Improved Approximation Algorithms for Projection Games - (Extended Abstract). - Pasin Manurangsi; Dana Moshkovitz (ESA, 2013)

Approximating Dense Max 2-CSPs. - Pasin Manurangsi; Dana Moshkovitz (APPROX-RANDOM, 2015)

Dissection with the Fewest Pieces is Hard, Even to Approximate. - Jeffrey Bosboom; Erik D. Demaine; Martin L. Demaine; Jayson Lynch; Pasin Manurangsi; Mikhail Rudoy; Anak Yodpinyanee (JCDCGG, 2015)

Near-Optimal UGC-hardness of Approximating Max k-CSP_R. - Pasin Manurangsi; Preetum Nakkiran; Luca Trevisan 001 (APPROX-RANDOM, 2016)

Almost-Polynomial Ratio ETH-Hardness of Approximating Densest k-Subgraph. - Pasin Manurangsi (Electron. Colloquium Comput. Complex., 2016)

Inapproximability of VC Dimension and Littlestone's Dimension. - Pasin Manurangsi; Aviad Rubinstein (COLT, 2017)

From Gap-ETH to FPT-Inapproximability: Clique, Dominating Set, and More. - Parinya Chalermsook; Marek Cygan; Guy Kortsarz; Bundit Laekhanukit; Pasin Manurangsi; Danupon Nanongkai; Luca Trevisan 001 (FOCS, 2017)

Inapproximability of Maximum Edge Biclique, Maximum Balanced Biclique and Minimum k-Cut from the Small Set Expansion Hypothesis. - Pasin Manurangsi (ICALP, 2017)

A Birthday Repetition Theorem and Complexity of Approximating Dense CSPs. - Pasin Manurangsi; Prasad Raghavendra (ICALP, 2017)

Computing an Approximately Optimal Agreeable Set of Items. - Pasin Manurangsi; Warut Suksompong (IJCAI, 2017)

An Improved Integrality Gap for the Călinescu-Karloff-Rabani Relaxation for Multiway Cut. - Haris Angelidakis; Yury Makarychev; Pasin Manurangsi (IPCO, 2017)

Approximation Algorithms for Label Cover and The Log-Density Threshold. - Eden Chlamtác; Pasin Manurangsi; Dana Moshkovitz; Aravindan Vijayaraghavan (SODA, 2017)

Improved Approximation Algorithms for Projection Games. - Pasin Manurangsi; Dana Moshkovitz (Algorithmica, 2017)

Even 1×n Edge-Matching and Jigsaw Puzzles are Really Hard. - Jeffrey Bosboom; Erik D. Demaine; Martin L. Demaine; Adam Hesterberg; Pasin Manurangsi; Anak Yodpinyanee (CoRR, 2017)

Parameterized Approximation Algorithms for Directed Steiner Network Problems. - Rajesh Chitnis; Andreas Emil Feldmann; Pasin Manurangsi (CoRR, 2017)

Inapproximability of Maximum Biclique Problems, Minimum $k$-Cut and Densest At-Least-$k$-Subgraph from the Small Set Expansion Hypothesis. - Pasin Manurangsi (CoRR, 2017)

On the Parameterized Complexity of Approximating Dominating Set. - Karthik C. S. 001; Bundit Laekhanukit; Pasin Manurangsi (Electron. Colloquium Comput. Complex., 2017)

Even 1 × n Edge-Matching and Jigsaw Puzzles are Really Hard. - Jeffrey Bosboom; Erik D. Demaine; Martin L. Demaine; Adam Hesterberg; Pasin Manurangsi; Anak Yodpinyanee (J. Inf. Process., 2017)

Asymptotic existence of fair divisions for groups. - Pasin Manurangsi; Warut Suksompong (Math. Soc. Sci., 2017)

Sherali-Adams Integrality Gaps Matching the Log-Density Threshold. - Eden Chlamtác; Pasin Manurangsi (APPROX-RANDOM, 2018)

Mildly Exponential Time Approximation Algorithms for Vertex Cover, Balanced Separator and Uniform Sparsest Cut. - Pasin Manurangsi; Luca Trevisan 001 (APPROX-RANDOM, 2018)

