2023
Aaron Roth, Alexander Tolbert and Scott Weinstein. Reconciling Individual Probability Forecasts. In the Proceedings of FAccT 2023.
Angelos Assos, Idan Attias, Yuval Dagan, Constantinos Daskalakis, Maxwell Fishelson. Online Learning and Solving Infinite Games with an ERM Oracle. In the 35th Annual Conference on Learning Theory, 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. Also, a short version of that paper (same title and authors) appeared at ACM CSLaw 2022.
Bar Karov, Moni Naor. New Algorithms and Applications for Risk-Limiting Audits. FORC 2023: 2:1-2:27.
C. Dwork, C. Hayes, J. Kleinberg, and M. Raghavan. Content Moderation and the Formation of Online Communities: A Theoretical Framework, in preparation.
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: to appear.
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 Z. Lee, Georgy Noarov Mallesh Pai, Aaron Roth. Online Multiobjective Minimax Optimization: Calibeating and Other Applications. In the Proceedings of NeurIPS 2022.
Davin Choo, Yuval Dagan, Constantinos Daskalakis, Anthimos-Vardis Kandiros. Learning and Testing Latent-Tree Ising Models Efficiently. In the 35th Annual Conference on Learning Theory, 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.
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.
Georgy Noarov, Aaron Roth. The Scope of Multicalibration: Characterizing Multicalibration via Property Elicitation. In the Proceedings of ICML 2023.
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.
Ido Nachum, Thomas Weinberger, Jonathan Shafer, Michael Gastpar. Fantastic Generalization Measures are Nowhere to be Found. Submitted to 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. (Also presented at 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. In the Proceedings of AIES 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.
Jamie Morgenstern. Evaluation of targeted dataset collection on racial equity in face recognition.
Jamie Morgenstern. Multi-learner risk reduction under endogenous participation dynamics.
Jamie Morgenstern. Multicalibrated Regression for Downstream Fairness.
Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu, Adam Tauman Kalai, Preetum Nakkiran. Loss Minimization Yields Multicalibration for Large Neural Networks. In submission.
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? In submission.
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? Proceedings, 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. In submission.
Katrina Ligett and Tomer Shadmy. Realizing the Dream of Decentralized Machine Learning. In preparation.
Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, and Juba Ziani. Wealth Dynamics Over Generations: Analysis and Interventions. In the Proceedings of SaTML 2023.
Lee Cohen, Saeed Sharifi-Malvajerdi, Kevin Stangl, Ali Vakilian, Juba Ziani. Sequential Strategic Screening. International Conference on Machine Learning, 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 Optimization, 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, in submission, 2023.
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. The 35th Annual Conference on Neural Information Processing Systems, NeurIPS 2022.
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. Backward Baselines: Is your model predicting the past? Under Review.
Moshe Shenfeld and Katrina Ligett. Generalization in the Face of Adaptivity: A Bayesian Perspective. In submission.
N. Amit, G. N. Rothblum. Constant-Round Argument from One-Way Functions. ACM Symposium on Theory of Computing, STOC 2023: to appear.
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. Accepted to COLT 2023.
Orr Paradise, Pratyusha Sharma, Shikhar Murty. Pseudointelligence: A Unifying Lens on LLM Evaluation. In submission.
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, Mihir Singhal, Shengjia Zhao. Low-Degree Multicalibrationhttps. COLT 2022.
Parikshit Gopalan, Michael P. Kim, Omer Reingold. Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration. Under Review.
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. Accepted to 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.
Sam Gunn, Doseok Jang, Orr Paradise, Lucas Spangher, Costas J. Spanos. Adversarial Poisoning Attacks on Reinforcement Learning-driven Energy Pricing. The 9th ACM International Conference on Systems for Energy-Efficient Built Environments, BuildSys 2022.
Shafi Goldwasser, Michael P. Kim, Vinod Vaikuntanathan, Or Zamir. Planting Undetectable Backdoors in Machine Learning Models. FOCS 2022.
Shay Moran, Hilla Schefler, Jonathan Shafer. The Bayesian Stability Zoo. Submitted to NeurIPS 2023.
Steve Hanneke, Shay Moran, Jonathan Shafer. A Trichotomy for Transductive Online Learning. Submitted to NeurIPS 2023.
Sumegha Garg, Christopher Jung, Omer Reingold and Aaron Roth. Oracle Efficient Online Multicalibration and Omniprediction, in submission, 2023.
