2014)
Ankit Gupta, Pritish Kamath, Neeraj Kayal, Ramprasad Saptharishi
Arithmetic Circuits: A Chasm at Depth Three. FOCS (2013)
Ankit Gupta, Pritish Kamath, Neeraj Kayal, Ramprasad Saptharishi
Approaching the Chasm at Depth Four. Computational Complexity Conference (2013)
Ankit Gupta, Pritish Kamath, Neeraj Kayal, Ramprasad Saptharishi
Arithmetic circuits: A chasm at depth three. Electron. Colloquium Comput. Complex. 20 (2013)
Krishnendu Chatterjee, Siddhesh Chaubal, Pritish Kamath
Faster Algorithms for Alternating Refinement Relations. CSL (2012)
Ankit Gupta, Pritish Kamath, Neeraj Kayal, Ramprasad Saptharishi
An exponential lower bound for homogeneous depth four arithmetic circuits with bounded bottom fanin. Electron. Colloquium Comput. Complex. 19 (2012)
Krishnendu Chatterjee, Siddhesh Chaubal, Pritish Kamath
Faster Algorithms for Alternating Refinement Relations CoRR (2012)
Abhisekh Sankaran, Bharat Adsul, Vivek Madan, Pritish Kamath, Supratik Chakraborty
Preservation under Substructures modulo Bounded Cores CoRR (2012)

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Publication Activity (10 Years)

Top Topics

Top Venues

Pritish Kamath