Average Whenever You Meet: Opportunistic Protocols for Community Detection. - Luca Becchetti; Andrea Clementi; Pasin Manurangsi; Emanuele Natale; Francesco Pasquale; Prasad Raghavendra; Luca Trevisan 001 (ESA, 2018)

Inapproximability of Maximum Biclique Problems, Minimum k-Cut and Densest At-Least-k-Subgraph from the Small Set Expansion Hypothesis. - Pasin Manurangsi (Algorithms, 2018)

Mildly Exponential Time Approximation Algorithms for Vertex Cover, Uniform Sparsest Cut and Related Problems. - Pasin Manurangsi; Luca Trevisan 001 (CoRR, 2018)

The Computational Complexity of Training ReLU(s). - Pasin Manurangsi; Daniel Reichman 001 (CoRR, 2018)

Parameterized Intractability of Even Set and Shortest Vector Problem from Gap-ETH. - Arnab Bhattacharyya 001; Suprovat Ghoshal; Karthik C. S. 001; Pasin Manurangsi (Electron. Colloquium Comput. Complex., 2018)

ETH-Hardness of Approximating 2-CSPs and Directed Steiner Network. - Irit Dinur; Pasin Manurangsi (Electron. Colloquium Comput. Complex., 2018)

On Closest Pair in Euclidean Metric: Monochromatic is as Hard as Bichromatic. - Karthik C. S. 001; Pasin Manurangsi (Electron. Colloquium Comput. Complex., 2018)

Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin. - Ilias Diakonikolas; Daniel Kane 001; Pasin Manurangsi (NeurIPS, 2019)

Losing Treewidth by Separating Subsets. - Anupam Gupta 001; Euiwoong Lee; Jason Li 006; Pasin Manurangsi; Michal Wlodarczyk 001 (SODA, 2019)

A Note on Max k-Vertex Cover: Faster FPT-AS, Smaller Approximate Kernel and Improved Approximation. - Pasin Manurangsi (SOSA, 2019)

Computing a small agreeable set of indivisible items. - Pasin Manurangsi; Warut Suksompong (Artif. Intell., 2019)

Tight Running Time Lower Bounds for Strong Inapproximability of Maximum k-Coverage, Unique Set Cover and Related Problems (via t-Wise Agreement Testing Theorem). - Pasin Manurangsi (CoRR, 2019)

Parameterized Intractability of Even Set and Shortest Vector Problem. - Arnab Bhattacharyya 001; Édouard Bonnet; László Egri; Suprovat Ghoshal; Karthik C. S. 001; Bingkai Lin; Pasin Manurangsi; Dániel Marx (Electron. Colloquium Comput. Complex., 2019)

Nearly Optimal Robust Secret Sharing against Rushing Adversaries. - Pasin Manurangsi; Akshayaram Srinivasan; Prashant Nalini Vasudevan (IACR Cryptol. ePrint Arch., 2019)

A note on degree vs gap of Min-Rep Label Cover and improved inapproximability for connectivity problems. - Pasin Manurangsi (Inf. Process. Lett., 2019)

Approximation and Hardness: Beyond P and NP. - Pasin Manurangsi (2019)

Private Aggregation from Fewer Anonymous Messages. - Badih Ghazi; Pasin Manurangsi; Rasmus Pagh; Ameya Velingker (EUROCRYPT (2), 2020)

Pure Differentially Private Summation from Anonymous Messages. - Badih Ghazi; Noah Golowich; Ravi Kumar 001; Pasin Manurangsi; Rasmus Pagh; Ameya Velingker (ITC, 2020)

Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi; Rasmus Pagh (ICML, 2020)

Tight Approximation for Proportional Approval Voting. - Szymon Dudycz; Pasin Manurangsi; Jan Marcinkowski; Krzysztof Sornat (IJCAI, 2020)

The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise. - Ilias Diakonikolas; Daniel M. Kane; Pasin Manurangsi (NeurIPS, 2020)

Differentially Private Clustering: Tight Approximation Ratios. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi (NeurIPS, 2020)

Tight Running Time Lower Bounds for Strong Inapproximability of Maximum k-Coverage, Unique Set Cover and Related Problems (via t-Wise Agreement Testing Theorem). - Pasin Manurangsi (SODA, 2020)

