FORC 2023 Accepted Papers


Archival Track


Screening with Disadvantaged Agents.
Hedyeh Beyhaghi, Modibo Camara, Jason Hartline, Aleck Johnsen and Sheng Long.

From the Real Towards the Ideal: Risk Prediction in a Better World.
Cynthia Dwork, Omer Reingold and Guy Rothblum.

New Algorithms and Applications for Risk-Limiting Audits.
Bar Karov and Moni Naor.

Resistance to Timing Attacks for Sampling and Privacy Preserving Schemes.
Moni Naor, Liron David, Elad Tzalik and Yoav Ben Dov.

Multiplicative Metric Fairness Under Composition.
Milan Mossé.

Bidding Strategies for Proportional Representation in Advertisement Campaigns.
Inbal Livni Navon, Charlotte Peale, Omer Reingold and Judy Hanwen Shen.

Setting Fair Incentives to Maximize Improvement.
Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum and Keziah Naggita.

Fair Correlation Clustering in Forests.
Katrin Casel, Tobias Friedrich, Martin Schirneck and Simon Wietheger.

An Algorithmic Approach to Address Course Enrollment Challenges.
Arpita Biswas, Yiduo Ke, Samir Khuller and Quanquan C. Liu.

Distributionally Robust Data Join.
Pranjal Awasthi, Christopher Jung and Jamie Morgenstern.

Fair Grading Algorithms for Randomized Exams.
Jiale Chen, Jason Hartline and Onno Zoeter.




Forget Unlearning: Towards True Data-Deletion in Machine Learning.
Rishav Chourasia and Neil Shah.

Accounting for Stakes in Democratic Decisions.
Bailey Flanigan, Ariel Procaccia and Sven Wang.

Diagnosing Model Performance Under Distribution Shift.
Tiffany Cai, Hongseok Namkoong and Steve Yadlowsky.

Control, Confidentiality, and the Right to be Forgotten.
Aloni Cohen, Adam Smith, Marika Swanberg and Prashan Nalini Vasudevan.

On-Demand Sampling: Learning Optimally from Multiple Distributions.
Nika Haghtalab, Michael Jordan and Eric Zhao.

The Price of Differential Privacy under Continual Observation.
Palak Jain, Sofya Raskhodnikova, Satchit Sivakumar and Adam Smith.

From Robustness to Privacy and Back.
Hilal Asi, Jonathan Ullman and Lydia Zakynthinou.

Node-Differentially Private Estimation of the Number of Connected Components.
Iden Kalemaj, Sofya Raskhodnikova, Adam Smith and Charalampos Tsourakakis.

Recommending to Strategic Users.
Andreas Haupt, Dylan Hadfield-Menell and Chara Podimata.

Differentially Private Aggregation via Imperfect Shuffling.
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Jelani Nelson and Samson Zhou.

Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions.
Gavin Brown, Samuel Hopkins and Adam Smith.

Concurrent Composition Theorems for Differential Privacy.
Wanrong Zhang, Xin Lyu and Salil Vadhan.

Multicalibration as Boosting for Regression.
Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth and Jessica Sorrell.

Ticketed Learning–Unlearning Schemes.
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari and Chiyuan Zhang.

Group fairness in dynamic refugee assignment.
Daniel Freund, Thodoris Lykouris, Elisabeth Paulson, Bradley Sturt and Wentao Weng.

Stability is Stable: Connections between Replicability, Privacy, and Adaptive Generalization.
Mark Bun, Marco Gaboardi, Max Hopkins, Russell Impagliazzo, Rex Lei, Toniann Pitassi, Satchit Sivakumar and Jessica Sorrell.

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