FORC 2025: Accepted Papers

Hardness and Approximation Algorithms for Balanced Districting Problems
Prathamesh Dharangutte, Jie Gao, Shang-En Huang and Fang-Yi Yu

Privacy-Computation Trade-Offs in Private Repetition and Metaselection
Kunal Talwar

Private Estimation when Data and Privacy Demands are Correlated
Syomantak Chaudhuri and Thomas Courtade

Count on Your Elders: Laplace vs Gaussian Noise
Joel Daniel Andersson, Rasmus Pagh, Teresa Anna Steiner and Sahel Torkamani

Near-Universally-Optimal Differentially Private Minimum Spanning Trees
Richard Hladík and Jakub Tětek

Optimal Rates for Robust Stochastic Convex Optimization
Changyu Gao, Andrew Lowy, Xingyu Zhou and Stephen Wright

Laplace Transform Interpretation of Differential Privacy
Rishav Chourasia, Uzair Javaid and Biplap Sikdar

Pessimism Traps and Algorithmic Interventions
Avrim Blum, Emily Diana, Kavya Ravichandran and Alexander Tolbert

Differential Privacy with Multiple Selections
Ashish Goel, Zhihao Jiang, Aleksandra Korolova, Kamesh Munagala and Sahasrajit Sarmasarkar

Kernel Multiaccuracy
Carol Long, Wael Alghamdi, Alexander Glynn, Yixuan Wu, and Flavio Calmon

Smooth Sensitivity Revisited: Towards Optimality
Richard Hladík and Jakub Tětek

Group Fairness and Multi-criteria Optimization in School Assignment
Santhini K. A., Kamesh Munagala, Meghana Nasre and Govind S. Sankar

Cost over Content: Information Choice in Trade
Kristof Madarasz and Marek Pycia

Infinitely Divisible Noise for Differential Privacy: Nearly Optimal Error in the High ε Regime
Charlie Harrison and Pasin Manurangsi

Model Ensembling for Constrained Optimization
Ira Globus-Harris, Varun Gupta, Michael Kearns and Aaron Roth

Differentially Private Sequential Learning
Yuxin Liu and Amin Rahimian

When Does a Predictor Know its Own Loss?
Aravind Gollakota, Parikshit Gopalan, Aayush Karan, Charlotte Peale and Udi Wieder

Differentially Private High-Dimensional Approximate Range Counting, Revisited
Martin Aumüller, Fabrizio Boninsegna and Francesco Silvestri

Mapping the Tradeoffs and Limitations of Algorithmic Fairness
Etam Benger and Katrina Ligett

Smoothed Calibration and Decision Making
Jason Hartline, Yifan Wu and Yunran Yang

The Correlated Gaussian Sparse Histogram Mechanism
Christian Janos Lebeda and Lukas Retschmeier

Debiasing Functions of Private Statistics in Postprocessing
Flavio Calmon, Elbert Du, Cynthia Dwork, Brian Finley and Grigory Franguridi

OWA for Bipartite Assignments
Jabari Hastings, Sigal Oren and Omer Reingold