To Close Is Easier Than To Open: Dual Parameterization To k-Median. - Jaroslaw Byrka; Szymon Dudycz; Pasin Manurangsi; Jan Marcinkowski; Michal Wlodarczyk 001 (WAOA, 2020)

A Survey on Approximation in Parameterized Complexity: Hardness and Algorithms. - Andreas Emil Feldmann; Karthik C. S. 001; Euiwoong Lee; Pasin Manurangsi (Algorithms, 2020)

Near-tight closure bounds for Littlestone and threshold dimensions. - Badih Ghazi; Noah Golowich; Ravi Kumar 001; Pasin Manurangsi (CoRR, 2020)

From Gap-Exponential Time Hypothesis to Fixed Parameter Tractable Inapproximability: Clique, Dominating Set, and More. - Parinya Chalermsook; Marek Cygan; Guy Kortsarz; Bundit Laekhanukit; Pasin Manurangsi; Danupon Nanongkai; Luca Trevisan 001 (SIAM J. Comput., 2020)

Multitasking Capacity: Hardness Results and Improved Constructions. - Noga Alon; Jonathan D. Cohen 003; Thomas L. Griffiths 001; Pasin Manurangsi; Daniel Reichman 001; Igor Shinkar; Tal Wagner; Alexander Y. Ku (SIAM J. Discret. Math., 2020)

When Do Envy-Free Allocations Exist? - Pasin Manurangsi; Warut Suksompong (SIAM J. Discret. Math., 2020)

Robust and Private Learning of Halfspaces. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi; Thao Nguyen (AISTATS, 2021)

Near-tight closure b ounds for the Littlestone and threshold dimensions. - Badih Ghazi; Noah Golowich; Ravi Kumar 001; Pasin Manurangsi (ALT, 2021)

On Avoiding the Union Bound When Answering Multiple Differentially Private Queries. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi (COLT, 2021)

Locally Private k-Means in One Round. - Alisa Chang; Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi (ICML, 2021)

Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi; Rasmus Pagh; Amer Sinha (ICML, 2021)

On Distributed Differential Privacy and Counting Distinct Elements. - Lijie Chen 001; Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi (ITCS, 2021)

Tight Hardness Results for Training Depth-2 ReLU Networks. - Surbhi Goel; Adam R. Klivans; Pasin Manurangsi; Daniel Reichman 001 (ITCS, 2021)

The Strongish Planted Clique Hypothesis and Its Consequences. - Pasin Manurangsi; Aviad Rubinstein; Tselil Schramm (ITCS, 2021)

Deep Learning with Label Differential Privacy. - Badih Ghazi; Noah Golowich; Ravi Kumar 001; Pasin Manurangsi; Chiyuan Zhang (NeurIPS, 2021)

User-Level Differentially Private Learning via Correlated Sampling. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi (NeurIPS, 2021)

Contextual Recommendations and Low-Regret Cutting-Plane Algorithms. - Sreenivas Gollapudi; Guru Guruganesh; Kostas Kollias; Pasin Manurangsi; Renato Paes Leme; Jon Schneider (NeurIPS, 2021)

Sample-efficient proper PAC learning with approximate differential privacy. - Badih Ghazi; Noah Golowich; Ravi Kumar 001; Pasin Manurangsi (STOC, 2021)

Tight Inapproximability of Minimum Maximal Matching on Bipartite Graphs and Related Problems. - Szymon Dudycz; Pasin Manurangsi; Jan Marcinkowski (WAOA, 2021)

Approximation and hardness of Shift-Bribery. - Piotr Faliszewski; Pasin Manurangsi; Krzysztof Sornat (Artif. Intell., 2021)

Google COVID-19 Vaccination Search Insights: Anonymization Process Description. - Shailesh Bavadekar; Adam Boulanger; John Davis; Damien Desfontaines; Evgeniy Gabrilovich; Krishna Gadepalli; Badih Ghazi; Tague Griffith; Jai Prakash Gupta 001; Chaitanya Kamath; Dennis Kraft; Ravi Kumar 001; et al. (CoRR, 2021)

User-Level Private Learning via Correlated Sampling. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi (CoRR, 2021)

Linear discrepancy is Π2-hard to approximate. - Pasin Manurangsi (Inf. Process. Lett., 2021)

