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ORCID6th FORC 2025: Stanford, CA, USA
- Mark Bun
:
6th Symposium on Foundations of Responsible Computing, FORC 2025, Stanford University, CA, USA, June 4-6, 2025. LIPIcs 329, Schloss Dagstuhl - Leibniz-Zentrum für Informatik 2025, ISBN 978-3-95977-367-6 - Front Matter, Table of Contents, Preface, Conference Organization. 0:i-0:x
- Kunal Talwar:
Privacy-Computation Trade-Offs in Private Repetition and Metaselection. 1:1-1:14 - Richard Hladík, Jakub Tetek:
Smooth Sensitivity Revisited: Towards Optimality. 2:1-2:17 - Syomantak Chaudhuri, Thomas A. Courtade:
Private Estimation When Data and Privacy Demands Are Correlated. 3:1-3:20 - Prathamesh Dharangutte, Jie Gao, Shang-En Huang, Fang-Yi Yu:
Hardness and Approximation Algorithms for Balanced Districting Problems. 4:1-4:24 - Avrim Blum, Emily Diana, Kavya Ravichandran, Alexander Tolbert:
Pessimism Traps and Algorithmic Interventions. 5:1-5:19 - Richard Hladík, Jakub Tetek:
Near-Universally-Optimal Differentially Private Minimum Spanning Trees. 6:1-6:19 - Carol Xuan Long, Wael Alghamdi, Alexander Glynn, Yixuan Wu, Flávio P. Calmon:
Kernel Multiaccuracy. 7:1-7:23 - Ashish Goel, Zhihao Jiang, Aleksandra Korolova, Kamesh Munagala, Sahasrajit Sarmasarkar:
Differential Privacy Under Multiple Selections. 8:1-8:25 - Changyu Gao, Andrew Lowy, Xingyu Zhou, Stephen J. Wright:
Optimal Rates for Robust Stochastic Convex Optimization. 9:1-9:21 - Joel Daniel Andersson, Rasmus Pagh, Teresa Anna Steiner, Sahel Torkamani:
Count on Your Elders: Laplace vs Gaussian Noise. 10:1-10:24 - Rishav Chourasia, Uzair Javaid, Biplab Sikdar:
Laplace Transform Interpretation of Differential Privacy. 11:1-11:23 - Charlie Harrison, Pasin Manurangsi:
Infinitely Divisible Noise for Differential Privacy: Nearly Optimal Error in the High ε Regime. 12:1-12:24 - Kristóf Madarász, Marek Pycia:
Cost over Content: Information Choice in Trade (Extended Abstract). 13:1-13:1 - Ira Globus-Harris, Varun Gupta, Michael Kearns, Aaron Roth:
Model Ensembling for Constrained Optimization. 14:1-14:17 - Martin Aumüller, Fabrizio Boninsegna, Francesco Silvestri:
Differentially Private High-Dimensional Approximate Range Counting, Revisited. 15:1-15:24 - Jason D. Hartline, Yifan Wu, Yunran Yang:
Smooth Calibration and Decision Making. 16:1-16:26 - Flávio P. Calmon, Elbert Du, Cynthia Dwork, Brian Finley, Grigory Franguridi:
Debiasing Functions of Private Statistics in Postprocessing. 17:1-17:18 - Yuxin Liu, M. Amin Rahimian:
Differentially Private Sequential Learning (Extended Abstract). 18:1-18:6 - Etam Benger, Katrina Ligett:
Mapping the Tradeoffs and Limitations of Algorithmic Fairness. 19:1-19:20 - Santhini K. A., Kamesh Munagala, Meghana Nasre, Govind S. Sankar:
Group Fairness and Multi-Criteria Optimization in School Assignment. 20:1-20:20 - Jabari Hastings, Sigal Oren, Omer Reingold:
OWA for Bipartite Assignments. 21:1-21:20 - Aravind Gollakota, Parikshit Gopalan, Aayush Karan, Charlotte Peale, Udi Wieder:
When Does a Predictor Know Its Own Loss? 22:1-22:22 - Christian Janos Lebeda, Lukas Retschmeier:
The Correlated Gaussian Sparse Histogram Mechanism. 23:1-23:20
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