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2020 – today
- 2024
[j11]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): 605-621 (2024)- [c43]Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath, Raghu Meka:
Learning Neural Networks with Sparse Activations. COLT 2024: 406-425 - [c42]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
On Convex Optimization with Semi-Sensitive Features. COLT 2024: 1916-1938 - [c41]Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang:
LabelDP-Pro: Learning with Label Differential Privacy via Projections. ICLR 2024 - [c40]Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang:
How Private are DP-SGD Implementations? ICML 2024 - [c39]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon:
Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization. ICML 2024 - [i50]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 abs/2401.15246 (2024) - [i49]Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang:
How Private is DP-SGD? CoRR abs/2403.17673 (2024) - [i48]Badih Ghazi, Cristóbal Guzmán, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Differentially Private Optimization with Sparse Gradients. CoRR abs/2404.10881 (2024) - [i47]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon:
Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization. CoRR abs/2405.18534 (2024) - [i46]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 abs/2406.14322 (2024) - [i45]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 abs/2406.16135 (2024) - [i44]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon:
On Computing Pairwise Statistics with Local Differential Privacy. CoRR abs/2406.16305 (2024) - [i43]Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath, Raghu Meka:
Learning Neural Networks with Sparse Activations. CoRR abs/2406.17989 (2024) - [i42]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
On Convex Optimization with Semi-Sensitive Features. CoRR abs/2406.19040 (2024) - 2023
- [j10]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): 1245-1264 (2023) - [c38]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 - [c37]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 - [c36]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang:
Ticketed Learning-Unlearning Schemes. COLT 2023: 5110-5139 - [c35]Badih Ghazi, Rahul Ilango, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Towards Separating Computational and Statistical Differential Privacy. FOCS 2023: 580-599 - [c34]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Kewen Wu:
On Differentially Private Counting on Trees. ICALP 2023: 66:1-66:18 - [c33]Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash V. Varadarajan, Chiyuan Zhang:
Regression with Label Differential Privacy. ICLR 2023 - [c32]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
On User-Level Private Convex Optimization. ICML 2023: 11283-11299 - [c31]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 - [c30]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
User-Level Differential Privacy With Few Examples Per User. NeurIPS 2023 - [c29]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon:
On Computing Pairwise Statistics with Local Differential Privacy. NeurIPS 2023 - [c28]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 - [i41]Badih Ghazi, Rahul Ilango, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Separating Computational and Statistical Differential Privacy (Under Plausible Assumptions). CoRR abs/2301.00104 (2023) - [i40]Badih Ghazi, Pritish Kamath, Ravi Kumar, Raghu Meka, Pasin Manurangsi, Chiyuan Zhang:
On User-Level Private Convex Optimization. CoRR abs/2305.04912 (2023) - [i39]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang:
