 |
 When does a predictor know its own loss?Symposium on Foundations of Responsible Computing (FRC), 2025 |
 On Calibration in Multi-Distribution LearningConference on Fairness, Accountability and Transparency (FAccT), 2024 |
 Fair Risk Control: A Generalized Framework for Calibrating Multi-group
Fairness RisksInternational Conference on Machine Learning (ICML), 2024 |
 Cryptographic Hardness of Score EstimationNeural Information Processing Systems (NeurIPS), 2024 |
 Calibration by Distribution Matching: Trainable Kernel Calibration
MetricsNeural Information Processing Systems (NeurIPS), 2023 |
 Performative Prediction: Past and FutureStatistical Science (Statist. Sci.), 2023 |
 On the Vulnerability of Fairness Constrained Learning to Malicious NoiseInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023 |
 Loss Minimization Yields Multicalibration for Large Neural NetworksInformation Technology Convergence and Services (ITCS), 2023 |
 HappyMap: A Generalized Multi-calibration MethodInformation Technology Convergence and Services (ITCS), 2023 |
 Generative Models of Huge ObjectsCybersecurity and Cyberforensics Conference (CC), 2023 |
 A Unifying Perspective on Multi-Calibration: Game Dynamics for
Multi-Objective LearningNeural Information Processing Systems (NeurIPS), 2023 |
 Swap Agnostic Learning, or Characterizing Omniprediction via
MulticalibrationNeural Information Processing Systems (NeurIPS), 2023 |
 From Pseudorandomness to Multi-Group Fairness and BackAnnual Conference Computational Learning Theory (COLT), 2023 |
 A Unifying Theory of Distance from CalibrationSymposium on the Theory of Computing (STOC), 2022 |
 Comparative Learning: A Sample Complexity Theory for Two Hypothesis
ClassesInformation Technology Convergence and Services (ITCS), 2022 |
 On-Demand Sampling: Learning Optimally from Multiple DistributionsNeural Information Processing Systems (NeurIPS), 2022 |
 Loss Minimization through the Lens of Outcome IndistinguishabilityInformation Technology Convergence and Services (ITCS), 2022 |
 Making Decisions under Outcome PerformativityInformation Technology Convergence and Services (ITCS), 2022 |
 Omnipredictors for Constrained OptimizationInternational Conference on Machine Learning (ICML), 2022 |
 Multicalibrated Regression for Downstream FairnessAAAI/ACM Conference on AI, Ethics, and Society (AIES), 2022 |
 Reconciling Individual Probability ForecastsConference on Fairness, Accountability and Transparency (FAccT), 2022 |
 Is your model predicting the past?Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO), 2022 |
 Adversarial Scrutiny of Evidentiary Statistical SoftwareConference on Fairness, Accountability and Transparency (FAccT), 2022 |
 Fair Algorithm Design: Fair and Efficacious Machine SchedulingAlgorithmic Game Theory (AGT), 2022 |
 Decision-Making under MiscalibrationInformation Technology Convergence and Services (ITCS), 2022 |
 Metric Entropy Duality and the Sample Complexity of Outcome
IndistinguishabilityInternational Conference on Algorithmic Learning Theory (ALT), 2022 |
 Low-Degree MulticalibrationAnnual Conference Computational Learning Theory (COLT), 2022 |
 An Algorithmic Framework for Bias BountiesConference on Fairness, Accountability and Transparency (FAccT), 2022 |
