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ORCID7th FORC 2026: Cambridge, MA, USA
- Huijia (Rachel) Lin
:
7th Symposium on Foundations of Responsible Computing, FORC 2026, Harvard University, Cambridge, MA, USA, June 3-5, 2026. LIPIcs 368, Schloss Dagstuhl - Leibniz-Zentrum für Informatik 2026, ISBN 978-3-95977-419-2 - Front Matter, Table of Contents, Preface, Conference Organization. 0:i-0:xii
- Badih Ghazi, Cristóbal Guzmán, Pritish Kamath, Alexander Knop, Ravi Kumar, Pasin Manurangsi:
Computational Hardness of Private Coreset. 1:1-1:14 - Nikita P. Kalinin, Joel Daniel Andersson:
Learning Rate Scheduling with Matrix Factorization for Private Training. 2:1-2:21 - Charlie Harrison, Pasin Manurangsi:
Exact zCDP Characterizations for Fundamental Differentially Private Mechanisms. 3:1-3:18 - Ethan Leeman, Pasin Manurangsi:
Nearly-Optimal Private Selection via Gaussian Mechanism. 4:1-4:13 - Monika Henzinger, Nikita P. Kalinin, Jalaj Upadhyay:
Normalized Square Root: Sharper Matrix Factorization Bounds for Differentially Private Continual Counting (Extended Abstract). 5:1-5:1 - Adam Bouyamourn, Alexander Williams Tolbert:
Escaping the Subprime Trap in Algorithmic Lending. 6:1-6:27 - Charlotte Park, Kate Donahue, Manish Raghavan:
When to Ask a Question: Understanding Communication Strategies in Generative AI Tools. 7:1-7:25 - Noam Mazor, Andrew Morgan, Rafael Pass:
Can We Watermark Low-Entropy LLM Outputs? 8:1-8:22 - Rajni Dabas, Samir Khuller, Emilie Rivkin:
Serving Clients Fairly: On Facility Location and k-Median with Fair Outliers. 9:1-9:19 - Ho-Lin Chen, Po-Yu Chou, Prathamesh Dharangutte, Jie Gao, Shang-En Huang, Fang-Yi Yu:
Packing Compact Subgraphs with Applications to Districting. 10:1-10:25 - Rina Panigrahy, Vatsal Sharan:
Limitations on Accurate, Trusted, Human-Level Reasoning. 11:1-11:21 - Sara Fish, Yannai A. Gonczarowski, Jason Z. Tang, Salil P. Vadhan:
Tradeoffs in Privacy, Welfare, and Fairness for Facility Location. 12:1-12:22 - Cynthia Dwork, Chris Hays, Lunjia Hu, Nicole Immorlica, Juan C. Perdomo:
Inducing Efficient and Equitable Professional Networks Through Link Recommendations. 13:1-13:18 - Yannan Bai, Kamesh Munagala, Yiheng Shen, Davidson Zhu:
Fair Multi-Agent Persuasion with Submodular Constraints. 14:1-14:22 - Xinyan Hu, Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt:
Incentivizing High-Quality Content in Online Recommender Systems. 15:1-15:18 - Charlie Harrison, Pasin Manurangsi:
Optimal Partition Selection with Rényi Differential Privacy. 16:1-16:22 - Aloni Cohen, Refael Kohen, Kobbi Nissim, Uri Stemmer:
Protecting the Undeleted in Machine Unlearning. 17:1-17:18 - Mor Hale, Or Sheffet:
A Differentially Private Approximation of the Width Problem. 18:1-18:18 - Melissa Dutz, Han Shao, Avrim Blum, Aloni Cohen:
A Machine Learning Theory Perspective on Strategic Litigation. 19:1-19:17 - Ben Jacobsen, Nitin Kohli:
Privacy, Prediction, and Allocation. 20:1-20:24 - Parikshit Gopalan, Konstantinos Stavropoulos, Kunal Talwar, Pranay Tankala:
The Importance of Being Smoothly Calibrated. 21:1-21:22 - Mark Bun, Marco Gaboardi, Connor Wagaman:
Separating Oblivious and Adaptive Differential Privacy Under Continual Observation. 22:1-22:11
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