The Price of Fairness for Indivisible Goods. - Xiaohui Bei; Xinhang Lu; Pasin Manurangsi; Warut Suksompong (Theory Comput. Syst., 2021)

On the complexity of fair house allocation. - Naoyuki Kamiyama; Pasin Manurangsi; Warut Suksompong (Oper. Res. Lett., 2021)

Closing Gaps in Asymptotic Fair Division. - Pasin Manurangsi; Warut Suksompong (SIAM J. Discret. Math., 2021)

Parameterized Approximation Algorithms for Bidirected Steiner Network Problems. - Rajesh Chitnis; Andreas Emil Feldmann; Pasin Manurangsi (ACM Trans. Algorithms, 2021)

Private Rank Aggregation in Central and Local Models. - Daniel Alabi; Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi (AAAI, 2022)

Hardness of Learning a Single Neuron with Adversarial Label Noise. - Ilias Diakonikolas; Daniel Kane 001; Pasin Manurangsi; Lisheng Ren (AISTATS, 2022)

Private Robust Estimation by Stabilizing Convex Relaxations. - Pravesh Kothari; Pasin Manurangsi; Ameya Velingker (COLT, 2022)

Large-Scale Differentially Private BERT. - Rohan Anil; Badih Ghazi; Vineet Gupta 001; Ravi Kumar 001; Pasin Manurangsi (EMNLP (Findings), 2022)

Improved Approximation Algorithms and Lower Bounds for Search-Diversification Problems. - Amir Abboud; Vincent Cohen-Addad; Euiwoong Lee; Pasin Manurangsi (ICALP, 2022)

Faster Privacy Accounting via Evolving Discretization. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi (ICML, 2022)

Cryptographic Hardness of Learning Halfspaces with Massart Noise. - Ilias Diakonikolas; Daniel Kane 001; Pasin Manurangsi; Lisheng Ren (NeurIPS, 2022)

Anonymized Histograms in Intermediate Privacy Models. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi (NeurIPS, 2022)

Private Isotonic Regression. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi (NeurIPS, 2022)

Tight Bounds for Differentially Private Anonymized Histograms. - Pasin Manurangsi (SOSA, 2022)

Generalized kings and single-elimination winners in random tournaments. - Pasin Manurangsi; Warut Suksompong (Auton. Agents Multi Agent Syst., 2022)

Differentially Private All-Pairs Shortest Path Distances: Improved Algorithms and Lower Bounds. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi; Jelani Nelson (CoRR, 2022)

Distributed, Private, Sparse Histograms in the Two-Server Model. - James Bell 001; Adrià Gascón; Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi; Mariana Raykova 001; Phillipp Schoppmann (IACR Cryptol. ePrint Arch., 2022)

Connect the Dots: Tighter Discrete Approximations of Privacy Loss Distributions. - Vadym Doroshenko; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi (Proc. Priv. Enhancing Technol., 2022)

Multiparty Reach and Frequency Histogram: Private, Secure, and Practical. - Badih Ghazi; Ben Kreuter; Ravi Kumar 001; Pasin Manurangsi; Jiayu Peng; Evgeny Skvortsov; Yao Wang; Craig Wright (Proc. Priv. Enhancing Technol., 2022)

Private Aggregation of Trajectories. - Badih Ghazi; Neel Kamal; Ravi Kumar 001; Pasin Manurangsi; Annika Zhang (Proc. Priv. Enhancing Technol., 2022)

Almost envy-freeness for groups: Improved bounds via discrepancy theory. - Pasin Manurangsi; Warut Suksompong (Theor. Comput. Sci., 2022)

Near-Optimal NP-Hardness of Approximating Max k-CSPR. - Pasin Manurangsi; Preetum Nakkiran; Luca Trevisan 001 (Adv. Math. Commun., 2022)

Consensus Halving for Sets of Items. - Paul W. Goldberg; Alexandros Hollender; Ayumi Igarashi 001; Pasin Manurangsi; Warut Suksompong (Math. Oper. Res., 2022)

Differentially Private Heatmaps. - Badih Ghazi; Junfeng He; Kai Kohlhoff; Ravi Kumar 001; Pasin Manurangsi; Vidhya Navalpakkam; Nachiappan Valliappan (AAAI, 2023)

