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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
[j33]Pasin Manurangsi:
A note on hardness of computing recursive teaching dimension. Inf. Process. Lett. 183: 106429 (2024)- [j32]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) - [j31]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) - [j30]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) - [c96]Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Pure-DP Aggregation in the Shuffle Model: Error-Optimal and Communication-Efficient. ITC 2024: 4:1-4:13 - [c95]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
On Convex Optimization with Semi-Sensitive Features. COLT 2024: 1916-1938 - [c94]Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang:
LabelDP-Pro: Learning with Label Differential Privacy via Projections. ICLR 2024 - [c93]Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang:
How Private are DP-SGD Implementations? ICML 2024 - [c92]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon:
Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization. ICML 2024 - [c91]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 - [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 - [i121]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) - [i120]Pasin Manurangsi:
Improved Lower Bound for Differentially Private Facility Location. CoRR abs/2403.04874 (2024) - [i119]Pasin Manurangsi:
Improved FPT Approximation Scheme and Approximate Kernel for Biclique-Free Max k-Weight SAT: Greedy Strikes Back. CoRR abs/2403.06335 (2024) - [i118]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) - [i117]Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang:
How Private is DP-SGD? CoRR abs/2403.17673 (2024) - [i116]Badih Ghazi, Cristóbal Guzmán, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Differentially Private Optimization with Sparse Gradients. CoRR abs/2404.10881 (2024) - [i115]Pasin Manurangsi, Warut Suksompong:
Ordinal Maximin Guarantees for Group Fair Division. CoRR abs/2404.11543 (2024) - [i114]Zihan Li, Pasin Manurangsi, Jonathan Scarlett, Warut Suksompong:
Complexity of Round-Robin Allocation with Potentially Noisy Queries. CoRR abs/2404.19402 (2024) - [i113]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) - [i112]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) - [i111]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) - [i110]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon:
On Computing Pairwise Statistics with Local Differential Privacy. CoRR abs/2406.16305 (2024) - [i109]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
On Convex Optimization with Semi-Sensitive Features. CoRR abs/2406.19040 (2024) - [i108]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) - [i107]Ilan Doron-Arad, Ariel Kulik, Pasin Manurangsi:
Fine Grained Lower Bounds for Multidimensional Knapsack. CoRR abs/2407.10146 (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. 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,