Sumegha Garg, Madhu Sudan and Gabriel Wu. Testing Tensor Products of Algebraic Codes, in submission, 2023.
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.
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. In the Proceedings of ICML 2023.
Yeshwanth Cherapanamjeri, Constantinos Daskalakis, Andrew Ilyas, Manolis Zampetakis. What Makes A Good Fisherman? Linear Regression under Self-Selection Bias. In the 55th ACM Symposium on Theory of Computing, 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, Proceedings, ICLR 2023.
Zhun Deng and Cynthia Dwork and Linjun Zhang. HappyMap: A Generalized Multicalibration Method, Proceedings ITCS 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 $NC^0$
The 41st Annual International Conference on the Theory and Applications of Cryptographic Techniques. EUROCRYPT 2022.
Avrim Blum, Kevin Stangl, Ali Vakilian. Multi Stage Screening: Enforcing Fairness and Maximizing Efficiency in a Pre-Existing Pipeline. ACM FAccT 2022 (to appear).
Ayelet Gordon-Tapiero and Alexandra Wood, The case for establishing a collective perspective to address the harms of platform personalization, Vanderbilt Journal of Entertainment and Technology Law, Accepted for publication (to appear in 2023).
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. In the 54th ACM Symposium on Theory of Computing (STOC), 2022.
D. Fudenberg, J. Kleinberg, A. Liang, S. Mullainathan. Measuring the Completeness of Economic Models. Journal of Political Economy, 2022.
Dwork, C., Kim, M.P., Reingold, O., Rothblum, G.N. and Yona, G., 2022, March. Beyond Bernoulli: Generating Random Outcomes that cannot be Distinguished from Nature. In International Conference on Algorithmic Learning Theory (pp. 342-380). PMLR.
Dwork, C. and Minow, M., 2022. Distrust of Artificial Intelligence: Sources & Responses from Computer Science & Law. Daedalus, 151(2), pp.309-321.
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, Proceedings of the 3rd Symposium on Foundations of Responsible Computing (FORC 2022).
Giannis Daras, Yuval Dagan, Alex Dimakis, Constantinos Daskalakis: Score-Guided Intermediate Level Optimization: Fast Langevin Mixing for Inverse Problems. In the 39th International Conference on Machine Learning (ICML), 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. Proc. 23rd ACM Conference on Economics and Computation (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.
Nir Bitansky, Huijia Lin, Omri Shmueli. Non-malleable Commitments Against Quantum Attacks.
The 41st Annual International Conference on the Theory and Applications of Cryptographic Techniques. EUROCRYPT 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. STOC 2022QuORAM: A Quorum-Replicated Fault Tolerant ORAM Datastore.
Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum, Keziah Naggita. On Classification of Strategic Agents who can both Game and Improve. FORC 2022 (to appear).
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.
Samuel B. Hopkins, Aayush Jain, Huijia Lin.Counterexamples to New Circular Security Assumptions Underlying iO.
The 41st International Cryptology Conference. Crypto 2021.
Sarah Dean and Jamie Morgenstern. Preference Dynamics Under Personalized Recommendations. EC 2022.
Sujaya Maiyya, Seif Ibrahim, Caitlin Scarberry, Amr El Abbadi, Divyakant Agrawal, Rachel Lin, Stefano Tessaro, Victor Zakhary
The 31st Usenix Security Symposium (Usenix Security 2022).
Yang Cai, Constantinos Daskalakis: Recommender Systems meet Mechanism Design. In the 23rd ACM Conference on Economics and Computation (EC), 2022.
Yeshwanth Cherapanamjeri, Constantinos Daskalakis, Andrew Ilyas, Manolis Zampetakis: Estimation of Standard Auction Models. In the 23rd ACM Conference on Economics and Computation (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. In the 35th Annual Conference on Learning Theory (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 (in preparation) .
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 (to be submitted to Neurips shortly).
2021
Aayush Jain, Huijia Lin, Amit Sahai. Indistinguishability Obfuscation from Well-Founded Assumptions. The 53rd ACM Symposium on Theory of Computing (STOC 2021). Best Paper Award.
Alex B. Grilo, Huijia Lin, Fang Song, Vinod Vaikuntanathan. Oblivious Transfer is in MiniQCrypt. The 40th Annual International Conference on the Theory and Applications of Cryptographic Techniques (EUROCRYPT 2021). 24th Annual Conference on Quantum Information Processing (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. In the 35th Annual Conference on Neural Information Processing Systems (NeurIPS), 2021.