Optimizing Hierarchical Queries for the Attribution Reporting API. - Matthew Dawson; Badih Ghazi; Pritish Kamath; Kapil Kumar; Ravi Kumar 001; Bo Luan; Pasin Manurangsi; Nishanth Mundru; Harikesh Nair; Adam Sealfon; Shengyu Zhu 004 (AdKDD@KDD, 2023)

Private Ad Modeling with DP-SGD. - Carson Denison; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Krishna Giri Narra; Amer Sinha; Avinash V. Varadarajan; Chiyuan Zhang (AdKDD@KDD, 2023)

Differentially Private Aggregation via Imperfect Shuffling. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi; Jelani Nelson; Samson Zhou (ITC, 2023)

Ticketed Learning-Unlearning Schemes. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Ayush Sekhari; Chiyuan Zhang (COLT, 2023)

Towards Separating Computational and Statistical Differential Privacy. - Badih Ghazi; Rahul Ilango; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi (FOCS, 2023)

On Differentially Private Counting on Trees. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Kewen Wu 001 (ICALP, 2023)

Regression with Label Differential Privacy. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Ethan Leeman; Pasin Manurangsi; Avinash V. Varadarajan; Chiyuan Zhang (ICLR, 2023)

On User-Level Private Convex Optimization. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Raghu Meka; Chiyuan Zhang (ICML, 2023)

Algorithms with More Granular Differential Privacy Guarantees. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi; Thomas Steinke 002 (ITCS, 2023)

Private Counting of Distinct and k-Occurring Items in Time Windows. - Badih Ghazi; Ravi Kumar 001; Jelani Nelson; Pasin Manurangsi (ITCS, 2023)

Improved Inapproximability of VC Dimension and Littlestone's Dimension via (Unbalanced) Biclique. - Pasin Manurangsi (ITCS, 2023)

Privacy in Advertising: Analytics and Modeling. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi (KDD, 2023)

On Differentially Private Sampling from Gaussian and Product Distributions. - Badih Ghazi; Xiao Hu 005; Ravi Kumar 001; Pasin Manurangsi (NeurIPS, 2023)

Sparsity-Preserving Differentially Private Training of Large Embedding Models. - Badih Ghazi; Yangsibo Huang; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (NeurIPS, 2023)

User-Level Differential Privacy With Few Examples Per User. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Raghu Meka; Chiyuan Zhang (NeurIPS, 2023)

On Computing Pairwise Statistics with Local Differential Privacy. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Adam Sealfon (NeurIPS, 2023)

Optimal Unbiased Randomizers for Regression with Label Differential Privacy. - Ashwinkumar Badanidiyuru Varadaraja; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Ethan Leeman; Pasin Manurangsi; Avinash V. Varadarajan; Chiyuan Zhang (NeurIPS, 2023)

Differentially Private Data Release over Multiple Tables. - Badih Ghazi; Xiao Hu 005; Ravi Kumar 001; Pasin Manurangsi (PODS, 2023)

Differentially Private All-Pairs Shortest Path Distances: Improved Algorithms and Lower Bounds. - Justin Y. Chen; Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi; Shyam Narayanan; Jelani Nelson; Yinzhan Xu (SODA, 2023)

On the Fine-Grained Complexity of Approximating k-Center in Sparse Graphs. - Amir Abboud; Vincent Cohen-Addad; Euiwoong Lee; Pasin Manurangsi (SOSA, 2023)

Separating Computational and Statistical Differential Privacy (Under Plausible Assumptions). - Badih Ghazi; Rahul Ilango; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi (CoRR, 2023)

On maximum bipartite matching with separation. - Pasin Manurangsi; Erel Segal-Halevi; Warut Suksompong (Inf. Process. Lett., 2023)

Justifying groups in multiwinner approval voting. - Edith Elkind; Piotr Faliszewski; Ayumi Igarashi 001; Pasin Manurangsi; Ulrike Schmidt-Kraepelin; Warut Suksompong (Theor. Comput. Sci., 2023)

Fixing knockout tournaments with seeds. - Pasin Manurangsi; Warut Suksompong (Discret. Appl. Math., 2023)

