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ORCIDPasin Manurangsi
พศิน มนูรังษี
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- affiliation: Google, Mountain View, CA, USA
- affiliation: University of California, Berkeley, USA
- unicode name: พศิน มนูรังษี
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2020 – today
- 2025
[j38]Pasin Manurangsi:
Improved lower bound for differentially private facility location. Inf. Process. Lett. 187: 106502 (2025)- [j37]Alex Berke, Badih Ghazi, Enrico Bacis, Pritish Kamath, Ravi Kumar, Robin Lassonde, Pasin Manurangsi, Umar Syed:
How Unique is Whose Web Browser? The role of demographics in browser fingerprinting among US users. Proc. Priv. Enhancing Technol. 2025(1): 720-758 (2025) - [j36]Pasin Manurangsi:
Improved FPT approximation scheme and approximate kernel for biclique-free max k-weight SAT: Greedy strikes back. Theor. Comput. Sci. 1028: 115033 (2025) - [c102]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Serena Wang:
Differential Privacy on Trust Graphs. ITCS 2025: 53:1-53:23 - 2024
- [j35]Pasin Manurangsi:
A note on hardness of computing recursive teaching dimension. Inf. Process. Lett. 183: 106429 (2024) - [j34]John Delaney, Badih Ghazi, Charlie Harrison, Christina Ilvento, Ravi Kumar, Pasin Manurangsi, Martin Pál, Karthik Prabhakar, Mariana Raykova:
Differentially Private Ad Conversion Measurement. Proc. Priv. Enhancing Technol. 2024(2): 124-140 (2024) - [j33]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) - [j32]Noga Alon, Jonathan D. Cohen, Thomas L. Griffiths, Pasin Manurangsi, Daniel Reichman, Igor Shinkar, Tal Wagner:
Erratum: Multitasking Capacity: Hardness Results and Improved Constructions. SIAM J. Discret. Math. 38(2): 2001-2003 (2024) - [j31]Edith Elkind
, Piotr Faliszewski, Ayumi Igarashi, Pasin Manurangsi, Ulrike Schmidt-Kraepelin, Warut Suksompong:
The Price of Justified Representation. ACM Trans. Economics and Comput. 12(3): 11:1-11:27 (2024) - [j30]Martin Hoefer, Pasin Manurangsi, Alexandros Psomas:
Algorithmic Persuasion with Evidence. ACM Trans. Economics and Comput. 12(4): 12:1-12:34 (2024) - [c101]Lynn Chua, Qiliang Cui, Badih Ghazi, Charlie Harrison, Pritish Kamath, Walid Krichene, Ravi Kumar, Pasin Manurangsi, Nicolas Mayoraz, Hema Venkata Krishna Giri Narra, Steffen Rendle, Amer Sinha, Avinash V. Varadarajan, Chiyuan Zhang:
Training Differentially Private Ad Prediction Models With Semi-Sensitive Features. AdKDD@KDD 2024 - [c100]Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Pure-DP Aggregation in the Shuffle Model: Error-Optimal and Communication-Efficient. ITC 2024: 4:1-4:13 - [c99]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
On Convex Optimization with Semi-Sensitive Features. COLT 2024: 1916-1938 - [c98]Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang:
LabelDP-Pro: Learning with Label Differential Privacy via Projections. ICLR 2024 - [c97]Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang:
How Private are DP-SGD Implementations? ICML 2024 - [c96]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon:
Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization. ICML 2024 - [c95]Pasin Manurangsi, Warut Suksompong:
Ordinal Maximin Guarantees for Group Fair Division. IJCAI 2024: 2922-2930 - [c94]Euiwoong Lee, Pasin Manurangsi:
Hardness of Approximating Bounded-Degree Max 2-CSP and Independent Set on k-Claw-Free Graphs. ITCS 2024: 71:1-71:17 - [c93]Karthik C. S., Euiwoong Lee, Pasin Manurangsi:
On Equivalence of Parameterized Inapproximability of k-Median, k-Max-Coverage, and 2-CSP. IPEC 2024: 6:1-6:18 - [c92]Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang:
Scalable DP-SGD: Shuffling vs. Poisson Subsampling. NeurIPS 2024 - [c91]Badih Ghazi, Cristóbal Guzmán, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Differentially Private Optimization with Sparse Gradients. NeurIPS 2024 - [c90]Zihan Li, Pasin Manurangsi, Jonathan Scarlett, Warut Suksompong:
Complexity of Round-Robin Allocation with Potentially Noisy Queries. SAGT 2024: 520-537 - [c89]Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Privacy in Web Advertising: Analytics and Modeling. WWW (Companion Volume) 2024: 1288-1289 - [i127]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) - [i126]Pasin Manurangsi:
Improved Lower Bound for Differentially Private Facility Location. CoRR abs/2403.04874 (2024) - [i125]Pasin Manurangsi:
Improved FPT Approximation Scheme and Approximate Kernel for Biclique-Free Max k-Weight SAT: Greedy Strikes Back. CoRR abs/2403.06335 (2024) - [i124]John Delaney, Badih Ghazi, Charlie Harrison, Christina Ilvento, Ravi Kumar, Pasin Manurangsi, Martin Pal, Karthik Prabhakar, Mariana Raykova:
Differentially Private Ad Conversion Measurement. CoRR abs/2403.15224 (2024) - [i123]Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang:
How Private is DP-SGD? CoRR abs/2403.17673 (2024) - [i122]Badih Ghazi, Cristóbal Guzmán, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Differentially Private Optimization with Sparse Gradients. CoRR abs/2404.10881 (2024) - [i121]Pasin Manurangsi, Warut Suksompong:
Ordinal Maximin Guarantees for Group Fair Division. CoRR abs/2404.11543 (2024) - [i120]Zihan Li, Pasin Manurangsi, Jonathan Scarlett, Warut Suksompong:
Complexity of Round-Robin Allocation with Potentially Noisy Queries. CoRR abs/2404.19402 (2024) - [i119]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) - [i118]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) - [i117]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) - [i116]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon:
On Computing Pairwise Statistics with Local Differential Privacy. CoRR abs/2406.16305 (2024) - [i115]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
On Convex Optimization with Semi-Sensitive Features. CoRR abs/2406.19040 (2024) - [i114]Karthik C. S., Euiwoong Lee, Pasin Manurangsi:
On Equivalence of Parameterized Inapproximability of k-Median, k-Max-Coverage, and 2-CSP. CoRR abs/2407.08917 (2024) - [i113]Ilan Doron-Arad, Ariel Kulik, Pasin Manurangsi:
Fine Grained Lower Bounds for Multidimensional Knapsack. CoRR abs/2407.10146 (2024) - [i112]Alex Berke, Enrico Bacis, Badih Ghazi, Pritish Kamath, Ravi Kumar, Robin Lassonde, Pasin Manurangsi, Umar Syed:
How Unique is Whose Web Browser? The role of demographics in browser fingerprinting among US users. CoRR abs/2410.06954 (2024) - [i111]Yangsibo Huang, Daogao Liu, Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Milad Nasr, Amer Sinha, Chiyuan Zhang:
Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy. CoRR abs/2410.09591 (2024) - [i110]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Serena Wang:
Differential Privacy on Trust Graphs. CoRR abs/2410.12045 (2024) - [i109]Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang:
Scalable DP-SGD: Shuffling vs. Poisson Subsampling. CoRR abs/2411.04205 (2024) - [i108]Lynn Chua, Badih Ghazi, Charlie Harrison, Ethan Leeman, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang:
Balls-and-Bins Sampling for DP-SGD. CoRR abs/2412.16802 (2024) - [i107]Badih Ghazi, Charlie Harrison, Arpana Hosabettu, Pritish Kamath, Alexander Knop, S. Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Vikas Sahu:
On the Differential Privacy and Interactivity of Privacy Sandbox Reports. CoRR abs/2412.16916 (2024) - [i106]Karthik C. S., Pasin Manurangsi:
On Inapproximability of Reconfiguration Problems: PSPACE-Hardness and some Tight NP-Hardness Results. Electron. Colloquium Comput. Complex. TR24 (2024) - 2023
- [j29]Pasin Manurangsi, Warut Suksompong:
Fixing knockout tournaments with seeds. Discret. Appl. Math. 339: 21-35 (2023) - [j28]Pasin Manurangsi, Erel Segal-Halevi, Warut Suksompong:
On maximum bipartite matching with separation. Inf. Process. Lett. 182: 106388 (2023) - [j27]Edith Elkind, Piotr Faliszewski, Ayumi Igarashi, Pasin Manurangsi, Ulrike Schmidt-Kraepelin, Warut Suksompong:
Justifying groups in multiwinner approval voting. Theor. Comput. Sci. 969: 114039 (2023) - [c88]Pasin Manurangsi, Warut Suksompong:
Differentially Private Fair Division. AAAI 2023: 5814-5822 - [c87]Badih Ghazi, Junfeng He, Kai Kohlhoff, Ravi Kumar, Pasin Manurangsi, Vidhya Navalpakkam, Nachiappan Valliappan:
Differentially Private Heatmaps. AAAI 2023: 7696-7704 - [c86]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 - [c85]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 - [c84]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Jelani Nelson, Samson Zhou:
Differentially Private Aggregation via Imperfect Shuffling. ITC 2023: 17:1-17:22 - [c83]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang:
Ticketed Learning-Unlearning Schemes. COLT 2023: 5110-5139 - [c82]Badih Ghazi, Rahul Ilango, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Towards Separating Computational and Statistical Differential Privacy. FOCS 2023: 580-599 - [c81]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Kewen Wu:
On Differentially Private Counting on Trees. ICALP 2023: 66:1-66:18 - [c80]Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash V. Varadarajan, Chiyuan Zhang:
Regression with Label Differential Privacy. ICLR 2023 - [c79]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
On User-Level Private Convex Optimization. ICML 2023: 11283-11299 - [c78]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thomas Steinke:
Algorithms with More Granular Differential Privacy Guarantees. ITCS 2023: 54:1-54:24 - [c77]Badih Ghazi, Ravi Kumar, Jelani Nelson, Pasin Manurangsi:
Private Counting of Distinct and k-Occurring Items in Time Windows. ITCS 2023: 55:1-55:24 - [c76]Pasin Manurangsi:
Improved Inapproximability of VC Dimension and Littlestone's Dimension via (Unbalanced) Biclique. ITCS 2023: 85:1-85:18 - [c75]Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Privacy in Advertising: Analytics and Modeling. KDD 2023: 5802 - [c74]Badih Ghazi, Xiao Hu, Ravi Kumar, Pasin Manurangsi:
On Differentially Private Sampling from Gaussian and Product Distributions. NeurIPS 2023 - [c73]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 - [c72]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
User-Level Differential Privacy With Few Examples Per User. NeurIPS 2023 - [c71]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon:
On Computing Pairwise Statistics with Local Differential Privacy. NeurIPS 2023 - [c70]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 - [c69]Badih Ghazi, Xiao Hu, Ravi Kumar, Pasin Manurangsi:
Differentially Private Data Release over Multiple Tables. PODS 2023: 207-219 - [c68]Justin Y. Chen, Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Shyam Narayanan, Jelani Nelson, Yinzhan Xu:
Differentially Private All-Pairs Shortest Path Distances: Improved Algorithms and Lower Bounds. SODA 2023: 5040-5067 - [c67]Amir Abboud, Vincent Cohen-Addad, Euiwoong Lee, Pasin Manurangsi:
On the Fine-Grained Complexity of Approximating k-Center in Sparse Graphs. SOSA 2023: 145-155 - [i105]Badih Ghazi, Rahul Ilango, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Separating Computational and Statistical Differential Privacy (Under Plausible Assumptions). CoRR abs/2301.00104 (2023) - [i104]Pasin Manurangsi, Erel Segal-Halevi, Warut Suksompong:
On Maximum Bipartite Matching with Separation. CoRR abs/2303.02283 (2023) - [i103]Badih Ghazi, Pritish Kamath, Ravi Kumar, Raghu Meka, Pasin Manurangsi, Chiyuan Zhang:
On User-Level Private Convex Optimization. CoRR abs/2305.04912 (2023) - [i102]Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Pure-DP Aggregation in the Shuffle Model: Error-Optimal and Communication-Efficient. CoRR abs/2305.17634 (2023) - [i101]Badih Ghazi, Xiao Hu, Ravi Kumar, Pasin Manurangsi:
On Differentially Private Sampling from Gaussian and Product Distributions. CoRR abs/2306.12549 (2023) - [i100]Badih Ghazi, Xiao Hu, Ravi Kumar, Pasin Manurangsi:
Differentially Private Data Release over Multiple Tables. CoRR abs/2306.15201 (2023) - [i99]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang:
Ticketed Learning-Unlearning Schemes. CoRR abs/2306.15744 (2023) - [i98]Pasin Manurangsi:
A Note on Hardness of Computing Recursive Teaching Dimension. CoRR abs/2307.09792 (2023) - [i97]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) - [i96]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Jelani Nelson, Samson Zhou:
Differentially Private Aggregation via Imperfect Shuffling. CoRR abs/2308.14733 (2023) - [i95]Euiwoong Lee, Pasin Manurangsi:
Hardness of Approximating Bounded-Degree Max 2-CSP and Independent Set on k-Claw-Free Graphs. CoRR abs/2309.04099 (2023) - [i94]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) - [i93]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) - [i92]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) - [i91]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) - [i90]Karthik C. S., Pasin Manurangsi:
On Inapproximability of Reconfiguration Problems: PSPACE-Hardness and some Tight NP-Hardness Results. CoRR abs/2312.17140 (2023) - 2022
- [j26]Pasin Manurangsi, Warut Suksompong:
Generalized kings and single-elimination winners in random tournaments. Auton. Agents Multi Agent Syst. 36(1): 28 (2022) - [j25]Paul W. Goldberg, Alexandros Hollender, Ayumi Igarashi, Pasin Manurangsi, Warut Suksompong:
Consensus Halving for Sets of Items. Math. Oper. Res. 47(4): 3357-3379 (2022) - [j24]Badih Ghazi, Ben Kreuter, Ravi Kumar, Pasin Manurangsi, Jiayu Peng, Evgeny Skvortsov, Yao Wang, Craig Wright:
Multiparty Reach and Frequency Histogram: Private, Secure, and Practical. Proc. Priv. Enhancing Technol. 2022(1): 373-395 (2022) - [j23]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) - [j22]Badih Ghazi, Neel Kamal, Ravi Kumar, Pasin Manurangsi, Annika Zhang:
Private Aggregation of Trajectories. Proc. Priv. Enhancing Technol. 2022(4): 626-644 (2022) - [j21]Pasin Manurangsi, Warut Suksompong:
Almost envy-freeness for groups: Improved bounds via discrepancy theory. Theor. Comput. Sci. 930: 179-195 (2022) - [j20]Pasin Manurangsi, Preetum Nakkiran, Luca Trevisan:
Near-Optimal NP-Hardness of Approximating Max k-CSPR. Adv. Math. Commun. 18: 1-29 (2022) - [c66]Edith Elkind, Piotr Faliszewski, Ayumi Igarashi, Pasin Manurangsi, Ulrike Schmidt-Kraepelin, Warut Suksompong:
The Price of Justified Representation. AAAI 2022: 4983-4990 - [c65]Daniel Alabi, Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Private Rank Aggregation in Central and Local Models. AAAI 2022: 5984-5991 - [c64]Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi, Lisheng Ren:
Hardness of Learning a Single Neuron with Adversarial Label Noise. AISTATS 2022: 8199-8213 - [c63]James Bell, Adrià Gascón, Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Mariana Raykova, Phillipp Schoppmann:
Distributed, Private, Sparse Histograms in the Two-Server Model. CCS 2022: 307-321 - [c62]Pravesh Kothari, Pasin Manurangsi, Ameya Velingker:
Private Robust Estimation by Stabilizing Convex Relaxations. COLT 2022: 723-777 - [c61]Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, Pasin Manurangsi:
Large-Scale Differentially Private BERT. EMNLP (Findings) 2022: 6481-6491 - [c60]Amir Abboud, Vincent Cohen-Addad, Euiwoong Lee, Pasin Manurangsi:
Improved Approximation Algorithms and Lower Bounds for Search-Diversification Problems. ICALP 2022: 7:1-7:18 - [c59]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Faster Privacy Accounting via Evolving Discretization. ICML 2022: 7470-7483 - [c58]Pasin Manurangsi, Warut Suksompong:
Fixing Knockout Tournaments With Seeds. IJCAI 2022: 412-418 - [c57]Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi, Lisheng Ren:
Cryptographic Hardness of Learning Halfspaces with Massart Noise. NeurIPS 2022 - [c56]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Anonymized Histograms in Intermediate Privacy Models. NeurIPS 2022 - [c55]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Private Isotonic Regression. NeurIPS 2022 - [c54]Edith Elkind, Piotr Faliszewski, Ayumi Igarashi, Pasin Manurangsi, Ulrike Schmidt-Kraepelin, Warut Suksompong:
Justifying Groups in Multiwinner Approval Voting. SAGT 2022: 472-489 - [c53]Pasin Manurangsi:
Tight Bounds for Differentially Private Anonymized Histograms. SOSA 2022: 203-213 - [i89]Amir Abboud, Vincent Cohen-Addad, Euiwoong Lee, Pasin Manurangsi:
Improved Approximation Algorithms and Lower Bounds for Search-Diversification Problems. CoRR abs/2203.01857 (2022) - [i88]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Jelani Nelson:
Differentially Private All-Pairs Shortest Path Distances: Improved Algorithms and Lower Bounds. CoRR abs/2203.16476 (2022) - [i87]Pasin Manurangsi, Warut Suksompong:
Fixing Knockout Tournaments With Seeds. CoRR abs/2204.11171 (2022) - [i86]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) - [i85]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Faster Privacy Accounting via Evolving Discretization. CoRR abs/2207.04381 (2022) - [i84]Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi, Lisheng Ren:
Cryptographic Hardness of Learning Halfspaces with Massart Noise. CoRR abs/2207.14266 (2022) - [i83]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thomas Steinke:
Algorithms with More Granular Differential Privacy Guarantees. CoRR abs/2209.04053 (2022) - [i82]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Private Isotonic Regression. CoRR abs/2210.15175 (2022) - [i81]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Anonymized Histograms in Intermediate Privacy Models. CoRR abs/2210.15178 (2022) - [i80]Pasin Manurangsi:
Improved Inapproximability of VC Dimension and Littlestone's Dimension via (Unbalanced) Biclique. CoRR abs/2211.01443 (2022) - [i79]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Jelani Nelson:
Private Counting of Distinct and k-Occurring Items in Time Windows. CoRR abs/2211.11718 (2022) - [i78]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) - [i77]