Fixing Tournaments for Kings, Chokers, and More. - Michael P. Kim; Virginia Vassilevska Williams (IJCAI, 2015)

Calibration for the (Computationally-Identifiable) Masses. - Úrsula Hébert-Johnson; Michael P. Kim; Omer Reingold; Guy N. Rothblum (CoRR, 2017)

Who Can Win a Single-Elimination Tournament? - Michael P. Kim; Warut Suksompong; Virginia Vassilevska Williams (SIAM J. Discret. Math., 2017)

On Estimating Edit Distance: Alignment, Dimension Reduction, and Embeddings. - Moses Charikar; Ofir Geri; Michael P. Kim; William Kuszmaul (ICALP, 2018)

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

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

Multiaccuracy: Black-Box Post-Processing for Fairness in Classification. - Michael P. Kim; Amirata Ghorbani; James Y. Zou (AIES, 2019)

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

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

A Distributional Framework For Data Valuation. - Amirata Ghorbani; Michael P. Kim; James Zou 001 (ICML, 2020)

Preference-Informed Fairness. - Michael P. Kim; Aleksandra Korolova; Guy N. Rothblum; Gal Yona (ITCS, 2020)

Outcome Indistinguishability. - Cynthia Dwork; Michael P. Kim; Omer Reingold; Guy N. Rothblum; Gal Yona (Electron. Colloquium Comput. Complex., 2020)

A complexity-theoretic perspective on fairness. - Michael P. Kim (2020)

Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration. - Shengjia Zhao; Michael P. Kim; Roshni Sahoo; Tengyu Ma 001; Stefano Ermon (NeurIPS, 2021)

mcboost: Multi-Calibration Boosting for R. - Florian Pfisterer; Christoph Kern 001; Susanne Dandl; Matthew Sun; Michael P. Kim; Bernd Bischl (J. Open Source Softw., 2021)

mcboost: Multi-Calibration Boosting for R. - Florian Pfisterer; Christoph Kern 001; Susanne Dandl; Mathew Sun; Michael P. Kim; Bernd Bischl (Zenodo, 2021)

Beyond Bernoulli: Generating Random Outcomes that cannot be Distinguished from Nature. - Cynthia Dwork; Michael P. Kim; Omer Reingold; Guy N. Rothblum; Gal Yona (ALT, 2022)

Low-Degree Multicalibration. - Parikshit Gopalan; Michael P. Kim; Mihir Singhal; Shengjia Zhao (COLT, 2022)

Planting Undetectable Backdoors in Machine Learning Models : [Extended Abstract]. - Shafi Goldwasser; Michael P. Kim; Vinod Vaikuntanathan; Or Zamir (FOCS, 2022)

Planting Undetectable Backdoors in Machine Learning Models. - Shafi Goldwasser; Michael P. Kim; Vinod Vaikuntanathan; Or Zamir (CoRR, 2022)

Backward baselines: Is your model predicting the past? - Moritz Hardt; Michael P. Kim (CoRR, 2022)

Is Your Model Predicting the Past? - Michael P. Kim; Moritz Hardt (EAAMO, 2023)

Loss Minimization Through the Lens Of Outcome Indistinguishability. - Parikshit Gopalan; Lunjia Hu; Michael P. Kim; Omer Reingold; Udi Wieder (ITCS, 2023)

Making Decisions Under Outcome Performativity. - Michael P. Kim; Juan C. Perdomo (ITCS, 2023)

Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration. - Parikshit Gopalan; Michael P. Kim; Omer Reingold (NeurIPS, 2023)

Characterizing notions of omniprediction via multicalibration. - Parikshit Gopalan; Michael P. Kim; Omer Reingold (CoRR, 2023)

Multi-CATE: Multi-Accurate Conditional Average Treatment Effect Estimation Robust to Unknown Covariate Shifts. - Christoph Kern 001; Michael P. Kim; Angela Zhou (CoRR, 2024)

Certifying Private Probabilistic Mechanisms. - Zoë Ruha Bell; Shafi Goldwasser; Michael P. Kim; Jean-Luc Watson (IACR Cryptol. ePrint Arch., 2024)

Multi-Accurate CATE is Robust to Unknown Covariate Shifts. - Christoph Kern 001; Michael P. Kim; Angela Zhou (Trans. Mach. Learn. Res., 2024)

Near-Optimal Algorithms for Omniprediction. - Princewill Okoroafor; Robert Kleinberg; Michael P. Kim (CoRR, 2025)