Constantinos Daskalakis, Stratis Skoulakis, Manolis Zampetakis: The Complexity of Constrained Min-Max Optimization.
In the 53rd ACM Symposium on Theory of Computing (STOC), 2021.
Constantinos Daskalakis, Vasilis Kontonis, Christos Tzamos, Emmanouil Zampetakis: A Statistical Taylor Theorem and Extrapolation of Truncated Densities. In the 34th Annual Conference on Learning Theory (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 of the Foundations of Responsible Computing conference, 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).
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. Proceedings of the 36th International Conference on Machine Learning,{ICML} 2019, 9-15 June 2019, Long Beach, California, (USA). Proceedings of Machine Learning Research, volume 97, pages 1436–1445, PMLR, (2019).
Elliot Creager and David Madras and Toniann Pitassi and Richard S. Zemel. Causal Modeling for Fairness In Dynamical Systems. Proceedings of the 37th International Conference on Machine Learning, (ICML) 2020, 13-18 July 2020, Virtual Event, Proceedings of Machine Learning Research, volume 119, pages 2185–2195, PMLR (2020).
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. The 40th Annual International Conference on the Theory and Applications of Cryptographic Techniques (EUROCRYPT 2021).
G. N. Rothblum and Gal Yona, Multi-group Agnostic PAC Learnability. International Conference on Machine Learning (ICML) 2021: to appear.
Gal Vardi and Daniel Reichman and Toniann Pitassi and Ohad Shamir. Size and Depth Separation in Approximating Natural Functions with Neural Networks. Conference on Computational Learning Theory, COLT. 2021.
H. Heidari, J. Kleinberg. Allocating Opportunities in a Dynamic Model of Intergenerational Mobility. Proc. ACM Conference on Fairness, Accountability, and Transparency (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. Proc. 22nd ACM Conference on Economics and Computation (EC), 2021. Exemplary Applied Modeling Award at EC 2021.
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. In the 54th ACM Symposium on Theory of Computing (STOC), 2022.
J. Gaitonde, J. Kleinberg, E. Tardos. Polarization in Geometric Opinion Dynamics. Proc. 22nd ACM Conference on Economics and Computation (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. In the 24th International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.
K. Donahue, J. Kleinberg. Optimality and Stability in Federated Learning: A Game-theoretic Approach. Advances in Neural Information Processing Systems (NeurIPS) 35, 2021.
Kenji Kawaguchi, Linjun Zhang, Zhun Deng, Dynamics of Learning Nonlinear Representations in Supervised Learning, in submission.
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. In submission.
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. The 40th Annual International Conference on the Theory and Applications of Cryptographic Techniques (EUROCRYPT 2021).
Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum, Keziah Naggita:
The Strategic Perceptron. EC 2021: 6-25.
Shafi Goldwasser, G. N. Rothblum, J. Shafer and A. Yehudayoff, Interactive Proofs for Verifying Machine Learning. Innovations in Theoretical Computer Science conference (ITCS) 2021: 41:1-41:19.
Shafi Goldwasser, Russell Impagliazzo, Toniann Pitassi, Rahul Santhanam:
On the Pseudo-Deterministic Query Complexity of NP Search Problems.Computational Complexity Conference 2021: 36:1-36:22.
Shweta Agrawal, Shafi Goldwasser, Saleet Mossel: Deniable Fully Homomorphic Encryption from LWE will be published in CRYPTO 2021.
Talya Eden, Saleet Mossel, Ronitt, Rubinfeld: Sampling Multiple Edges Efficiently. Will be published in APPROX/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. In the 53rd ACM Symposium on Theory of Computing (STOC), 2021.
Yuval Dagan, Constantinos Daskalakis, Nishanth Dikkala, Surbhi Goel, Anthimos Vardis Kandiros: Statistical Estimation from Dependent Data. In the 34th Annual Conference on Learning Theory (ICML), 2021.
Zhun Deng, Hangfeng He, Weijie J. Su. Toward Better Generalization Bounds with Locally Elastic Stability. ICML 2021 (April).
Zhun Deng, Jiaoyang Huang, Kenji Kawaguchi The Role of Gradient Noise in the Optimization of Neural Networks, manuscript.
2020
Aditya Saraf, Anna R. Karlin, Jamie Morgenstern:
Competition Alleviates Present Bias in Task Completion.WINE 2020: 266-279.
Alexandra Chouldechova, Aaron Roth (2020) A snapshot of the frontiers of fairness in machine learning. Commun. ACM 63(5): 82-89.
