Publications

2025

Aaron Roth, Alexander Tolbert. Resolving the Reference Class Problem at Scale. Joint work with Forthcoming in Philosophy of Science, 2025.

Aaron Roth, Buxin Su, Jiayao Zhang, Natalie Collina, Yuling Yan, Didong Li, Kyunghyun Cho, Jianqing Fan, Weijie J. Su. Analysis of the ICML 2023 Ranking Data: Can Authors’ Opinions of Their Own Papers Assist Peer Review in Machine Learning? Forthcoming in the Journal of the American Statistical Association (JASA), 2025.

Aaron Roth, Eric Eaton, Marcel Hussing, Michael Kearns, Sikata Sengupta, and Jessica Sorrell. Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces. Joint work with In the Proceedings of ICML 2025.

Aaron Roth, Eshwar Ram Arunachaleswaran, Natalie Collina, and Mirah Shi. An Elementary Predictor Obtaining 2√T Distance to Calibration. In the Proceedings of SODA 2025.

Aaron Roth, Eshwar Ram Arunachaleswaran, Natalie Collina, Sampath Kannan, and Juba Ziani. Algorithmic Collusion Without Threats. In the Proceedings of ITCS 2025.

Aaron Roth, Georgy Noarov, Ramya Ramalingam, and Stephan Xie. High-Dimensional Prediction for Sequential Decision Making. In the Proceedings of ICML 2025.

Aaron Roth, Ira Globus-Harris, Varun Gupta, and Michael Kearns. Model Ensembling for Constrained Optimization. In the Proceedings of FORC 2025.

Aaron Roth, Jiuyao Lu and Mirah Shi. Sample Efficient Omniprediction and Downstream Swap Regret for Non-Linear Losses. In the Proceedings of COLT 2025.

Aaron Roth, Joint work with Natalie Collina and Varun Gupta. Repeated Contracting with Multiple Non-Myopic Agents: Policy Regret and Limited Liability. In the Proceedings of EC 2024.

Aaron Roth, Marcel Hussing, Michael Kearns, Sikata Sengupta, and Jessica Sorrell. Oracle-Efficient Reinforcement Learning for Max Value Ensembles. In the Proceedings of NeurIPS 2024.

Aaron Roth, Martin Bertran, Shuai Tang, Michael Kearns, Jamie Morgenstern, and Steven Wu. Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable. In the Proceedings of NeurIPS 2024.

Aaron Roth, Mirah Shi. Forecasting for Swap Regret for All Downstream Agents. JIn the Proceedings of EC 2024.

Aaron Roth, Natalie Collina and Han Shao. Efficient Prior-Free Mechanisms for No-Regret Agents. In the Proceedings of EC 2024.

Aaron Roth, Natalie Collina, Surbhi Goel, and Varun Gupta. Tractable Agreement Protocols. Joint work with In the Proceedings of STOC 2025.

Aaron Roth, Ramya Ramalingam and Shayan Kiyani. The Relationship between No-Regret Learning and Online Conformal Prediction. Joint work with In the Proceedings of ICML 2025.

Aaron Roth, Shayan Kiyani, George Pappas, and Hamed Hassani. Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents .  Joint work with In the Proceedings of ICML 2025.

Aayush Jain, Huijia Lin, Sagnik Saha. A Systematic Study of Sparse LWE (CRYPTO 2024).

Angelos Assos, Yuval Dagan, Constantinos Daskalakis: Maximizing utility in multi-agent environments by anticipating the behavior of other learners. In the 38th Annual Conference on Neural Information Processing Systems (NeurIPS), 2024.

Aravind Gollakota, Parikshit Gopalan, Aayush Karan, Charlotte Peale, Udi Wieder, When Does a Predictor Know its Own Loss? To appear in FORC 2025.

Arnab Bhattacharyya, Constantinos Daskalakis, Themis Gouleakis, Yuhao Wang: Learning High-dimensional Gaussians from Censored Data. AISTATS, 2025.

Ashia Wilson. Aligning Evaluation with Clinical Priorities: Calibration, Label Shift, and Error Costs: Machine learning-based decision support systems are increasingly deployed in clinical settings, where probabilistic scoring functions are used to inform and prioritize patient management decisions. However, widely used scoring rules, such as accuracy and AUC-ROC, fail to adequately reflect key clinical priorities, including calibration, robustness to distributional shifts, and sensitivity to asymmetric error costs. In this work, we propose a principled yet practical evaluation framework for selecting calibrated thresholded classifiers that explicitly accounts for the uncertainty in class prevalences and domain-specific cost asymmetries often found in clinical settings. Building on the theory of proper scoring rules, particularly the Schervish representation, we derive an adjusted variant of cross-entropy (log score) that averages cost-weighted performance over clinically relevant ranges of class balance. The resulting evaluation is simple to apply, sensitive to clinical deployment conditions, and designed to prioritize models that are both calibrated and robust to real-world variations.

Ashia Wilson. Semivalue-based data valuation is arbitrary and gameable: We show that semi-value valuations are almost always underspecified, and that this underspecification leaves open the possibility of gamification. Specifically, through theoretical constructions and empirical examples, we demonstrate that a bad-faith valuator can manipulate utility specifications to favor preferred datapoints, and that a good-faith valuator is left without principled guidance to justify any particular specification.

Ashia Wilson.The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches: we revisit this Gaussian Sketching for privacy through the lens of Renyi Differential Privacy (RDP), providing a refined privacy analysis that yields significantly tighter bounds than prior results. We then demonstrate how this improved analysis leads to performance improvement in different linear regression settings, establishing theoretical utility guarantees. Empirically, our methods improve performance across multiple datasets and, in several cases, reduce runtime. this is in collaboration with co-PI Katrina Ligett.

Avrim Blum and Kavya Ravichandran. Nearly-tight Approximation Guarantees for the Improving Multi-Armed Bandits Problem – now appears in ALT 2025.

Avrim Blum, Emily Diana, Kavya Ravichandran and Alexander Tolbert. Pessimism Traps and Algorithmic Interventions.  FORC 2025.

Avrim Blum, Steve Hanneke, Chirag Pabbaraju, Donya Saless. Proofs as Explanations: Short Certificates for Reliable Predictions.  COLT 2025.

Ayelet Gordon-Tapiero and Katrina Ligett. The Collective Aspect of Job Seekers’ Data Rights, COMPAR. LAB. L. & POL’Y J. (Forthcoming, 2025).

Ayelet Gordon-Tapiero and Yotam Kaplan. Generative AI Training as Unjust Enrichment, 86 OHIO ST. L. J. 287 (2025) (Winner of Carnegie Endowment for International Peace Award for AI and Liability).

Ayelet Gordon-Tapiero, Gideon Parchomovsky, & Yotam Kaplan. Deepfake Liability, 104 N.C. L. REV. (Forthcoming, 2026).

Ayelet Gordon-Tapiero, Katrina Ligett, and Kobbi Nissim. On the Rival Nature of Data: Tech and Policy Implications. CSLAW 2025.

Ayelet Gordon-Tapiero. A Liability Framework for AI Companions, 1 GEO. WASH. U. J. L. & TECH. (Forthcoming, 2025).

Charlotte Peale, Vinod Raman, Omer Reingold, Representative Language Generation, ICML 2025.

Constantinos Daskalakis, Gabriele Farina, Maxwell Fishelson, Charilaos Pipis, Jon Schneider: Efficient Learning and Computation of Linear Correlated Equilibrium in General Convex Games. In the 57th ACM Symposium on Theory of Computing (STOC), 2025.

Constantinos Daskalakis, Ian Gemp, Yanchen Jiang, Renato Paes Leme, Christos Papadimitriou, Georgios Piliouras: Charting the Shapes of Stories with Game Theory. In the 38th Annual Conference on Neural Information Processing Systems (NeurIPS), 2024, Creative Track. 

Cynthia Dwork, Chris Hays, Lunjia Hu, Nicole Immorlica, Juan Perdomo. Inducing Efficient and Equitable Professional Networks through Link Recommendations. [arXiv] 2025.

Cynthia Dwork, Chris Hays, Lunjia Hu, Nicole Immorlica, Juan Perdomo, Inducing Efficient and Equitable Professional Networks through Link Recommendations. Preprint 2025

Cynthia Dwork, Chris Hays, Nicole Immorlica, Juan Perdomo, Pranay Tankala. From Fairness to Infinity: Outcome-Indistinguishable (Omni)Prediction in Evolving Graphs Conference on Learning Theory (COLT), 2025.

Cynthia Dwork, Lunjia Hu, and Han Shao. How Many Domains Suffice for Domain Generalization?  A Tight Characterization via the Domain Shattering Dimension, 2025.

Cynthia Dwork, Pranay Tankala, Linjun Zhang. Differentially Private Learning Beyond the Classical Dimensionality Regime. Theory and Practice of Differential Privacy (TPDP), 2025.

Cynthia Dwork. ‘Prenatal’ is a Marketing Condition.

Cynthia Dwork. Oral Presentation. Foundations of Responsible Computing (FORC), 2025 – Highlights Track.

Cynthia Dwork. The Folly of AI for Age Verification, Reid McIlroy-Young, AAAI 2025 Workshop on AI for Public Mission.

Diana Freed, Reid McIlroy-Young, Sarah Radway, Gabriela Becher, Diane Bernabei, Christina Lee & Cynthia Dwork. Medical Data for Sale: Accessing Reproductive Health Information via the Data Brokerage Landscape. IEEE Security & Privacy, Jul/Aug 2025: Gender and Sexuality in Online Safety.

E. Pierson, D. Shanmugam, R. Movva, J. Kleinberg, M. Agrawal, M. Dredze, K. Ferryman, J.W. Gichoya, D. Jurafsky, P.W. Koh, K. Levy, S. Mullainathan, Z. Obermeyer, H. Suresh, K. Vafa. Using Large Language Models to Promote Health Equity. New England Journal of Medicine AI (NEJM AI) 2(2), 2025.

Etam Benger and Katrina Ligett. Mapping the Tradeoffs and Limitations of Algorithmic Fairness, FORC 2025.

F. Calmon, E. Du, C. Dwork, B. Finley, and G. Franguridi. Debiasing Functions of Private.

G. Noti, K. Donahue, J. Kleinberg, S. Oren. AI-Assisted Decision Making with Human Learning. Proc. 26th ACM Conference on Economics and Computation (EC), 2025. 

Gavin Brown, Jonathan Hayase, Sam Hopkins, Weihao Kong, Xiyang Liu, Seewong Oh, Juan Carlos Perdomo, Adam Smith. Insufficient Statistics Perturbation: Stable Estimator for Private Least Squares. Conference on Learning Theory (COLT) 2024.

Gavin Brown, Jonathan Hayase, Sam Hopkins, Weihao Kong, Xiyang Liu, Seewong Oh, Juan Carlos Perdomo, Adam Smith. Non-archival track at Foundations of Responsible Computing (FORC) 2024.

Giannis Daras, Yeshwanth Cherapanamjeri, Constantinos Daskalakis: How much is a noisy image worth? Data Scaling Laws for Ambient Diffusion. ICLR, 2025.

Gustaf Ahdritz, Aravind Gollakota, Parikshit Gopalan, Charlotte Peale, Udi Wieder, Provable Uncertainty Decomposition via Higher-Order Calibration, To appear in ICLR 2025

Idan Attias, Avrim Blum, Keziah Naggita, Donya Saless, Dravyansh Sharma, Matthew R. Walter. PAC Learning with Improvements.  ICML 2025.

J. Kleinberg, S. Mullainathan. Language Generation in the Limit. Advances in Neural Information Processing Systems (NeurIPS) 38, 2024. 

Jabari Hastings, Sigal Oren, Omer Reingold, OWA for Bipartite Assignments, to appear in FORC 2025.

Jamie Morgenstern. A Common Pool of Privacy Problems: Legal and Technical Lessons from a Large-Scale Web-Scraped Machine Learning Dataset. 

Jamie Morgenstern. DIFFERENTIALLY PRIVATE MECHANISM DESIGN VIA QUANTILE ESTIMATION.

Jamie Morgenstern. Large language models that replace human participants can harmfully misportray and flatten identity groups.

Jamie Morgenstern. Who’s in and who’s out? A case study of multimodal CLIP-filtering in DataComp. 

Joshua P. Gardner, Juan Carlos Perdomo, Ludwig Schmidt. Large Scale Transfer Learning for Tabular Data via Language Modeling. Advances in Neural Information Processing Systems (Neurips) 2024.

Juan Carlos Perdomo, Tolani Britton, Moritz Hardt, Rediet Abebe. Difficult Lessons on Social Prediction from Wisconsin Public Schools. ACM Conference on Fairness, Accountability, and Transparency (FAccT) 2025.

Juan Carlos Perdomo. Revisiting the Predictability of Performative, Social Events International Conference on Machine Learning (ICML), 2025.

Judy Hanwen Shen, Ellen Vitercik, and Anders Wikum, Algorithms with Calibrated Machine Learning Predictions, to appear in International Conference on Machine Learning (ICML) 2025 

Lee Cohen, Jack Hsieh, Connie Hong, and Judy Hanwen Shen, Two Tickets are Better than One: Fair and Accurate Hiring Under Strategic LLM Manipulations. To appear in International Conference on Machine Learning (ICML) 2025 

Judy Hanwen Shen, and Carlos Guestrin, Societal Impacts Research Requires Benchmarks for Creative Composition Tasks, to appear in International Conference on Machine Learning (ICML) 2025 

Inbal Rachel Livni Navon, Omer Reingold, Judy Hanwen Shen, Fairness with respect to Stereotype Predictors: Impossibilities and Best Practices. To appear in Transactions on Machine Learning Research (TMLR) 2025 

Judy Hanwen Shen, Inioluwa Deborah Raji, and Irene Y. Chen, The Data Addition Dilemma, Machine Learning for Health Care Conference (MLHC 2024)

K. Donahue, J. Kleinberg. Private Blotto: Viewpoint Competition with Polarized Agents. Proc. 39th AAAI Conference on Artificial Intelligence (AAAI-25), 2025. 

K. Hamade, R. McIlroy-Young, S. Sen, J. Kleinberg, A. Anderson. Designing Skill-Compatible AI: Methodologies and Frameworks in Chess. Proc 12th International Conference on Learning Representations (ICLR), 2024. 

K. Peng, N. Garg, J. Kleinberg. A No Free Lunch Theorem for Human-AI Collaboration. Proc. 39th AAAI Conference on Artificial Intelligence (AAAI-25), 2025. 

K. Tomlinson, T. Namjoshi, J. Ugander, J. Kleinberg. Replicating Electoral Success. Proc. 39th AAAI Conference on Artificial Intelligence (AAAI-25), 2025.

K. Vafa, J.Y. Chen, J. Kleinberg, S. Mullainathan, A. Rambachan. Evaluating the World Model Implicit in a Generative Model. Advances in Neural Information Processing Systems (NeurIPS) 38, 2024. 

Katrina Brown & Reid McIlroy-Young. Order-Independence With Fine Tuning, Bi-Align workshop paper at ICLR 2025.

Lee Cohen, Yishay Mansour, Shay Moran, Han Shao, Probably Approximately Precision and Recall Learning, preprint.

Lunjia Hu, Arun Jambulapati, Kevin Tian, Chutong Yang. Testing Calibration in Nearly-Linear Time, NeurIPS 2024.

Lunjia Hu, Arun Jambulapati, Kevin Tian, Chutong Yang. Testing Calibration in Nearly-Linear Time. [arXiv] NeurIPS 2024.

Lunjia Hu, Kevin Tian, Chutong Yang, Omnipredicting Single-Index Models with Multi-Index Models, STOC 2025.

Lunjia Hu, Kevin Tian, Chutong Yang. Omnipredicting Single-Index Models with Multi-Index Models. [arXiv].

Lunjia Hu, Yifan Wu. Calibration Error for Decision Making. [arXiv] FOCS 2024 (published under the old title “Predict to Minimize Swap Regret for All Payoff-Bounded Tasks”.

N. Fleming, T. Pitassi, R. Robere. PPP is not Turing-closed. Symposium on Theory of Computing (STOC), 2024.

O. Korten, and T. Pitassi. Strong vs. Weak Range Avoidance and the Linear Ordering Principle. Foundations of Computer Science (FOCS), 2024.

O. Korten, T. Pitassi, R. Impagliazzo.  Stronger Cell Probe Lower Bounds via Local PRGs. ECCC TR25-030.

Omer Reingold, Judy Hanwen Shen, Aditi Talati, Dissenting Explanations: Leveraging Disagreement to Reduce Model Overreliance. AAAI 2024: 21537-21544.

Parikshit Gopalan, Lunjia Hu, Guy N. Rothblum. On Computationally Efficient Multi-Class Calibration. [arXiv] COLT 2024.

Pushkar Shukla, Dhruv Srikanth, Lee Cohen, Matthew Turk, Utilizing Adversarial Examples for Bias Mitigation and Accuracy Enhancement, preprint.

R. Movva, K. Peng, N. Garg, J. Kleinberg, E. Pierson. Sparse Autoencoders for Hypothesis Generation. Proc. 42nd Intl. Conf. on Machine Learning (ICML), 2025.

Reid McIlroy-Young, Katrina Brown, Conlan Olson, Linjun Zhang & Cynthia Dwork. Order-Independence Without Fine Tuning, NeurIPS, 2024.

S. Greenwood, K. Levy, S. Barocas, H. Heidari, J. Kleinberg. Designing algorithmic delegates: The role of indistinguishability in human-AI handoff. Proc. 26th ACM Conference on Economics and Computation (EC), 2025.

Statistics in Postprocessing. In M. Bun, editor, 6th Symposium on Foundations of Responsible Computing (FORC 2025), volume 329 of Leibniz International Proceedings in Informatics (LIPIcs), pages 17:1–17:18, Dagstuhl, Germany, 2025. Schloss Dagstuhl – Leibniz-Zentrum f¨ur Informatik.

Sumegha Garg, Christopher Jung, Omer Reingold, Aaron Roth, Oracle Efficient Online Multicalibration and Omniprediction. SODA 2024: 2725-2792

Tomer Shadmy and Katrina Ligett. Reimagining AI Decentralization. To appear in the George Washington Journal of Law & Technology.

Tomer Shadmy, Addressing  AI Social Disruption, ANU Journal of Law and Technology (Forthcoming).

Tomer Shadmy, Data-Driven Collective Rights, Comparative Labor Law & Policy Journal (CLLPJ) (Forthcoming).

Tomer Shadmy, What Machines Cannot Imitate, in AI and Human Interaction: A Human Rights Perspective / Oxford University Press (John Tasioulas and Yuval Shany ed.)

Unai Fischer-Abaigar, Cristoph Kern, Juan Carlos Perdomo. The Value of Prediction in Identifying the Worst-Off. International Conference on Machine Learning (ICML) 2025 [oral, top 1% of submissions] Foundations of Responsible Computing (FORC) 2025 [highlights track].

Vardis Kandiros, Charilaos Pipis, Constantinos Daskalakis, Christopher Harshaw: The Conflict Graph Design: Estimating Causal Effects under Arbitrary Neighborhood Interference. Under Submission. 

X. Chen, W. Pires, T. Pitassi, R. Servedio.  Relative Error Testing of conjunctions and decision lists. ICALP 2025.

X. Chen, W. Pires, T. Pitassi, R. Servedio. Testing Juntas and Junta Subclasses with Relative Error. COLT 2025.

Y. Mahdavihyeh, J. Lucas, M. Ren, A. Tolias, R. Zemel, T. Pitassi. Replay can provably increase forgetting, Conference on Lifelong Learning Agenda (COLLA) 2025.

Y. Wang, H. Cui, J. Kleinberg. Microstructures and Accuracy of Graph Recall by Large Language Models. Advances in Neural Information Processing Systems (NeurIPS) 38, 2024.

Yang Cai, Constantinos Daskalakis, Haipeng Luo, Chen-Yu Wei, Weiqiang Zheng: On Tractable Φ-Equilibria in Non-Concave Games. In the 38th Annual Conference on Neural Information Processing Systems (NeurIPS), 2024.

Yao-Ching Hsieh, Aayush Jain, Huijia Lin. Lattice-Based Post-Quantum iO from Circular Security with Random Opening Assumption (Crypto 2025).

Yuval Dagan, Constantinos Daskalakis, Maxwell Fishelson, Noah Golowich, Robert Kleinberg, Princewill Okoroafor: Breaking the T^2/3 Barrier for Sequential Calibration. In the 57th ACM Symposium on Theory of Computing (STOC), 2025.

Zollo, T., Deng, Z., Snell, J., Pitassi, T., Zemel, R. Improving Predictor Reliability with Selective Recalibration. Transactions on Machine Learning Research, July 2024.

2024

Aayush Jain, Huijia Lin, Amit Sahai. Indistinguishability Obfuscation from Well-Founded Assumptions. Communications of ACM 2024

Andres Cristi and Sigal Oren, Planning against a prophet: a graph-theoretic framework for making sequential decisions, EC 2024

Anirudh Krishna, Inbal Livni Navon, Mary Wootters, Viderman’s algorithm for quantum LDPC codes, SODA 2024

Avrim Blum and Kavya Ravichandran. Nearly-tight Approximation Guarantees for the Improving Multi-Armed Bandits Problem. ArXiv preprint 2024

Avrim Blum, Princewill Okoroafor, Aadirupa Saha, and Kevin Stangl. On the Vulnerability of Fairness Constrained Learning to Malicious Noise. Proceedings of the 27th International Conference on Artificial Intelligence and Statistics. AISTATS, 2024

Ayelet Gordon Tapiero, Kobbi Nissim, Paul Ohm and Muthuramakrishnan Venkitasubramaniam. Intent and Purpose in an Algorithmic Reality. 2024

Ayelet Gordon Tapiero and Yotam Kaplan. Generative AI as Unjust Enrichment. Accepted for publication in the Ohio State Law Journal. 2024

B. Laufer, J. Kleinberg, H. Heidari. Fine-Tuning Games: Bargaining and Adaptation for General-Purpose Models. Proc. 33rd International World Wide Web Conference, 2024

Bar Alon, Moni Naor, Eran Omri, Uri Stemmer, MPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably, Crypto 2024

C. Dwork, C. Hays, J. Kleinberg, M. Raghavan. Content Moderation and the Formation of Online Communities: A Theoretical Framework. Proc. 33rd International World Wide Web Conference, 2024

C. Dwork, C. Hays, J. Kleinberg, M. Raghavan. Equilibria, Efficiency, and Inequality in Network Formation for Hiring and Opportunity. EC, 2024.

Cecilia Boschini, Hila Dahari, Moni Naor, Eyal Ronen, That’s not my Signature! Fail-Stop Signatures for a Post-Quantum World, Crypto 2024

Constantinos Daskalakis, Noah Golowich, Nika Haghtalab, Abhishek Shetty: Smooth Nash Equilibria: Algorithms and Complexity. ITCS 2024

Constantinos Daskalakis, Noah Golowich: Is Efficient PAC Learning Possible with an Oracle That Responds “Yes” or “No”? COLT 2024

Du, Finley, and Frangurini, Debiasing Functions of Private Statistics in Post-Processing. 2024

Fan Chen, Constantinos Daskalakis, Noah Golowich, Sash Rakhlin: Near-Optimal Learning and Planning in Separated Latent MDPs. COLT 2024

Gal Arnon, Alessandro Chiesa, Giacomo Fenzi, and Eylon Yogev, STIR: Reed–Solomon Proximity Testing with Fewer Queries, Crypto 2024

Giannis Daras, Alex Dimakis, Constantinos Daskalakis: Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data. ICML 2024

Ira Globus-Harris, Declan Harrison, Michael Kearns, Pietro Perona, Aaron Roth. Diversified Ensembling: An Experiment in Crowdsourced Machine Learning. FAccT 2024

Jabari Hastings, Christopher Jung, Charlotte Peale, Vasilis Syrgkanis, Taking a Moment for Distributional Robustness, manuscript 2024

Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu, Adam Tauman Kalai, Preetum Nakkiran, Loss Minimization Yields Multicalibration for Large Neural Networks. ITCS 2024

Jon Kleinberg, Sigal Oren, Emily Ryu, and Éva Tardos, Modeling reputation-based behavioral biases in school choice, EC 2024

Justin Whitehouse, Christopher Jung, Vasilis Syrgkanis, Bryan Wilder, Zhiwei Steven Wu, Orthogonal Causal Calibration, manuscript 2024

K. Peng, M. Raghavan, E. Pierson, J. Kleinberg, N. Garg. Reconciling the accuracy-diversity trade-off in recommendations. Proc. 33rd International World Wide Web Conference, 2024

K. Tomlinson, J. Ugander, J. Kleinberg. The Moderating Effect of Instant Runoff Voting. Proc. 38th AAAI Conference on Artificial Intelligence (AAAI-24), 2024

Keziah Naggita, Matthew R. Walter, and Avrim Blum. Learning Actionable Counterfactual Explanations in Large State Spaces. ArXiv preprint 2024

Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani. Oracle Efficient Algorithms for Groupwise Regret. ICLR 2024

L.T. Liu, S. Barocas, J. Kleinberg, K. Levy. On the Actionability of Outcome Prediction. Proc. 38th AAAI Conference on Artificial Intelligence (AAAI-24), 2024

Lee Cohen, Yishay Mansour, Shay Moran, Han Shao, Learnability Gaps of Strategic Classification, COLT 2024

Linjun Zhang, Lujing Zhang, Aaron Roth. Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks. ICML 2024

Lunjia Hu, Charlotte Peale, Judy Hanwen Shen, Multigroup Robustness, ICML 2024

Lunjia Hu, Yifan Wu Predict to Minimize Swap Regret for All Payoff-Bounded Tasks, manuscript 2024

Martin Bertrain, Gianluca Detommaso, Riccardo Fogliato, Aaron Roth. Multicalibration for Confidence Scoring in LLMs. ICML 2024

Moni Naor, Eugene Pekel: Adjacency Sketches in Adversarial Environments. SODA 2024: 1067-1098

Natalie Collina, Han Shao, Aaron Roth. Efficient Prior-Free Mechanisms for No-Regret Agents. EC 2024

Natalie Collina, Han Shao, Aaron Roth. Forecasting for Swap Regret for All Downstream Agents. Aaron Roth, Mirah Shi. In the Proceedings of EC 2024

Noga Amit, Guy N. Rothblum, Constant-Round Arguments for Batch-Verification and Bounded-Space Computations from One-Way Functions. CRYPTO 2024

Parikshit Gopalan, Lunjia Hu, Guy N. Rothblum, On Computationally Efficient Multi-Class Calibration. COLT 2024

Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson. Fair Active Learning in Low-Data Regimes. UAI 2024

Saba Ahmadi, Avrim Blum, Omar Montasser, Kevin Stangl. Agnostic Multi-Robust Learning using ERM. AISTATS, 2024

Sarah Dean, Mihaela Curmei, Lillian J. Ratliff, Jamie Morgenstern, Maryam Fazel. Multi-learner risk reduction under endogenous participation dynamics. AISTATS 2024

Shafi Goldwasser, Guy Rothblum Constant-Round Arguments for Batch-Verification and Bounded-Space Computations from One-Way Functions. 2024

Shafi Goldwasser, Orr Paradise and Guy Rothblum. Models That Prove Their Own Correctness. 2024

Sumegha Garg, Christopher Jung, Aaron Roth, Omer Reingold. Oracle Efficient Online Multicalibration and Omniprediction. SODA 2024: 2725-2792

T. Zollo, T. Morrill, Z. Deng, J. Snell, T. Pitassi, and R. Zemel. Prompt Risk Control: A flexible framework for bounding the probability of high-loss predictions. ICLR, 2024

Theodora Worledge, Judy Hanwen Shen, Nicole Meister, Caleb Winston, Carlos Guestrin, Unifying corroborative and contributive attributions in large language models. SaTML 2024

Tomer Shadmy and Katrina Ligett. Reimagining AI Decentralization. PLSC 2024

Yao-Ching Hsieh, Huijia Lin, Ji Luo. A General Framework for Lattice-Based ABE Using Evasive Inner-Product Functional Encryption. EUROCRYPT 2024

Yoav Ben-Dov, Liron David, Moni Naor, Elad Tzalik, Are Your Keys Protected? Time will Tell. ITC, 2024

Yuval Dagan, Constantinos Daskalakis, Maxwell Fishelson, Noah Golowich: From External to Swap Regret 2.0: An Efficient Reduction and Oblivious Adversary for Large Action Spaces. STOC 2024

Zoe Ruha Bell, Shafi Goldwasser, Michael P. Kim, and Jean-Luc Watson. Certifying Private Probabilistic Mechanisms. CRYPTO 2024

2023

Aaron Roth, Alexander Tolbert and Scott Weinstein. Reconciling Individual Probability Forecasts. FAccT 2023

Abiy Tasissa, Pranay Tankala, James M. Murphy, and Demba E. Ba. K-Deep Simplex: Manifold Learning via Local Dictionaries. IEEE Transactions on Signal Processing. 71:3741–3754, 2023

Angelos Assos, Idan Attias, Yuval Dagan, Constantinos Daskalakis, Maxwell Fishelson. Online Learning and Solving Infinite Games with an ERM Oracle. COLT 2023.

Ayelet Gordon-Tapiero, Alexandra Wood, and Katrina Ligett. The Case for Establishing a Collective Perspective to Address the Harms of Platform Personalization. Vanderbilt Journal of Entertainment & Technology Law, Forthcoming. 2023

B. Laufer, J. Kleinberg, K. Levy, H. Nissenbaum. Strategic Evaluation: Subjects, Evaluators, and Society. Proc. 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization. EAAMO, 2023

Bar Karov, Moni Naor. New Algorithms and Applications for Risk-Limiting Audits. FORC 2023: 2:1-2:27.

Bun, M., Gaboardi, M., Hopkins, M., Impagliazzo, R., Lei, R., Pitassi, T., Sivakumar, S., and Sorrell, J. Stability is Stable: Connections between Replicability, Privacy, and Adaptive Generalization. STOC, 2023.

C. Dwork, C. Hayes, J. Kleinberg, and M. Raghavan. Content Moderation and the Formation of Online Communities: A Theoretical Framework. 2023.

C. Dwork, D. Lee, R. Lin, and P. Tankala. From Pseudorandomness to Multi-Group Fairness and Back, Proc, COLT, 2023.

C. Dwork, O. Reingold, G. N. Rothblum. From the Real Towards the Ideal: Risk Prediction in a Better World, Symposium on Foundations of Responsible Computing. FORC 2023

C. Morewedge, S. Mullainathan, H.F. Naushan, C. Sunstein, J. Kleinberg, M. Raghavan, J. Ludwig. Human bias in algorithm design. Nature Human Behaviour, 7(2023)

Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth. Batch Multivalid Conformal Prediction. In the Proceedings of ICLR 2023.

Constantinos Daskalakis, Noah Golowich, Kaiqing Zhang. The Complexity of Markov Equilibrium in Stochastic Games. In the 35th Annual Conference on Learning Theory. COLT 2023.

Constantinos Daskalakis, Noah Golowich, Stratis Skoulakis, Manolis Zampetakis. STay-ON-the-Ridge: Guaranteed Convergence to Local Minimax Equilibrium in Nonconvex-Nonconcave Games. In the 35th Annual Conference on Learning Theory. COLT 2023.

Cynthia Dwork, Daniel Lee, Huijia Lin, Pranay Tankala. From Pseudorandomness to Multi-Group Fairness and Back. The 36th Annual Conference on Learning Theory. COLT 2023.

Cynthia Dwork, Omer Reingold, Guy N. Rothblum. From the Real Towards the Ideal: Risk Prediction in a Better World. FORC 2023: 1:1-1:17. (FORC 2023 best paper award.)

Daniel Alabi, Pravesh K. Kothari, Pranay Tankala, Prayaag Venkat, and Fred Zhang. Privately Estimating a Gaussian: Efficient, Robust, and Optimal. STOC, 2023

Davin Choo, Yuval Dagan, Constantinos Daskalakis, Anthimos-Vardis Kandiros. Learning and Testing Latent-Tree Ising Models Efficiently. COLT 2023.

G. N. Rothblum, G. Yona. Decision Making under Miscalibration. Innovations in Theoretical Computer Science Conference. ITCS 2023: 92:1-92:20.

G. N. Rothblum. Indistinguishable Predictions and Multi-group Fair Learning. Annual International Conference on the Theory and Applications of Cryptographic Techniques. EUROCRYPT (1) 2023: 3-21.

Georgy Noarov, Aaron Roth. The Scope of Multicalibration: Characterizing Multicalibration via Property Elicitation. ICML 2023

H. Heidari, S. Barocas, J. Kleinberg, K. Levy. Informational Diversity and Affinity Bias in Team Growth Dynamics. Proc. 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization. EAAMO, 2023

Han Shao, Avrim Blum, and Omar Montasser. Strategic Classification under Unknown Personalized Manipulation. NeurIPS, 2023

Hanjun Li, Huijia Lin, Antigoni Polychroniadou, Stefano Tessaro. LERNA: Secure Single-Server Aggregation via Key-Homomorphic Masking. ASIACRYPT 2023

Ido Nachum, Thomas Weinberger, Jonathan Shafer, Michael Gastpar. Fantastic Generalization Measures are Nowhere to be Found. NeurIPS 2023

Inbal Dekel, Rachel Cummings, Ori Heffetz, and Katrina Ligett. The Privacy Elasticity of Behavior: Conceptualization and Application. EC 2023.

Inbal Livni Navon, Charlotte Peale, Omer Reingold, Judy Hanwen Shen. Bidding Strategies for Proportional Representation in Advertisement Campaigns. FORC 2023: 3:1-3:22.

Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, and Jessica Sorrell. Multicalibration as Boosting for Regression. In the Proceedings of ICML 2023. FORC 2023. Selected as an oral presentation.

Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth. Multicalibrated Regression for Downstream Fairness. AIES 2023.

J. Kleinberg, J. Ludwig, S. Mullainathan, M. Raghavan. The Inversion Problem: Why Algorithms Should Infer Mental State and Not Just Predict Behavior. Perspectives on Psychological Science. 2023

J. Snell, T. Zollo, Z. Deng, T. Pitassi, and R. Zemel. Quantile Risk Control: A Flexible Framework for Bounding the Probability of High Loss Predictions, Proceedings. ICLR 2023.

Jamie Morgenstern. Distributionally Robust Data Join. 2023.

Jamie Morgenstern. Evaluation of targeted dataset collection on racial equity in face recognition. 2023

Jamie Morgenstern. Multi-learner risk reduction under endogenous participation dynamics. 2023

Jamie Morgenstern. Multicalibrated Regression for Downstream Fairness. 2023

Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran, When Does Optimizing a Proper Loss Yield Calibration? NeurIPS 2023

Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu, Adam Tauman Kalai, Preetum Nakkiran. Loss Minimization Yields Multicalibration for Large Neural Networks. 2023

Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran. A Unifying Theory of Distance from Calibration. STOC 2023.

Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran. When Does Optimizing a Proper Loss Yield Calibration? 2023

John Duchi, Vitaly Feldman, Lunjia Hu, Kunal Talwar. Subspace Recovery from Heterogeneous Data with Non-isotropic Noise, NeurIPS 2022

K. Donahue, A. Chouldechova, K. Kenthapadi. Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness. Proc. ACM Conference on Fairness, Accountability, and Transparency. FAccT, 2022

K. Donahue, J. Kleinberg. Fairness in model-sharing games. Proc. 32nd International World Wide Web Conference. 2023.

K. Kawaguchi, Z. Deng, X. Ji, and J. Huang. How does Information Bottleneck help Deep Learning? ICML. 2023.

K. Tomlinson, J. Ugander, J. Kleinberg. Ballot length in instant runoff voting. Proc. 37th AAAI Conference on Artificial Intelligence, AAAI-23, 2023.

Katrina Ligett and Dana Yaffe. Fairness and the Israeli Credit Scoring System: A Case Study. 2023

Katrina Ligett and Tomer Shadmy. Realizing the Dream of Decentralized Machine Learning. 2023

Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, and Juba Ziani. Wealth Dynamics Over Generations: Analysis and Interventions. SaTML 2023.

Lee Cohen, Saeed Sharifi-Malvajerdi, Kevin Stangl, Ali Vakilian, Juba Ziani. Sequential Strategic Screening. ICML, 2023.

Lunjia Hu, Charlotte Peale. Comparative Learning: A Sample Complexity Theory for Two Hypothesis Classes. ITCS 2023.

Lunjia Hu, Inbal Livni Navon, Omer Reingold. Generative Models of Huge Objects. CCC 2023

Lunjia Hu, Inbal Rachel Livni Navon, Omer Reingold, Chutong Yang. Omnipredictors for Constrained Optimizatio. ICML 2023.

M. Bun, M. Gaboardi, M. Hopkins, R. Impagliazzo, R. Lei, T. Pitassi, S. Sivakumar, J. Sorrell. Stability is Stable: Connections between Replicability, Privacy and Adaptive Generalization, Proceedings Symposium on Theory of Computing. STOC 2023.

M. Papachristou, S. Banerjee, J. Kleinberg. Dynamic Interventions for Networked Contagions. Proc. 32nd International World Wide Web Conference, 2023.

Mark Braverman, Sumegha Garg and David Woodruff. Multi-pass lower bounds for coin problem. 2023

Michael P. Kim and Juan Carlos Perdomo. Making Decisions under Outcome Performativity. ITCS 2023

Miranda Christ, Sam Gunn, Or Zamir. Undetectable Watermarks for Language Models. In submission. 

Moni Naor, Kobbi Nissim, Uri Stemmer, Chao Yan. Private Everlasting Prediction. CoRR abs/2305.09579 2023

Moritz Hardt and Michael P. Kim. “Is your model predicting the past?”; In Equity and Access in Algorithms, Mechanisms, and Optimization. EAAMO 2023

Moshe Shenfeld and Katrina Ligett. Generalization in the Face of Adaptivity: A Bayesian Perspective. 2023

Moses Charikar, Monika Henzinger, Lunjia Hu, Maximilian Vötsch, Erik Waingarten, Simple, Scalable and Effective Clustering via One-Dimensional Projections. NeurIPS 2023

N. Amit, G. N. Rothblum. Constant-Round Argument from One-Way Functions. ACM Symposium on Theory of Computing. STOC, 2023

Natalie Collina, Varun Gupta, Aaron Roth. Repeated Contracting with Multiple Non-Myopic Agents: Policy Regret and Limited Liability. FAccT 2023

O. Goldreich, G. N. Rothblum, T. Skverer. On Interactive Proofs of Proximity with Proof-Oblivious Queries. Innovations in Theoretical Computer Science Conference. ITCS 2023: 59:1-59:16.

Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya Tolstikhin. Fine-Grained Distribution-Dependent Learning Curves. COLT 2023.

Orr Paradise, Pratyusha Sharma, Shikhar Murty. Pseudointelligence: A Unifying Lens on LLM Evaluation. 2023

Osbert Bastani, Varun Gupta, Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth. Practical Adversarial Multivalid Conformal Prediction. In the Proceedings of NeurIPS 2022.

Parikshit Gopalan, Lunjia Hu, Michael P. Kim, Omer Reingold, Udi Wieder. Loss Minimization Through the Lens Of Outcome Indistinguishability. ITCS 2023: 60:1-60:20.

Parikshit Gopalan, Michael P. Kim, and Omer Reingold. “Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration”; In Proceedings of the 37th International Conference on Neural Information Processing Systems. NeurIPS 2023

Rachel Hong, Tadayohsi Kohno, and Jamie Morgenstern. Evaluation of targeted dataset collection on racial equity in face recognition. AIES 2023

Roey Magen, Moni Naor: Mirror games against an open book player. Theor. Comput. Sci. 976: 114159 (2023) (special issue of FUN 2022)

S. Hanneke, S. Kpotufe, and Y. Mahdaviyeh, Limits of Model Selection under Transfer Learning. Colt 2023.

Saachi Mutreja, Jonathan Shafer. PAC Verification of Statistical Algorithms. COLT 2023.

Saba Ahmadi, Avrim Blum, Kunhe Yang. Fundamental Bounds on Online Strategic Classification. Proc. 24th ACM Conference on Economics and Computation. EC, 2023.

Saba Ahmadi, Avrim Blum, Omar Montasser, Kevin Stangl. Certifiable (Multi)Robustness Against Patch Attacks Using ERM. ArXiv preprint, 2023.

Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum, and Keziah Naggita. Setting Fair Incentives to Maximize Improvement. FORC 2023.

Shay Moran, Hilla Schefler, Jonathan Shafer. The Bayesian Stability Zoo. NeurIPS 2023.

Snell, J., Zollo, T., Deng, Z., Pitassi, T., and Zemel, R. Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions. Proceedings International Conference on Learning Representations. ICLR, 2023.

Steve Hanneke, Shay Moran, Jonathan Shafer. A Trichotomy for Transductive Online Learning. NeurIPS 2023.

Sumegha Garg, Christopher Jung, Omer Reingold and Aaron Roth. Oracle Efficient Online Multicalibration and Omniprediction. 2023.

Sumegha Garg, Madhu Sudan and Gabriel Wu. Testing Tensor Products of Algebraic Codes, in submission, 2023.

Tal Herman, Guy N. Rothblum, Doubly-Efficient Interactive Proofs for Distribution Properties, FOCS 2023: 743-751

Travis Dick, Cynthia Dwork, Michael Kearns, Terrance Liu, Aaron Roth, Giuseppe Vietri, and Steven Wu. Confidence-Ranked Reconstruction of Census Microdata from Published Statistics. Proceedings of the National Academy of Sciences. PNAS, 2023.

Yahav Bechavod, Aaron Roth. Individually Fair Learning with One-Sided Feedback. ICML 2023.

Yeshwanth Cherapanamjeri, Constantinos Daskalakis, Andrew Ilyas, Manolis Zampetakis. What Makes A Good Fisherman? Linear Regression under Self-Selection Bias. STOC 2023.

Yoav Ben Dov, Liron David, Moni Naor, Elad Tzalik. Resistance to Timing Attacks for Sampling and Privacy Preserving Schemes. FORC 2023: 11:1-11:23.

Z. Deng, J. Zhang, L. Zhang, T. Ye, Y. Copley, W. Su, and J. Zhou. FIFA: Making Fairness more Generalizable in Classifiers Trained on Imbalanced Data. ICLR 2023.

Zhun Deng and Cynthia Dwork and Linjun Zhang. HappyMap: A Generalized Multicalibration Method. ITCS 2023.

Zhun Deng, Thomas Zollo, Jake Snell, Toniann Pitassi, Richard Zemel. Distribution-free statistical dispersion control for societal applications. NeurIPS 2023

Zollo, T. Morrill, Z. Deng, J. Snell, T. Pitassi and R. Zemel. Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models. NeurIPS SoLaR Workshop, 2023

2022

Aaron Roth. Online Multiobjective Minimax Optimization and Applications. – Manuscript. 2022

Aaron Roth. Online Multivalid Learning: Means, Moments, and Prediction Intervals. ITCS 2022

Aayush Jain, Huijia Lin, Amit Sahai. Indistinguishability Obfuscation from LPN over $F_p$, DLIN, and PRGs in $NC^0$. EUROCRYPT 2022.

Avrim Blum, Kevin Stangl, Ali Vakilian. Multi Stage Screening: Enforcing Fairness and Maximizing Efficiency in a Pre-Existing Pipeline. ACM FAccT 2022

Ayelet Gordon-Tapiero, Alexandra Wood, and Katrina Ligett. The Case for Establishing a Collective Perspective to Address the Harms of Platform Personalization. Vanderbilt Journal of Entertainment & Technology Law, Forthcoming. Also, a short version of that paper (same title and authors) appeared at ACM CSLaw 2022.

B. Laufer, S. Jain, A. F. Cooper, J. Kleinberg, H. Heidari. Four Years of FAccT: A Reflexive, Mixed-Methods Analysis of Research Contributions, Shortcomings, and Future Prospects. Proc. ACM Conference on Fairness, Accountability, and Transparency. FAccT, 2022.

Constantinos Daskalakis, Noah Golowich: Fast Rates for Nonparametric Online Learning: From Realizability to Learning in Games. STOC, 2022.

D. Fudenberg, J. Kleinberg, A. Liang, S. Mullainathan. Measuring the Completeness of Economic Models. Journal of Political Economy, 2022.

Daniel Z. Lee, Georgy Noarov Mallesh Pai, Aaron Roth. Online Multiobjective Minimax Optimization: Calibeating and Other Applications. NeurIPS 2022.

Dwork, C., Kim, M.P., Reingold, O., Rothblum, G.N. and Yona, G. Beyond Bernoulli: Generating Random Outcomes that cannot be Distinguished from Nature. In International Conference on Algorithmic Learning Theory (pp. 342-380). PMLR. 2022

Dwork, C. and Minow, M. Distrust of Artificial Intelligence: Sources & Responses from Computer Science & Law. Daedalus, 151(2), pp.309-321. 2022

Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, Saeed Sharifi-Malvajerdi. Multiaccurate Proxies for Downstream Fairness. FAccT, 2022

Ebert Du and Cynthia Dwork, Improved Generalization Guarantees in Restricted Data Models. FORC, 2022

Gal Arnon, Alessandro Chiesa, Eylon Yogev. A PCP Theorem for Interactive Proofs and Applications. EUROCRYPT (2) 2022: 64-94.

Gal Arnon, Alessandro Chiesa, Eylon Yogev. A PCP Theorem for Interactive Proofs and Applications. EUROCRYPT (2) 2022: 64-94.

Gal Arnon, Alessandro Chiesa, Eylon Yogev. Hardness of Approximation for Stochastic Problems via Interactive Oracle Proofs. CCC 2022: 24:1-24:16.

Gal Arnon, Amey Bhangale, Alessandro Chiesa, Eylon Yogev.  A Toolbox for Barriers on Interactive Oracle Proofs. TCC (1) 2022: 447-466.

Giannis Daras, Yuval Dagan, Alex Dimakis, Constantinos Daskalakis: Score-Guided Intermediate Level Optimization: Fast Langevin Mixing for Inverse Problems. ICML, 2022

Hanjun Li, Huijia Lin, Ji Luo. ABE for Circuits with Constant-Size Secret Keys and Adaptive Security. The 20th IACR Theory of Cryptography Conference, TCC 2022

Ioannis Anagnostides, Constantinos Daskalakis, Gabriele Farina, Maxwell Fishelson, Noah Golowich, Tuomas Sandholm: Near-Optimal No-Regret Learning for Correlated Equilibria in Multi-Player General-Sum Games. STOC, 2022

Ira Globus-Harris, Michael Kearns, Aaron Roth. An Algorithmic Framework for Bias Bounties. FAccT 2022

J. Kleinberg, M. Raghavan, S. Mullainathan. The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization. EC, 2022.

Jacob Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russel, Claire Zhang. Active Sampling for Min-Max Fairness. ICML 2022.

Kenji Kawaguchi, Kyle Luh, Jiaoyang Huang, Zhun Deng. An Improved Analysis of Algorithmic Robustness ICML 2022 (Selected as long presentation, top 2% among submissions).

Linjun Zhang, Zhun Deng, Kenji Kawaguchi, James Zou (* for equal contribution). When and how does Mixup help Calibration. ICML 2022.

M. Papachristou, J. Kleinberg. Allocating Stimulus Checks in Times of Crisis. Proc. 31st International World Wide Web Conference, 2022.

Micah Carroll, Orr Paradise, Jessy Lin, Raluca Georgescu, Mingfei Sun, David Bignell, Stephanie Milani, Katja Hofmann, Matthew Hausknecht, Anca Dragan, Sam Devlin . Uni[MASK]: Unified inference in sequential decision problems. NeurIPS 2022.

Nir Bitansky, Huijia Lin, Omri Shmueli. Non-malleable Commitments Against Quantum Attacks. EUROCRYPT 2022.

Parikshit Gopalan, Michael P. Kim, Mihir Singhal, Shengjia Zhao. Low-Degree Multicalibrationhttps. COLT 2022.

R. McIlroy-Young, J. Kleinberg, S. Sen, S. Barocas, A. Anderson. Mimetic Models: Ethical Implications of AI that Acts Like You Proc. AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, 2022.

Russell Impagliazzo and Rex Lei and Toniann Pitassi and Jessica. Sorrell Reproducibility in Learning. QuORAM: A Quorum-Replicated Fault Tolerant ORAM Datastore. STOC 2022

Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum, Keziah Naggita. On Classification of Strategic Agents who can both Game and Improve. FORC 2022

Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum, Keziah Naggita. Setting Fair Incentives to Maximize Improvement. CoRR abs/2203.00134, 2022.

Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian. Individual Preference Stability for Clustering. ICML 2022.

Sam Gunn, Doseok Jang, Orr Paradise, Lucas Spangher, Costas J. Spanos. Adversarial Poisoning Attacks on Reinforcement Learning-driven Energy Pricing. BuildSys, 2022

Sarah Dean and Jamie Morgenstern. Preference Dynamics Under Personalized Recommendations. EC 2022.

Shafi Goldwasser, Michael P. Kim, Vinod Vaikuntanathan, Or Zamir. Planting Undetectable Backdoors in Machine Learning Models. FOCS 2022.

Sujaya Maiyya, Seif Ibrahim, Caitlin Scarberry, Amr El Abbadi, Divyakant Agrawal, Rachel Lin, Stefano Tessaro, Victor Zakhary. Usenix Security, 2022

T. Herman and G. N. Rothblum. Verifying The Unseen: Interactive Proofs for Label-Invariant Properties. ACM Symposium on Theory of Computing, STOC 2022: 1208-1219.

Yang Cai, Constantinos Daskalakis: Recommender Systems meet Mechanism Design. EC, 2022.

Yeshwanth Cherapanamjeri, Constantinos Daskalakis, Andrew Ilyas, Manolis Zampetakis: Estimation of Standard Auction Models. EC, 2022.

Yeshwanth Cherapanamjeri, Constantinos Daskalakis, Andrew Ilyas, Manolis Zampetakis: What Makes A Good Fisherman? Linear Regression under Self-Selection Bias. ArXiv preprint 2022

Yuval Dagan, Vardis Kandiros, Constantinos Daskalakis: EM’s Convergence in Gaussian Latent Tree Models. COLT, 2022.

Zhun Deng, Robustness, Generalization, and Fairness in Learning: Analysis and Design, PhD Thesis, May, 2022.

Zhun Deng, He Sun, Steven Wu, Linjun Zhang, David Parkes, The Impact of Long-term Dynamics of Fairness: Theory and Algorithms. 2022

Zhun Deng, Jiayao Zhang, Linjun Zhang, Ting Ye, Yates Coley, Weijie Su, James Zou, FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data. Neurips 2022

2021

Aayush Jain, Huijia Lin, Amit Sahai. Indistinguishability Obfuscation from Well-Founded Assumptions. STOC 2021. Best Paper Award.

Alex B. Grilo, Huijia Lin, Fang Song, Vinod Vaikuntanathan. Oblivious Transfer is in MiniQCrypt. EUROCRYPT 2021. QIP 2021. Plenary Talk at QIP 2021.

Avrim Blum, Shelby Heinecke, Lev Reyzin: Communication-Aware Collaborative Learning. AAAI 2021: 6786-6793.

Avrim Blum, Paul Gölz: Incentive-Compatible Kidney Exchange in a Slightly Semi-Random Model. EC 2021: 138-156.

Avrim Blum, Nika Haghtalab, Richard Lanas Phillips, Han Shao: One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning. ICML 2021: 1005-1014.

Boaz Menuhin, Moni Naor: Keep That Card in Mind: Card Guessing with Limited Memory. Electron. Colloquium Comput. Complex. 28: 96, 2021

Burhanpurkar, Deng, Dwork, and Zhang, Scaffolding Sets, arXiv preprint arXiv:2111.03135, 2021

Christopher Jung, Changhwa Lee, Mallesh Pai, Aaron Roth, Rakesh Vohra. Moment Multicalibration for Uncertainty Estimation. COLT, 2021

Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Moshe Shenfeld:
A new analysis of differential privacy’s generalization guarantees (invited paper). STOC 2021: 9.

Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu. An Algorithmic Framework for Fairness Elicitation. FORC, 2021

Constantinos Daskalakis, Patroklos Stefanou, Rui Yao, Manolis Zampetakis: Efficient Truncated Linear Regression with Unknown Noise Variance. NeurIPS, 2021.

Constantinos Daskalakis, Stratis Skoulakis, Manolis Zampetakis: The Complexity of Constrained Min-Max Optimization. STOC, 2021

Constantinos Daskalakis, Vasilis Kontonis, Christos Tzamos, Emmanouil Zampetakis: A Statistical Taylor Theorem and Extrapolation of Truncated Densities. COLT, 2021

Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum, Gal Yona. Outcome Indistinguishability. STOC 2021 (June), and in the non-archival track. FORC, 2021.

C. Dwork and M. Minow, Trust and Distrust in Artificial Intelligence, to appear, Daedalus (Journal of the American Academy of Arts and Sciences), 2021.

C. Dwork, W. Su, and L. Zhang, Differentially Private False Discovery Rate Control, to appear, J. Privacy and Confidentiality, 2021 (in copyediting).

Emily Diana, Travis Dick, Hadi Elzayn, Michael Kearns, Aaron Roth, Zachary Schutzman, Saeed Sharifi-Malvajerdi, Juba Ziani. Algorithms and Learning for Fair Portfolio Design. EC, 2021.

Emily Diana, Wesley Gill, Ira Globus-Harris, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi. Lexicographically Fair Learning: Algorithms and Generalization. FORC, 2021.

Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani. Pipeline Interventions. ITCS, 2021.

Fabrice Benhamouda, Aayush Jain, Ilan Komargodski, Huijia Lin. Multiparty Reusable Non-Interactive Secure Computation from LWE. EUROCRYPT, 2021.

G. N. Rothblum and Gal Yona, Multi-group Agnostic PAC Learnability. ICML, 2021

Gal Vardi and Daniel Reichman and Toniann Pitassi and Ohad Shamir. Size and Depth Separation in Approximating Natural Functions with Neural Networks. COLT, 2021.

H. Heidari, J. Kleinberg. Allocating Opportunities in a Dynamic Model of Intergenerational Mobility. FAccT, 2021. Best CS Paper Award at FAccT 2021.

H. Heidari, S. Barocas, J. Kleinberg, K. Levy. On Modeling Human Perceptions of Allocation Policies with Uncertain Outcomes. EC, 2021. Exemplary Applied Modeling Award at EC 2021

J. Gaitonde, J. Kleinberg, E. Tardos. Polarization in Geometric Opinion Dynamics. EC, 2021.

J. Hofman, D. Watts, S. Athey, F. Garip, T. Griffiths, J. Kleinberg, H. Margetts, S. Mullainathan, M. Salganik, S. Vazire, A. Vespignani, T. Yarkoni. Integrating explanation and prediction in computational social science. Nature 595 (2021)

J. Kleinberg, M. Raghavan. Algorithmic Monoculture and Social Welfare. Proc. National Academy of Sciences, 118(22), 1 June 2021

James Lucas, Mengye Ren, Irene Raissa Kameni, Toniann Pitassi, Richard S. Zemel:
Theoretical bounds on estimation error for meta-learning. ICLR 2021.

Jelena Diakonikolas, Constantinos Daskalakis, Michael I. Jordan: Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization. AISTATS, 2021

K. Donahue, J. Kleinberg. Optimality and Stability in Federated Learning: A Game-theoretic Approach. NeurIPS, 2021.

Kenji Kawaguchi, Linjun Zhang, Zhun Deng, Dynamics of Learning Nonlinear Representations in Supervised Learning. 2021

Lunjia Hu, Omer Reingold. Robust Mean Estimation on Highly Incomplete Data with Arbitrary Outliers. AISTATS 2021: 1558-1566

Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Zou. How Does Mixup Help With Robustness and Generalization?  ICLR 2021 (May), Spotlight, top 6% among submissions.

Linjun Zhang, Zhun Deng, Kenji Kawaguchi, James Zou. When and How Mixup Improves Calibration. 2021

Marcel Neunhoeffer, Zhiwei Steven Wu, and Cynthia Dwork, Private post-gan boosting, ICLR 2021 (November), 2007.11934.pdf (arxiv.org) and TPDP 2020 poster.

Noga Alon, Omri Ben-Eliezer, Yuval Dagan, Shay Moran, Moni Naor, Eylon Yogev:
Adversarial laws of large numbers and optimal regret in online classification. STOC 2021: 447-455.

Ofer Grossman, Justin Holmgren. Error Correcting Codes for Uncompressed Messages. ITCS 2021.

Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder:
Multicalibrated Partitions for Importance Weights. CoRR abs/2103.05853 (2021).

Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang:
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. FAccT 2021: 206-214.

Romain Gay, Aayush Jain, Huijia Lin, Amit Sahai. Indistinguishability Obfuscation from Simple-to-State Hard Problems: New Assumptions, New Techniques, and Simplification. EUROCRYPT, 2021

Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum, Keziah Naggita: The Strategic Perceptron. EC 2021: 6-25.

Samuel B. Hopkins, Aayush Jain, Huijia Lin.Counterexamples to New Circular Security Assumptions Underlying iO. Crypto 2021

Shafi Goldwasser, G. N. Rothblum, J. Shafer and A. Yehudayoff, Interactive Proofs for Verifying Machine Learning. ITCS, 2021: 41:1-41:19.

Shafi Goldwasser, Russell Impagliazzo, Toniann Pitassi, Rahul Santhanam:
On the Pseudo-Deterministic Query Complexity of NP Search Problems. CCC 2021: 36:1-36:22.

Shweta Agrawal, Shafi Goldwasser, Saleet Mossel: Deniable Fully Homomorphic Encryption from LWE. CRYPTO 2021.

Talya Eden, Saleet Mossel, Ronitt, Rubinfeld: Sampling Multiple Edges Efficiently. RANDOM 2021.

Vikas K. Garg, Adam Tauman Kalai, Katrina Ligett, Zhiwei Steven Wu:
Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization. AISTATS, 2021: 3574-3582.

Yahav Bechavod, Katrina Ligett, Zhiwei Steven Wu, Juba Ziani:
Gaming Helps! Learning from Strategic Interactions in Natural Dynamics. AISTATS 2021: 1234-1242.

Yuval Dagan, Constantinos Daskalakis, Nishanth Dikkala, Anthimos Vardis Kandiros: Learning Ising Models from One or Multiple Samples. STOC, 2021

Yuval Dagan, Constantinos Daskalakis, Nishanth Dikkala, Surbhi Goel, Anthimos Vardis Kandiros: Statistical Estimation from Dependent Data. ICML, 2021

Zhun Deng, Hangfeng He, Weijie J. Su. Toward Better Generalization Bounds with Locally Elastic Stability. ICML, 2021

Zhun Deng, Jiaoyang Huang, Kenji Kawaguchi The Role of Gradient Noise in the Optimization of Neural Networks, manuscript. 2021

2020

Aditya Saraf, Anna R. Karlin, Jamie Morgenstern: Competition Alleviates Present Bias in Task Completion. WINE 2020: 266-279.

Alexandra Chouldechova, Aaron Roth. A snapshot of the frontiers of fairness in machine learning. Commun. ACM 63(5): 82-89. 2020

Amirata Ghorbani, Michael P. Kim, James Zou. A Distributional Framework for Data Valuation. ICML, 2020.

Andrew Ilyas, Emmanouil Zampetakis, Constantinos Daskalakis. A Theoretical and Practical Framework for Regression and Classification from Truncated Samples. AISTATS: 4463-4473. 2020

Avrim Blum, Thodoris Lykouris. Advancing Subgroup Fairness via Sleeping Experts. ITCS: 55:1-55:24. 2020

Avrim Blum, Technical perspective: Algorithm selection as a learning problem. Commun. ACM 63(6): 86, 2020

Avrim Blum, John P. Dickerson, Nika Haghtalab, Ariel D. Procaccia, Tuomas Sandholm, Ankit Sharma. Ignorance Is Almost Bliss: Near-Optimal Stochastic Matching with Few Queries. Oper. Res. 68(1): 16-34. 2020

Avrim Blum, Kevin Stangl. Recovering from Biased Data: Can Fairness Constraints Improve Accuracy? FORC: 3:1-3:20. 2020

Christopher Jung, Sampath Kannan, Changhwa Lee, Mallesh M. Pai, Aaron Roth, Rakesh Vohra. Fair Prediction with Endogenous Behavior. EC: 677-678. 2020

Cynthia Dwork, Christina Ilvento, Guy N. Rothblum, Pragya Sur. Abstracting Fairness: Oracles, Metrics, and Interpretability. FORC: 8:1-8:16, 2020.

Cynthia Dwork and C. Ilvento. Consistent Integer, Non-Negative, Hierarchical Histograms without Integer Programming. TPDP, 2020

Cynthia Dwork, Christina Ilvento, Meena Jagadeesan. Individual Fairness in Pipelines. FORC: 7:1-7:22, 2020

G. N. Rothblum and R. D. Rothblum, Batch Verification and Proofs of Proximity with Polylog Overhead. TCC, (2) 2020: 108-138.

Haim Kaplan, Katrina Ligett, Yishay Mansour, Moni Naor, Uri Stemmer:
Privately Learning Thresholds: Closing the Exponential Gap. COLT 2020: 2263-2285.

He Sun, Zhun Deng, Hui Chen, David Parkes. Decision-Aware Conditional GANs for Time Series Data. Manuscript. 2020

Kate Donahue, Jon M. Kleinberg. Fairness and utilization in allocating resources with uncertain demand. FAT*: 658-668. 2020

Kenji Kawaguchi, Linjun Zhang, Zhun Deng, Dynamics of Learning Nonlinear Representations in Supervised Learning. 2020

Margaret Mitchell, Dylan Baker, Nyalleng Moorosi, Emily Denton, Ben Hutchinson, Alex Hanna, Timnit Gebru, Jamie Morgenstern. Diversity and Inclusion Metrics in Subset Selection. AIES, 2020: 117-123.

Manish Raghavan, Solon Barocas, Jon M. Kleinberg, Karen Levy. Mitigating bias in algorithmic hiring: evaluating claims and practices. FAT*: 469-481. 2020

Michael P. Kim, Aleksandra Korolova, Guy N. Rothblum, Gal Yona. Preference-informed fairness. FAT*: 546. 2020

Michael P. Kim, Aleksandra Korolova, Guy N. Rothblum, Gal Yona. Preference-informed fairness. ITCS: 16:1-16:23. 2020

Moni Naor, Neil Vexler. Can Two Walk Together: Privacy Enhancing Methods and Preventing Tracking of Users. FORC 2020: 4:1-4:20.

N. Barda, D. Riesel, A. Akriv, J. Levy, U. Finkel, G. Yona, D. Greenfeld, S. Sheiba, J. Somer, E. Bachmat, G. N. Rothblum, U. Shalit, D. Netzer, R. Balicer and N. Dagan, Developing a COVID-19 Mortality Risk Prediction Model when Individual-Level Data are Not Available. Nature Communications 11, 4439, 2020

N. Barda, G. Yona, G. N. Rothblum, P. Greenland, M. Leibowitz, R. Balicer, E. Bachmat and N. Dagan, Addressing Bias in Prediction Models by Improving Subpopulation Calibration. JAMIA, 28(3): 549-558, 2020

Ofer Grossman, Justin Holmgren, Eylon Yogev. Transparent Error Correcting in a Computationally Bounded World. TCC, 2020

Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern. Equalized odds postprocessing under imperfect group information. AISTATS, 2020: 1770-1780.

Sanjam Garg, Shafi Goldwasser, Prashant Nalini Vasudevan. Formalizing Data Deletion in the Context of the Right to Be Forgotten. EUROCRYPT (2) 2020: 373-402

Shafi Goldwasser, Ofer Grossman, Sidhanth Mohanty, David P. Woodruff. Pseudo-Deterministic Streaming. ITCS, 2020

Shafi Goldwasser, Adam Tauman Kalai, Yael Tauman Kalai, Omar Montasser. Identifying unpredictable test examples with worst-case guarantees. ITA, 2020: 1-14.

Shafi Goldwasser, Adam Tauman Kalai, Yael Kalai, Omar Montasser. Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples. NeurIPS, 2020.

Shavit, Y., Edelman, B., & Axelrod, B. Causal strategic linear regression. In International Conference on Machine Learning (pp. 8676-8686). PMLR. Causal Strategic Linear Regression (mlr.press). 2020

Yahav Bechavod, Christopher Jung, Zhiwei Steven Wu. Metric-Free Individual Fairness in Online Learning. NeurIPS 2020.

Yang Cai, Federico Echenique, Hu Fu, Katrina Ligett, Adam Wierman, Juba Ziani:
Third-Party Data Providers Ruin Simple Mechanisms. Proc. ACM Meas. Anal. Comput. Syst. 4(1): 12:1-12:31, 2020.

Zhun Deng, Frances Ding, Cynthia Dwork, Rachel Hong, Giovanni Parmigiani, Prasad Patil, Pragya Sur. A Theoretical View of Adversarial Domain Generalization in the Hierarchical Model Setting. 2020

Zhun Deng, Cynthia Dwork, Jialiang Wang, Linjun Zhang. Interpreting Robust Optimization via Adversarial Influence Functions. ICML 2020.

Zhun Deng, Hangfeng He, Jiaoyang Huang, Weijie J. Su. Towards Understanding the Dynamics of the First-Order Adversaries.  ICML 2020.

Selected earlier publications

Amihood Amir, Oren Kapah, Tsvi Kopelowitz, Moni Naor, Ely Porat. The Family Holiday Gathering Problem or Fair and Periodic Scheduling of Independent Sets. SPAA: 367-375. 2016.

Rediet Abebe, Jon M. Kleinberg, David C. Parkes. Fair Division via Social Comparison. AAMAS: 281-289. 2017

Constantinos Daskalakis, Themis Gouleakis, Christos Tzamos, Manolis Zampetakis. Efficient Statistics, in High Dimensions, from Truncated Samples. FOCS: 639-649. 2018

Constantinos Daskalakis, Themis Gouleakis, Christos Tzamos, Manolis Zampetakis. Computationally and Statistically Efficient Truncated Regression. COLT: 955-960. 2019

Cynthia Dwork. What’s Fair? KDD: 1. 2017)

Cynthia Dwork, Christina Ilvento. Fairness Under Composition. ITCS: 33:1-33:20. 2019

Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum, Gal Yona. Learning from Outcomes: Evidence-Based Rankings. FOCS: 106-125. 2019

Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Richard S. Zemel. Fairness through awareness. ITCS: 214-226. 2012

Cynthia Dwork, Nicole Immorlica, Adam Tauman Kalai, Mark D. M. Leiserson. Decoupled Classifiers for Group-Fair and Efficient Machine Learning. FAT: 119-133. 2018

Elliot Creager and David Madras and J{\”{o}}rn{-}Henrik Jacobsen and Marissa A. Weis and Kevin Swersky and Toniann Pitassi and Richard S. Zemel. Flexibly Fair Representation Learning by Disentanglement. ICML, 2019, 9-15 June 2019, Long Beach, California, (USA). Proceedings of Machine Learning Research, volume 97, pages 1436–1445, PMLR, (2019).

Gal Yona, Guy N. Rothblum. Probably Approximately Metric-Fair Learning. ICML: 5666-5674. 2018

Geoff Pleiss, Manish Raghavan, Felix Wu, Jon M. Kleinberg, Kilian Q. Weinberger. On Fairness and Calibration. NIPS: 5680-5689. 2017

Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Zachary Schutzman. Fair Algorithms for Learning in Allocation Problems. FAT: 170-179. 2019

Jon M. Kleinberg. Inherent Trade-Offs in Algorithmic Fairness. SIGMETRICS (Abstracts): 40. 2018

Jon M. Kleinberg, Sendhil Mullainathan. Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability. EC: 807-808. 2019

Jon M. Kleinberg, Sendhil Mullainathan, Manish Raghavan. Inherent Trade-Offs in the Fair Determination of Risk Scores. ITCS: 43:1-43:23. 2017

Jon M. Kleinberg, Sigal Oren, Manish Raghavan. Planning Problems for Sophisticated Agents with Present Bias. EC: 343-360. 2016

Jon M. Kleinberg, Sigal Oren, Manish Raghavan (2017) Planning with Multiple Biases. EC: 567-584.

Jon M. Kleinberg, Manish Raghavan (2018) Selection Problems in the Presence of Implicit Bias. ITCS: 33:1-33:17.

Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan Ullman (2019) Differentially Private Fair Learning. ICML: 3000-3008.

Matthew Joseph, Michael J. Kearns, Jamie H. Morgenstern, Aaron Roth (2016) Fairness in Learning: Classic and Contextual Bandits. NIPS: 325-333.

Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth (2018) Meritocratic Fairness for Infinite and Contextual Bandits. AIES: 158-163.

Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu (2018) Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. ICML: 2569-2577.

Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu (2019) An Empirical Study of Rich Subgroup Fairness for Machine Learning. FAT: 100-109.

Michael P. Kim, Omer Reingold, Guy N. Rothblum (2018) Fairness Through Computationally-Bounded Awareness. NeurIPS: 4847-4857.

Moni Naor. (2005) On fairness in the carpool problem. J. Algorithms 55(1): 93-98.

Nicole Immorlica, Katrina Ligett, and Juba Ziani (2019) Access to Population-Level Signaling as a Source of Inequality. FAT*.

Richard S. Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, Cynthia Dwork (2013) Learning Fair Representations. ICML (3): 325-333.

Sampath Kannan, Michael J. Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Avrim Blum, Suriya Gunasekar, Thodoris Lykouris, Nati Srebro (2018) On preserving non-discrimination when combining expert advice. NeurIPS: 8386-8397 EC 2017: 369-386.

Sampath Kannan, Aaron Roth, Juba Ziani (2019) Downstream Effects of Affirmative Action. FAT: 240-248.

Saeed Sharifi-Malvajerdi, Michael J. Kearns, Aaron Roth (2019) Average Individual Fairness: Algorithms, Generalization and Experiments. NeurIPS: 8240-8249.

Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth (2017) Fairness in Reinforcement Learning. ICML: 1617-1626.

Stephen Gillen, Christopher Jung, Michael J. Kearns, Aaron Roth (2018) Online Learning with an Unknown Fairness Metric. NeurIPS: 2605-2614.

Sumegha Garg, Michael P. Kim, Omer Reingold (2019) Tracking and Improving Information in the Service of Fairness. EC: 809-824.

Úrsula Hébert-Johnson, Michael P. Kim, Omer Reingold, Guy N. Rothblum (2018) Multicalibration: Calibration for the (Computationally-Identifiable) Masses. ICML: 1944-1953.

Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Steven Z. Wu (2019) Equal Opportunity in Online Classification with Partial Feedback. NeurIPS: 8972-8982.