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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
- 2024
[j30]Pasin Manurangsi:
A note on hardness of computing recursive teaching dimension. Inf. Process. Lett. 183: 106429 (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) - [c80]Pasin Manurangsi, Warut Suksompong:
Differentially Private Fair Division. AAAI 2023: 5814-5822 - [c79]Badih Ghazi, Junfeng He, Kai Kohlhoff, Ravi Kumar, Pasin Manurangsi, Vidhya Navalpakkam, Nachiappan Valliappan:
Differentially Private Heatmaps. AAAI 2023: 7696-7704 - [c78]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Jelani Nelson, Samson Zhou:
Differentially Private Aggregation via Imperfect Shuffling. ITC 2023: 17:1-17:22 - [c77]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang:
Ticketed Learning-Unlearning Schemes. COLT 2023: 5110-5139 - [c76]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Kewen Wu:
On Differentially Private Counting on Trees. ICALP 2023: 66:1-66:18 - [c75]Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash V. Varadarajan, Chiyuan Zhang:
Regression with Label Differential Privacy. ICLR 2023 - [c74]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
On User-Level Private Convex Optimization. ICML 2023: 11283-11299 - [c73]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thomas Steinke:
Algorithms with More Granular Differential Privacy Guarantees. ITCS 2023: 54:1-54:24 - [c72]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 - [c71]Pasin Manurangsi:
Improved Inapproximability of VC Dimension and Littlestone's Dimension via (Unbalanced) Biclique. ITCS 2023: 85:1-85:18 - [c70]Badih Ghazi
, Ravi Kumar, Pasin Manurangsi:
Privacy in Advertising: Analytics and Modeling. KDD 2023: 5802 - [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 - [i103]Badih Ghazi, Rahul Ilango, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Separating Computational and Statistical Differential Privacy (Under Plausible Assumptions). CoRR abs/2301.00104 (2023) - [i102]Pasin Manurangsi, Erel Segal-Halevi, Warut Suksompong:
On Maximum Bipartite Matching with Separation. CoRR abs/2303.02283 (2023) - [i101]Badih Ghazi, Pritish Kamath, Ravi Kumar, Raghu Meka, Pasin Manurangsi, Chiyuan Zhang:
On User-Level Private Convex Optimization. CoRR abs/2305.04912 (2023) - [i100]Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Pure-DP Aggregation in the Shuffle Model: Error-Optimal and Communication-Efficient. CoRR abs/2305.17634 (2023) - [i99]Badih Ghazi, Xiao Hu, Ravi Kumar, Pasin Manurangsi:
On Differentially Private Sampling from Gaussian and Product Distributions. CoRR abs/2306.12549 (2023) - [i98]Badih Ghazi, Xiao Hu, Ravi Kumar, Pasin Manurangsi:
Differentially Private Data Release over Multiple Tables. CoRR abs/2306.15201 (2023) - [i97]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang:
Ticketed Learning-Unlearning Schemes. CoRR abs/2306.15744 (2023) - [i96]Pasin Manurangsi:
A Note on Hardness of Computing Recursive Teaching Dimension. CoRR abs/2307.09792 (2023) - [i95]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) - [i94]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Jelani Nelson, Samson Zhou:
Differentially Private Aggregation via Imperfect Shuffling. CoRR abs/2308.14733 (2023) - [i93]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) - [i92]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) - [i91]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) - [i90]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) - 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. Theory Comput. 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]Pasin Manurangsi, Warut Suksompong:
Differentially Private Fair Division. CoRR abs/2211.12738 (2022) - [i76]Badih Ghazi, Junfeng He, Kai Kohlhoff, Ravi Kumar, Pasin Manurangsi, Vidhya Navalpakkam, Nachiappan Valliappan:
Differentially Private Heatmaps. CoRR abs/2211.13454 (2022) - [i75]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) - [i74]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Kewen Wu:
On Differentially Private Counting on Trees. CoRR abs/2212.11967 (2022) - [i73]James Bell, Adrià Gascón, Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Mariana Raykova, Phillipp Schoppmann:
Distributed, Private, Sparse Histograms in the Two-Server Model. IACR Cryptol. ePrint Arch. 2022: 920 (2022) - 2021
- [j19]Piotr Faliszewski, Pasin Manurangsi, Krzysztof Sornat:
Approximation and hardness of Shift-Bribery. Artif. Intell. 298: 103520 (2021) - [j18]Pasin Manurangsi:
Linear discrepancy is Π2-hard to approximate. Inf. Process. Lett. 172: 106164 (2021) - [j17]Arnab Bhattacharyya, Édouard Bonnet, László Egri, Suprovat Ghoshal, Karthik C. S., Bingkai Lin, Pasin Manurangsi, Dániel Marx:
Parameterized Intractability of Even Set and Shortest Vector Problem. J. ACM 68(3): 16:1-16:40 (2021) - [j16]Xiaohui Bei, Xinhang Lu, Pasin Manurangsi, Warut Suksompong:
The Price of Fairness for Indivisible Goods. Theory Comput. Syst. 65(7): 1069-1093 (2021) - [j15]Naoyuki Kamiyama, Pasin Manurangsi, Warut Suksompong:
On the complexity of fair house allocation. Oper. Res. Lett. 49(4): 572-577 (2021) - [j14]Pasin Manurangsi, Warut Suksompong:
Closing Gaps in Asymptotic Fair Division. SIAM J. Discret. Math. 35(2): 668-706 (2021) - [j13]Rajesh Chitnis, Andreas Emil Feldmann, Pasin Manurangsi:
Parameterized Approximation Algorithms for Bidirected Steiner Network Problems. ACM Trans. Algorithms 17(2): 12:1-12:68 (2021) - [c52]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thao Nguyen:
Robust and Private Learning of Halfspaces. AISTATS 2021: 1603-1611 - [c51]Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi:
Near-tight closure b ounds for the Littlestone and threshold dimensions. ALT 2021: 686-696 - [c50]Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
On Avoiding the Union Bound When Answering Multiple Differentially Private Queries. COLT 2021: 2133-2146 - [c49]Alisa Chang, Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Locally Private k-Means in One Round. ICML 2021: 1441-1451 - [c48]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Amer Sinha:
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message. ICML 2021: 3692-3701 - [c47]Pasin Manurangsi, Warut Suksompong:
Generalized Kings and Single-Elimination Winners in Random Tournaments. IJCAI 2021: 328-334 - [c46]Pasin Manurangsi, Warut Suksompong:
Almost Envy-Freeness for Groups: Improved Bounds via Discrepancy Theory. IJCAI 2021: 335-341 - [c45]Martin Hoefer, Pasin Manurangsi, Alexandros Psomas:
Algorithmic Persuasion with Evidence. ITCS 2021: 3:1-3:20 - [c44]Pasin Manurangsi, Aviad Rubinstein, Tselil Schramm:
The Strongish Planted Clique Hypothesis and Its Consequences. ITCS 2021: 10:1-10:21 - [c43]Surbhi Goel, Adam R. Klivans, Pasin Manurangsi, Daniel Reichman:
Tight Hardness Results for Training Depth-2 ReLU Networks. ITCS 2021: 22:1-22:14 - [c42]Lijie Chen, Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
On Distributed Differential Privacy and Counting Distinct Elements. ITCS 2021: 56:1-56:18 - [c41]Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
User-Level Differentially Private Learning via Correlated Sampling. NeurIPS 2021: 20172-20184 - [c40]Sreenivas Gollapudi, Guru Guruganesh, Kostas Kollias, Pasin Manurangsi, Renato Paes Leme, Jon Schneider:
Contextual Recommendations and Low-Regret Cutting-Plane Algorithms. NeurIPS 2021: 22498-22508 - [c39]Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang:
Deep Learning with Label Differential Privacy. NeurIPS 2021: 27131-27145 - [c38]Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi:
Sample-efficient proper PAC learning with approximate differential privacy. STOC 2021: 183-196 - [c37]Szymon Dudycz, Pasin Manurangsi, Jan Marcinkowski:
Tight Inapproximability of Minimum Maximal Matching on Bipartite Graphs and Related Problems. WAOA 2021: 48-64 - [i72]Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang:
On Deep Learning with Label Differential Privacy. CoRR abs/2102.06062 (2021) - [i71]Alisa Chang, Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Locally Private k-Means in One Round. CoRR abs/2104.09734 (2021) - [i70]Pasin Manurangsi, Warut Suksompong:
Generalized Kings and Single-Elimination Winners in Random Tournaments. CoRR abs/2105.00193 (2021) - [i69]Pasin Manurangsi, Warut Suksompong:
Almost Envy-Freeness for Groups: Improved Bounds via Discrepancy Theory. CoRR abs/2105.01609 (2021) - [i68]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh:
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead. CoRR abs/2106.04247 (2021) - [i67]Sreenivas Gollapudi, Guru Guruganesh, Kostas Kollias, Pasin Manurangsi, Renato Paes Leme, Jon Schneider:
Contextual Recommendations and Low-Regret Cutting-Plane Algorithms. CoRR abs/2106.04819 (2021) - [i66]Naoyuki Kamiyama, Pasin Manurangsi, Warut Suksompong:
On the Complexity of Fair House Allocation. CoRR abs/2106.06925 (2021) - [i65]Shailesh Bavadekar, Adam Boulanger, John Davis, Damien Desfontaines, Evgeniy Gabrilovich, Krishna Gadepalli, Badih Ghazi, Tague Griffith, Jai Prakash Gupta, Chaitanya Kamath, Dennis Kraft, Ravi Kumar, Akim Kumok, Yael Mayer, Pasin Manurangsi, Arti Patankar, Irippuge Milinda Perera, Chris Scott, Tomer Shekel, Benjamin Miller, Karen Smith, Charlotte Stanton, Mimi Sun, Mark Young, Gregory Wellenius:
Google COVID-19 Vaccination Search Insights: Anonymization Process Description. CoRR abs/2107.01179 (2021) - [i64]Pasin Manurangsi:
Linear Discrepancy is Π2-Hard to Approximate. CoRR abs/2107.01235 (2021) - [i63]Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, Pasin Manurangsi:
Large-Scale Differentially Private BERT. CoRR abs/2108.01624 (2021) - [i62]Edith Elkind, Piotr Faliszewski, Ayumi Igarashi, Pasin Manurangsi, Ulrike Schmidt-Kraepelin, Warut Suksompong:
Justifying Groups in Multiwinner Approval Voting. CoRR abs/2108.12949 (2021) - [i61]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Amer Sinha:
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message. CoRR abs/2109.13158 (2021) - [i60]Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
User-Level Private Learning via Correlated Sampling. CoRR abs/2110.11208 (2021) - [i59]Pasin Manurangsi:
Tight Bounds for Differentially Private Anonymized Histograms. CoRR abs/2111.03257 (2021) - [i58]Pravesh K. Kothari, Pasin Manurangsi, Ameya Velingker:
Private Robust Estimation by Stabilizing Convex Relaxations. CoRR abs/2112.03548 (2021) - [i57]Edith Elkind, Piotr Faliszewski, Ayumi Igarashi, Pasin Manurangsi, Ulrike Schmidt-Kraepelin, Warut Suksompong:
The Price of Justified Representation. CoRR abs/2112.05994 (2021) - [i56]Daniel Alabi, Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Private Rank Aggregation in Central and Local Models. CoRR abs/2112.14652 (2021) - 2020
- [j12]Andreas Emil Feldmann, Karthik C. S., Euiwoong Lee, Pasin Manurangsi:
A Survey on Approximation in Parameterized Complexity: Hardness and Algorithms. Algorithms 13(6): 146 (2020) - [j11]Karthik C. S., Pasin Manurangsi:
On Closest Pair in Euclidean Metric: Monochromatic is as Hard as Bichromatic. Comb. 40(4): 539-573 (2020) - [j10]Parinya Chalermsook, Marek Cygan, Guy Kortsarz, Bundit Laekhanukit, Pasin Manurangsi, Danupon Nanongkai, Luca Trevisan:
From Gap-Exponential Time Hypothesis to Fixed Parameter Tractable Inapproximability: Clique, Dominating Set, and More. SIAM J. Comput. 49(4): 772-810 (2020) - [j9]Noga Alon, Jonathan D. Cohen, Thomas L. Griffiths, Pasin Manurangsi, Daniel Reichman, Igor Shinkar, Tal Wagner, Alexander Y. Ku:
Multitasking Capacity: Hardness Results and Improved Constructions. SIAM J. Discret. Math. 34(1): 885-903 (2020) - [j8]Pasin Manurangsi, Warut Suksompong:
When Do Envy-Free Allocations Exist? SIAM J. Discret. Math. 34(3): 1505-1521 (2020) - [c36]Pasin Manurangsi, Akshayaram Srinivasan, Prashant Nalini Vasudevan:
Nearly Optimal Robust Secret Sharing Against Rushing Adversaries. CRYPTO (3) 2020: 156-185 - [c35]Badih Ghazi, Pasin Manurangsi, Rasmus Pagh, Ameya Velingker:
Private Aggregation from Fewer Anonymous Messages. EUROCRYPT (2) 2020: 798-827 - [c34]Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Ameya Velingker:
Pure Differentially Private Summation from Anonymous Messages. ITC 2020: 15:1-15:23 - [c33]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh:
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead. ICML 2020: 3505-3514 - [c32]Szymon Dudycz, Pasin Manurangsi, Jan Marcinkowski, Krzysztof Sornat:
Tight Approximation for Proportional Approval Voting. IJCAI 2020: 276-282 - [c31]Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi:
The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise. NeurIPS 2020 - [c30]Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Differentially Private Clustering: Tight Approximation Ratios. NeurIPS 2020 - [c29]