Amirata Ghorbani, Michael P. Kim, James Zou. A Distributional Framework for Data Valuation. ICML 2020.
Andrew Ilyas, Emmanouil Zampetakis, Constantinos Daskalakis (2020) A Theoretical and Practical Framework for Regression and Classification from Truncated Samples. AISTATS: 4463-4473.
Avrim Blum, Thodoris Lykouris (2020). Advancing Subgroup Fairness via Sleeping Experts. ITCS: 55:1-55:24.
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 (2020). Ignorance Is Almost Bliss: Near-Optimal Stochastic Matching with Few Queries. Oper. Res. 68(1): 16-34.
Avrim Blum, Kevin Stangl (2020). Recovering from Biased Data: Can Fairness Constraints Improve Accuracy? FORC: 3:1-3:20.
Christopher Jung, Sampath Kannan, Changhwa Lee, Mallesh M. Pai, Aaron Roth, Rakesh Vohra (2020) Fair Prediction with Endogenous Behavior. EC: 677-678.
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 (poster), November 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. Theory of Cryptography Conference (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.
Kate Donahue, Jon M. Kleinberg (2020) Fairness and utilization in allocating resources with uncertain demand. FAT*: 658-668.
Kenji Kawaguchi, Linjun Zhang, Zhun Deng, Dynamics of Learning Nonlinear Representations in Supervised Learning, in submission.
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 (2020) Mitigating bias in algorithmic hiring: evaluating claims and practices. FAT*: 469-481.
Michael P. Kim, Aleksandra Korolova, Guy N. Rothblum, Gal Yona (2020) Preference-informed fairness. FAT*: 546.
Michael P. Kim, Aleksandra Korolova, Guy N. Rothblum, Gal Yona (2020) Preference-informed fairness. ITCS: 16:1-16:23.
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. Journal of the American Medical Informatics Association (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. (2020, November). Causal strategic linear regression. In International Conference on Machine Learning (pp. 8676-8686). PMLR. Causal Strategic Linear Regression (mlr.press).
Yahav Bechavod, Christopher Jung, Zhiwei Steven Wu (2020) 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. In submission.
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 (2016) The Family Holiday Gathering Problem or Fair and Periodic Scheduling of Independent Sets. SPAA: 367-375.
Rediet Abebe, Jon M. Kleinberg, David C. Parkes (2017) Fair Division via Social Comparison. AAMAS: 281-289.
Constantinos Daskalakis, Themis Gouleakis, Christos Tzamos, Manolis Zampetakis (2018) Efficient Statistics, in High Dimensions, from Truncated Samples. FOCS: 639-649.
Constantinos Daskalakis, Themis Gouleakis, Christos Tzamos, Manolis Zampetakis (2019) Computationally and Statistically Efficient Truncated Regression. COLT: 955-960.
Cynthia Dwork (2017) What’s Fair? KDD: 1.
Cynthia Dwork, Christina Ilvento (2019) Fairness Under Composition. ITCS: 33:1-33:20.
Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum, Gal Yona (2019) Learning from Outcomes: Evidence-Based Rankings. FOCS: 106-125.
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Richard S. Zemel (2012) Fairness through awareness. ITCS: 214-226.
Cynthia Dwork, Nicole Immorlica, Adam Tauman Kalai, Mark D. M. Leiserson (2018) Decoupled Classifiers for Group-Fair and Efficient Machine Learning. FAT: 119-133.
Gal Yona, Guy N. Rothblum (2018) Probably Approximately Metric-Fair Learning. ICML: 5666-5674.
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon M. Kleinberg, Kilian Q. Weinberger (2017) On Fairness and Calibration. NIPS: 5680-5689.
Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Zachary Schutzman (2019) Fair Algorithms for Learning in Allocation Problems. FAT: 170-179.
Jon M. Kleinberg (2018) Inherent Trade-Offs in Algorithmic Fairness. SIGMETRICS (Abstracts): 40.
Jon M. Kleinberg, Sendhil Mullainathan (2019) Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability. EC: 807-808.
Jon M. Kleinberg, Sendhil Mullainathan, Manish Raghavan (2017) Inherent Trade-Offs in the Fair Determination of Risk Scores. ITCS: 43:1-43:23.
Jon M. Kleinberg, Sigal Oren, Manish Raghavan (2016) Planning Problems for Sophisticated Agents with Present Bias. EC: 343-360.
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.