Training Differentially Private Ad Prediction Models With Semi-Sensitive Features. - Lynn Chua; Qiliang Cui; Badih Ghazi; Charlie Harrison; Pritish Kamath; Walid Krichene; Ravi Kumar 001; Pasin Manurangsi; Nicolas Mayoraz; Hema Venkata Krishna Giri Narra; Steffen Rendle; Amer Sinha; et al. (AdKDD@KDD, 2024)

Pure-DP Aggregation in the Shuffle Model: Error-Optimal and Communication-Efficient. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi (ITC, 2024)

On Convex Optimization with Semi-Sensitive Features. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Raghu Meka; Chiyuan Zhang (COLT, 2024)

LabelDP-Pro: Learning with Label Differential Privacy via Projections. - Badih Ghazi; Yangsibo Huang; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Chiyuan Zhang (ICLR, 2024)

How Private are DP-SGD Implementations? - Lynn Chua; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (ICML, 2024)

Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Adam Sealfon (ICML, 2024)

Hardness of Approximating Bounded-Degree Max 2-CSP and Independent Set on k-Claw-Free Graphs. - Euiwoong Lee; Pasin Manurangsi (ITCS, 2024)

On Equivalence of Parameterized Inapproximability of k-Median, k-Max-Coverage, and 2-CSP. - Karthik C. S. 001; Euiwoong Lee; Pasin Manurangsi (IPEC, 2024)

Scalable DP-SGD: Shuffling vs. Poisson Subsampling. - Lynn Chua; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (NeurIPS, 2024)

Differentially Private Optimization with Sparse Gradients. - Badih Ghazi; Cristóbal Guzmán; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi (NeurIPS, 2024)

Privacy in Web Advertising: Analytics and Modeling. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi (WWW (Companion Volume), 2024)

Training Differentially Private Ad Prediction Models with Semi-Sensitive Features. - Lynn Chua; Qiliang Cui; Badih Ghazi; Charlie Harrison; Pritish Kamath; Walid Krichene; Ravi Kumar 001; Pasin Manurangsi; Krishna Giri Narra; Amer Sinha; Avinash V. Varadarajan; Chiyuan Zhang (CoRR, 2024)

How Private is DP-SGD? - Lynn Chua; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (CoRR, 2024)

Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning. - Lynn Chua; Badih Ghazi; Yangsibo Huang; Pritish Kamath; Daogao Liu; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (CoRR, 2024)

Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models. - Lynn Chua; Badih Ghazi; Yangsibo Huang; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Amer Sinha; Chulin Xie; Chiyuan Zhang (CoRR, 2024)

Fine Grained Lower Bounds for Multidimensional Knapsack. - Ilan Doron-Arad; Ariel Kulik; Pasin Manurangsi (CoRR, 2024)

On the Differential Privacy and Interactivity of Privacy Sandbox Reports. - Badih Ghazi; Charlie Harrison; Arpana Hosabettu; Pritish Kamath; Alexander Knop; S. Ravi Kumar 001; Ethan Leeman; Pasin Manurangsi; Vikas Sahu (CoRR, 2024)

On Inapproximability of Reconfiguration Problems: PSPACE-Hardness and some Tight NP-Hardness Results. - Karthik C. S. 001; Pasin Manurangsi (Electron. Colloquium Comput. Complex., 2024)

Summary Reports Optimization in the Privacy Sandbox Attribution Reporting API. - Hidayet Aksu; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Adam Sealfon; Avinash V. Varadarajan (Proc. Priv. Enhancing Technol., 2024)

Differentially Private Ad Conversion Measurement. - John Delaney; Badih Ghazi; Charlie Harrison; Christina Ilvento; Ravi Kumar 001; Pasin Manurangsi; Martin Pál; Karthik Prabhakar; Mariana Raykova 001 (Proc. Priv. Enhancing Technol., 2024)

Erratum: Multitasking Capacity: Hardness Results and Improved Constructions. - Noga Alon; Jonathan D. Cohen 003; Thomas L. Griffiths 001; Pasin Manurangsi; Daniel Reichman 001; Igor Shinkar; Tal Wagner (SIAM J. Discret. Math., 2024)

A note on hardness of computing recursive teaching dimension. - Pasin Manurangsi (Inf. Process. Lett., 2024)

The Price of Justified Representation. - Edith Elkind; Piotr Faliszewski; Ayumi Igarashi 001; Pasin Manurangsi; Ulrike Schmidt-Kraepelin; Warut Suksompong (ACM Trans. Economics and Comput., 2024)

Algorithmic Persuasion with Evidence. - Martin Hoefer 001; Pasin Manurangsi; Alexandros Psomas 001 (ACM Trans. Economics and Comput., 2024)

Balls-and-Bins Sampling for DP-SGD. - Lynn Chua; Badih Ghazi; Charlie Harrison; Pritish Kamath; Ravi Kumar 001; Ethan Leeman; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (AISTATS, 2025)

PREM: Privately Answering Statistical Queries with Relative Error. - Badih Ghazi; Cristóbal Guzmán; Pritish Kamath; Alexander Knop; Ravi Kumar 001; Pasin Manurangsi; Sushant Sachdeva (COLT, 2025)

Infinitely Divisible Noise for Differential Privacy: Nearly Optimal Error in the High ε Regime. - Charlie Harrison; Pasin Manurangsi (FORC, 2025)

Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy. - Yangsibo Huang; Daogao Liu; Lynn Chua; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Milad Nasr; Amer Sinha; Chiyuan Zhang (ICLR, 2025)

Differential Privacy on Trust Graphs. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi; Serena Wang 001 (ITCS, 2025)

Differentially private fair division. - Pasin Manurangsi; Warut Suksompong (Artif. Intell., 2025)

Complexity of round-robin allocation with potentially noisy queries. - Zihan Li; Pasin Manurangsi; Jonathan Scarlett; Warut Suksompong (Inf. Comput., 2025)

Improved lower bound for differentially private facility location. - Pasin Manurangsi (Inf. Process. Lett., 2025)

How Unique is Whose Web Browser? The role of demographics in browser fingerprinting among US users. - Alex Berke; Badih Ghazi; Enrico Bacis; Pritish Kamath; Ravi Kumar 001; Robin Lassonde; Pasin Manurangsi; Umar Syed (Proc. Priv. Enhancing Technol., 2025)

On the Differential Privacy and Interactivity of Privacy Sandbox Reports. - Badih Ghazi; Charlie Harrison; Arpana Hosabettu; Pritish Kamath; Alexander Knop; Ravi Kumar 001; Ethan Leeman; Pasin Manurangsi; Mariana Raykova 001; Vikas Sahu; Phillipp Schoppmann (Proc. Priv. Enhancing Technol., 2025)

Improved FPT approximation scheme and approximate kernel for biclique-free max k-weight SAT: Greedy strikes back. - Pasin Manurangsi (Theor. Comput. Sci., 2025)

Ordinal maximin guarantees for group fair division. - Pasin Manurangsi; Warut Suksompong (Theor. Comput. Sci., 2025)

Linear-Time User-Level DP-SCO via Robust Statistics. - Badih Ghazi; Ravi Kumar 001; Daogao Liu; Pasin Manurangsi (CoRR, 2025)

Tight Lower Bound for Multicolor Discrepancy. - Pasin Manurangsi; Raghu Meka (CoRR, 2025)

Asymptotic Fair Division: Chores Are Easier Than Goods. - Pasin Manurangsi; Warut Suksompong (CoRR, 2025)

Asymptotic Analysis of Weighted Fair Division. - Pasin Manurangsi; Warut Suksompong; Tomohiko Yokoyama (CoRR, 2025)

Urania: Differentially Private Insights into AI Use. - Daogao Liu; Edith Cohen; Badih Ghazi; Peter Kairouz; Pritish Kamath; Alexander Knop; Ravi Kumar 001; Pasin Manurangsi; Adam Sealfon; Da Yu; Chiyuan Zhang (CoRR, 2025)

Dividing Conflicting Items Fairly. - Ayumi Igarashi 001; Pasin Manurangsi; Hirotaka Yoneda (CoRR, 2025)

Private Hyperparameter Tuning with Ex-Post Guarantee. - Badih Ghazi; Pritish Kamath; Alexander Knop; Ravi Kumar 001; Pasin Manurangsi; Chiyuan Zhang (CoRR, 2025)