Ticketed Learning-Unlearning Schemes. CoRR abs/2306.15744 (2023) - [i38]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 abs/2308.13510 (2023) - [i37]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
User-Level Differential Privacy With Few Examples Per User. CoRR abs/2309.12500 (2023) - [i36]Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang:
Sparsity-Preserving Differentially Private Training of Large Embedding Models. CoRR abs/2311.08357 (2023) - [i35]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 abs/2311.13586 (2023) - [i34]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 abs/2312.05659 (2023) - 2022
- [j9]Siyao Guo, Pritish Kamath, Alon Rosen, Katerina Sotiraki:
Limits on the Efficiency of (Ring) LWE-Based Non-interactive Key Exchange. J. Cryptol. 35(1): 1 (2022) - [j8]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): 552-570 (2022) - [c27]Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath:
Do More Negative Samples Necessarily Hurt In Contrastive Learning? ICML 2022: 1101-1116 - [c26]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Faster Privacy Accounting via Evolving Discretization. ICML 2022: 7470-7483 - [c25]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Anonymized Histograms in Intermediate Privacy Models. NeurIPS 2022 - [c24]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Private Isotonic Regression. NeurIPS 2022 - [c23]Gene Li, Pritish Kamath, Dylan J. Foster, Nati Srebro:
Understanding the Eluder Dimension. NeurIPS 2022 - [c22]Klim Efremenko, Bernhard Haeupler, Yael Tauman Kalai, Pritish Kamath, Gillat Kol, Nicolas Resch, Raghuvansh R. Saxena:
Circuits resilient to short-circuit errors. STOC 2022: 582-594 - [i33]Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath:
Do More Negative Samples Necessarily Hurt in Contrastive Learning? CoRR abs/2205.01789 (2022) - [i32]Vadym Doroshenko, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Connect the Dots: Tighter Discrete Approximations of Privacy Loss Distributions. CoRR abs/2207.04380 (2022) - [i31]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Faster Privacy Accounting via Evolving Discretization. CoRR abs/2207.04381 (2022) - [i30]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Private Isotonic Regression. CoRR abs/2210.15175 (2022) - [i29]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Anonymized Histograms in Intermediate Privacy Models. CoRR abs/2210.15178 (2022) - [i28]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. CoRR abs/2211.11896 (2022) - [i27]Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash V. Varadarajan, Chiyuan Zhang:
Regression with Label Differential Privacy. CoRR abs/2212.06074 (2022) - [i26]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Kewen Wu:
On Differentially Private Counting on Trees. CoRR abs/2212.11967 (2022) - [i25]Klim Efremenko, Bernhard Haeupler, Yael Kalai, Pritish Kamath, Gillat Kol, Nicolas Resch, Raghuvansh Saxena:
Circuits Resilient to Short-Circuit Errors. Electron. Colloquium Comput. Complex. TR22 (2022) - 2021
- [c21]Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, Nathan Srebro:
Does Invariant Risk Minimization Capture Invariance? AISTATS 2021: 4069-4077 - [c20]Eran Malach, Pritish Kamath, Emmanuel Abbe, Nathan Srebro:
Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels. ICML 2021: 7379-7389 - [c19]Emmanuel Abbe, Pritish Kamath, Eran Malach, Colin Sandon, Nathan Srebro:
On the Power of Differentiable Learning versus PAC and SQ Learning. NeurIPS 2021: 24340-24351 - [i24]Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, Nathan Srebro:
Does Invariant Risk Minimization Capture Invariance? CoRR abs/2101.01134 (2021) - [i23]Eran Malach, Pritish Kamath, Emmanuel Abbe, Nathan Srebro:
Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels. CoRR abs/2103.01210 (2021) - [i22]Gene Li, Pritish Kamath, Dylan J. Foster, Nathan Srebro:
Eluder Dimension and Generalized Rank. CoRR abs/2104.06970 (2021) - [i21]Ankit J. Shah, Pritish Kamath, Shen Li, Patrick L. Craven, Kevin J. Landers, Kevin Oden, Julie Shah:
Supervised Bayesian Specification Inference from Demonstrations. CoRR abs/2107.02912 (2021) - [i20]Emmanuel Abbe, Pritish Kamath, Eran Malach, Colin Sandon, Nathan Srebro:
On the Power of Differentiable Learning versus PAC and SQ Learning. CoRR abs/2108.04190 (2021) - 2020
- [j7]Mohammad Bavarian, Badih Ghazi, Elad Haramaty, Pritish Kamath, Ronald L. Rivest, Madhu Sudan:
Optimality of Correlated Sampling Strategies. Theory Comput. 16: 1-18 (2020) - [j6]Ankit Garg, Mika Göös, Pritish Kamath, Dmitry Sokolov:
Monotone Circuit Lower Bounds from Resolution. Theory Comput. 16: 1-30 (2020) - [c18]Mika Göös, Pritish Kamath, Katerina Sotiraki, Manolis Zampetakis:
On the Complexity of Modulo-q Arguments and the Chevalley - Warning Theorem. CCC 2020: 19:1-19:42 - [c17]Pritish Kamath, Omar Montasser, Nathan Srebro:
Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity. COLT 2020: 2236-2262 - [c16]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: 374-395 - [i19]Pritish Kamath, Omar Montasser, Nathan Srebro:
Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity. CoRR abs/2003.04180 (2020) - [i18]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: 1555 (2020)
2010 – 2019
- 2019
- [j5]Mika Göös, Pritish Kamath, Toniann Pitassi, Thomas Watson:
Query-to-Communication Lifting for P NP. Comput. Complex. 28(1): 113-144 (2019) - [j4]Mika Göös, Pritish Kamath, Toniann Pitassi, Thomas Watson:
Correction to: Query-to-Communication Lifting for P NP. Comput. Complex. 28(3): 543-544 (2019) - [c15]Mika Göös, Pritish Kamath, Robert Robere, Dmitry Sokolov:
Adventures in Monotone Complexity and TFNP. ITCS 2019: 38:1-38:19 - [i17]Mika Göös, Pritish Kamath, Katerina Sotiraki, Manolis Zampetakis:
On the Complexity of Modulo-q Arguments and the Chevalley-Warning Theorem. CoRR abs/1912.04467 (2019) - 2018
- [c14]Badih Ghazi, Pritish Kamath, Prasad Raghavendra:
Dimension Reduction for Polynomials over Gaussian Space and Applications. CCC 2018: 28:1-28:37 - [c13]Ankit Shah, Pritish Kamath, Julie A. Shah, Shen Li:
Bayesian Inference of Temporal Task Specifications from Demonstrations. NeurIPS 2018: 3808-3817 - [c12]Ankit Garg, Mika Göös, Pritish Kamath, Dmitry Sokolov:
Monotone circuit lower bounds from resolution. STOC 2018: 902-911 - [i16]Mika Göös, Pritish Kamath, Robert Robere, Dmitry Sokolov:
Adventures in Monotone Complexity and TFNP. Electron. Colloquium Comput. Complex. TR18 (2018) - 2017
- [j3]Ankit Gupta, Pritish Kamath, Neeraj Kayal, Ramprasad Saptharishi:
Unexpected power of low-depth arithmetic circuits. Commun. ACM 60(6): 93-100 (2017) - [c11]Mika Göös, Pritish Kamath, Toniann Pitassi, Thomas Watson:
Query-to-Communication Lifting for P^NP. CCC 2017: 12:1-12:16 - [c10]Badih Ghazi, Elad Haramaty, Pritish Kamath, Madhu Sudan:
Compression in a Distributed Setting. ITCS 2017: 19:1-19:22 - [c9]Jayadev Acharya, Arnab Bhattacharyya, Pritish Kamath:
Improved bounds for universal one-bit compressive sensing. ISIT 2017: 2353-2357 - [i15]Jayadev Acharya, Arnab Bhattacharyya, Pritish Kamath:
Improved Bounds for Universal One-Bit Compressive Sensing. CoRR abs/1705.00763 (2017) - [i14]Badih Ghazi, Pritish Kamath, Prasad Raghavendra:
Dimension Reduction for Polynomials over Gaussian Space and Applications. CoRR abs/1708.03808 (2017) - [i13]Ankit Garg, Mika Göös, Pritish Kamath, Dmitry Sokolov:
Monotone Circuit Lower Bounds from Resolution. Electron. Colloquium Comput. Complex. TR17 (2017) - [i12]Badih Ghazi, Pritish Kamath, Prasad Raghavendra:
Dimension Reduction for Polynomials over Gaussian Space and Applications. Electron. Colloquium Comput. Complex. TR17 (2017) - [i11]Mika Göös, Pritish Kamath, Toniann Pitassi, Thomas Watson:
Query-to-Communication Lifting for P^NP. Electron. Colloquium Comput. Complex. TR17 (2017) - 2016
- [j2]Ankit Gupta, Pritish Kamath, Neeraj Kayal, Ramprasad Saptharishi:
Arithmetic Circuits: A Chasm at Depth 3. SIAM J. Comput. 45(3): 1064-1079 (2016) - [c8]Badih Ghazi, Pritish Kamath, Madhu Sudan:
Decidability of Non-interactive Simulation of Joint Distributions. FOCS 2016: 545-554 - [c7]Badih Ghazi, Pritish Kamath, Madhu Sudan:
Communication Complexity of Permutation-Invariant Functions. SODA 2016: 1902-1921 - [i10]Badih Ghazi, Pritish Kamath, Madhu Sudan:
Decidability of Non-Interactive Simulation of Joint Distributions. CoRR abs/1607.04322 (2016) - [i9]Mohammad Bavarian, Badih Ghazi, Elad Haramaty, Pritish Kamath, Ronald L. Rivest, Madhu Sudan:
The Optimality of Correlated Sampling. CoRR abs/1612.01041 (2016) - [i8]Mohammad Bavarian, Badih Ghazi, Elad Haramaty, Pritish Kamath, Ronald L. Rivest, Madhu Sudan:
The Optimality of Correlated Sampling. Electron. Colloquium Comput. Complex. TR16 (2016) - [i7]Badih Ghazi, Pritish Kamath, Madhu Sudan:
Decidability of Non-Interactive Simulation of Joint Distributions. Electron. Colloquium Comput. Complex. TR16 (2016) - 2015
- [c6]Bernhard Haeupler, Pritish Kamath, Ameya Velingker:
Communication with Partial Noiseless Feedback. APPROX-RANDOM 2015: 881-897 - [i6]Badih Ghazi, Pritish Kamath, Madhu Sudan:
Communication Complexity of Permutation-Invariant Functions. CoRR abs/1506.00273 (2015) - [i5]Badih Ghazi, Pritish Kamath, Madhu Sudan:
Communication Complexity of Permutation-Invariant Functions. Electron. Colloquium Comput. Complex. TR15 (2015) - 2014
- [j1]Ankit Gupta, Pritish Kamath, Neeraj Kayal, Ramprasad Saptharishi:
Approaching the Chasm at Depth Four. J. ACM 61(6): 33:1-33:16 (2014) - 2013
- [c5]Ankit Gupta, Pritish Kamath, Neeraj Kayal, Ramprasad Saptharishi:
Approaching the Chasm at Depth Four. CCC 2013: 65-73 - [c4]Ankit Gupta, Pritish Kamath, Neeraj Kayal, Ramprasad Saptharishi:
Arithmetic Circuits: A Chasm at Depth Three. FOCS 2013: 578-587 - [i4]Ankit Gupta, Pritish Kamath, Neeraj Kayal, Ramprasad Saptharishi:
Arithmetic circuits: A chasm at depth three. Electron. Colloquium Comput. Complex. TR13 (2013) - 2012
- [c3]Krishnendu Chatterjee, Siddhesh Chaubal, Pritish Kamath:
Faster Algorithms for Alternating Refinement Relations. CSL 2012: 167-182 - [c2]Abhisekh Sankaran, Bharat Adsul, Vivek Madan, Pritish Kamath, Supratik Chakraborty:
Preservation under Substructures modulo Bounded Cores. WoLLIC 2012: 291-305 - [i3]Krishnendu Chatterjee, Siddhesh Chaubal, Pritish Kamath:
Faster Algorithms for Alternating Refinement Relations. CoRR abs/1201.4449 (2012) - [i2]Abhisekh Sankaran, Bharat Adsul, Vivek Madan, Pritish Kamath, Supratik Chakraborty:
Preservation under Substructures modulo Bounded Cores. CoRR abs/1205.1358 (2012) - [i1]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. TR12 (2012) - 2011
- [c1]Noël Malod-Dognin, Mathilde Le Boudic-Jamin, Pritish Kamath, Rumen Andonov:
Using Dominances for Solving the Protein Family Identification Problem. WABI 2011: 201-212
Coauthor Index
[j11] [c42] [c41] [c40] [c39] [i50] [i49] [i48] [i47] [i46] [i45] [i44] [i42] [c38] [c37] [c36] [c35] [c34] [c33] [c32] [c31] [c30] [c29] [c28] [i41] [i40] [i39] [i38] [i37] [i36] [i35] [i34] [j8] [c26] [c25] [c24] [i32] [i31] [i30] [i29] [i28] [i27] [i26] [j7] [c14] [c10] [i14] [i12] [c8] [c7] [i10] [i9] [i8] [i7] [i6] [i5]
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