FORC 2021: Accepted Papers

Yahav BechavodChristopher Jung and Zhiwei Steven Wu. Metric-Free Individual Fairness in Online Learning (non-archival)

Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton and Z. Steven Wu. An Algorithmic Framework for Fairness Elicitation (archival)

Christopher Jung, Changhwa Lee, Mallesh Pai, Aaron Roth and Rakesh Vohra. Moment Multicalibration for Uncertainty Estimation (non-archival)

Vahideh Manshadi and Scott Rodilitz. Online Policies for Efficient Volunteer Crowdsourcing (non-archival)

Vahideh Manshadi, Rad Niazadeh and Scott Rodilitz. Fair Dynamic Rationing (non-archival)

Ke YangJoshua Loftus and Julia Stoyanovich. Causal Intersectionality and Fair Ranking (archival)

Alisa Chang, Badih Ghazi, Ravi Kumar and Pasin Manurangsi. Locally Private k-Means in One Round (non-archival)

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

David Arnold, Will Dobbie and Peter Hull. Measuring and Reducing Algorithmic Discrimination in Bail Decisions (non-archival)

Wanrong Zhang, Olga Ohrimenko and Rachel Cummings. Attribute Privacy: Framework and Mechanisms (non-archival)

Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy Rothblum and Gal Yona. Outcome Indistinguishability (non-archival)

Vitaly Feldman and Tijana Zrnic. Individual Privacy Accounting via a Rényi Filter (non-archival)

Vincent Cohen-AddadPhilip KleinDániel Marx, Archer Wheeler and Christopher Wolfram. On the computational tractability of a geographic clustering problem arising in redistricting (archival)

Emily Diana, Wesley Gill, Aaron RothMichael Kearns, Ira Globus-Harris and Saeed Sharifi-Malvajerdi. Lexicographically Fair Learning: Algorithms and Generalization (archival)

Shuchi Chawla and Meena Jagadeesan. Individual Fairness in Advertising Auctions through Inverse Proportionality (non-archival)

Keegan HarrisHoda Heidari and Steven Wu. Stateful Strategic Regression (non-archival)

Enayat Ullah, Tung Mai, Anup Rao, Ryan Rossi and Raman Arora. Machine unlearning via algorithmic stability (non-archival)

Arun Ganesh and Jiazheng Zhao. Privately Answering Counting Queries with Generalized Gaussian Mechanisms (archival)

Kate Donahue and Solon Barocas. Better Together? How Externalities of Size Complicate Notions of Solidarity and Actuarial Fairness (non-archival)

John Miller, Juan Perdomo and Tijana Zrnic. Outside the Echo Chamber: Optimizing the Performative Risk (non-archival)

Cyrus Hettle, Shixiang Zhu, Swati Gupta and Yao Xie. Balanced Districting on Grid Graphs with Provable Compactness and Contiguity (non-archival)

Wanrong Zhang, Gautam Kamath and Rachel Cummings. PAPRIKA: Private Online False Discovery Rate Control (non-archival)

Claire Lazar Reich and Suhas Vijaykumar. A Possibility in Algorithmic Fairness: Can Calibration and Equal Error Rates be Reconciled? (archival)

Aloni Cohen, Moon Duchin, Jn Matthews and Bhushan Suwal. Census TopDown: Investigating the Impacts of Differential Privacy on Redistricting (archival)

%d bloggers like this: