Michael P. Kim

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 40

Top Topics

Manifold Embedding

Extended Abstract

Machine Learning

Top Venues

Publications

Zoë Ruha Bell, Shafi Goldwasser, Michael P. Kim, Jean-Luc Watson
Certifying Private Probabilistic Mechanisms. CRYPTO (6) (2024)
Zoë Ruha Bell, Shafi Goldwasser, Michael P. Kim, Jean-Luc Watson
Certifying Private Probabilistic Mechanisms. IACR Cryptol. ePrint Arch. 2024 (2024)
Parikshit Gopalan, Lunjia Hu, Michael P. Kim, Omer Reingold, Udi Wieder
Loss Minimization Through the Lens Of Outcome Indistinguishability. ITCS (2023)
Michael P. Kim, Juan C. Perdomo
Making Decisions Under Outcome Performativity. ITCS (2023)
Parikshit Gopalan, Michael P. Kim, Omer Reingold
Characterizing notions of omniprediction via multicalibration. CoRR (2023)
Michael P. Kim, Moritz Hardt
Is Your Model Predicting the Past? EAAMO (2023)
Parikshit Gopalan, Michael P. Kim, Omer Reingold
Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration. NeurIPS (2023)
Parikshit Gopalan, Michael P. Kim, Mihir Singhal, Shengjia Zhao
Low-Degree Multicalibration. CoRR (2022)
Parikshit Gopalan, Lunjia Hu, Michael P. Kim, Omer Reingold, Udi Wieder
Loss Minimization through the Lens of Outcome Indistinguishability. CoRR (2022)
Shafi Goldwasser, Michael P. Kim, Vinod Vaikuntanathan, Or Zamir
Planting Undetectable Backdoors in Machine Learning Models. CoRR (2022)
Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum, Gal Yona
Beyond Bernoulli: Generating Random Outcomes that cannot be Distinguished from Nature. ALT (2022)
Michael P. Kim, Juan C. Perdomo
Making Decisions under Outcome Performativity. CoRR (2022)
Shafi Goldwasser, Michael P. Kim, Vinod Vaikuntanathan, Or Zamir
Planting Undetectable Backdoors in Machine Learning Models : [Extended Abstract]. FOCS (2022)
Moritz Hardt, Michael P. Kim
Backward baselines: Is your model predicting the past? CoRR (2022)
Parikshit Gopalan, Michael P. Kim, Mihir Singhal, Shengjia Zhao
Low-Degree Multicalibration. COLT (2022)
Shengjia Zhao, Michael P. Kim, Roshni Sahoo, Tengyu Ma, Stefano Ermon
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration. CoRR (2021)
Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum, Gal Yona
Outcome indistinguishability. STOC (2021)
Shengjia Zhao, Michael P. Kim, Roshni Sahoo, Tengyu Ma, Stefano Ermon
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration. NeurIPS (2021)
Michael P. Kim, Aleksandra Korolova, Guy N. Rothblum, Gal Yona
Preference-Informed Fairness. ITCS (2020)
Michael P. Kim, Aleksandra Korolova, Guy N. Rothblum, Gal Yona
Preference-informed fairness. FAT* (2020)
Amirata Ghorbani, Michael P. Kim, James Zou
A Distributional Framework For Data Valuation. ICML (2020)
Amirata Ghorbani, Michael P. Kim, James Y. Zou
A Distributional Framework for Data Valuation. CoRR (2020)
Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum, Gal Yona
Outcome Indistinguishability. Electron. Colloquium Comput. Complex. 27 (2020)
Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum, Gal Yona
Outcome Indistinguishability. CoRR (2020)
Sumegha Garg, Michael P. Kim, Omer Reingold
Tracking and Improving Information in the Service of Fairness. EC (2019)
Michael P. Kim, Aleksandra Korolova, Guy N. Rothblum, Gal Yona
Preference-Informed Fairness. CoRR (2019)
Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum, Gal Yona
Learning from Outcomes: Evidence-Based Rankings. FOCS (2019)
Michael P. Kim, Amirata Ghorbani, James Y. Zou
Multiaccuracy: Black-Box Post-Processing for Fairness in Classification. AIES (2019)
Sumegha Garg, Michael P. Kim, Omer Reingold
Tracking and Improving Information in the Service of Fairness. CoRR (2019)
Michael P. Kim, Omer Reingold, Guy N. Rothblum
Fairness Through Computationally-Bounded Awareness. CoRR (2018)
Moses Charikar, Ofir Geri, Michael P. Kim, William Kuszmaul
On Estimating Edit Distance: Alignment, Dimension Reduction, and Embeddings. CoRR (2018)
Michael P. Kim, Amirata Ghorbani, James Y. Zou
Multiaccuracy: Black-Box Post-Processing for Fairness in Classification. CoRR (2018)
Moses Charikar, Ofir Geri, Michael P. Kim, William Kuszmaul
On Estimating Edit Distance: Alignment, Dimension Reduction, and Embeddings. ICALP (2018)
Úrsula Hébert-Johnson, Michael P. Kim, Omer Reingold, Guy N. Rothblum
Multicalibration: Calibration for the (Computationally-Identifiable) Masses. ICML (2018)
Michael P. Kim, Omer Reingold, Guy N. Rothblum
Fairness Through Computationally-Bounded Awareness. NeurIPS (2018)
Michael P. Kim, Warut Suksompong, Virginia Vassilevska Williams
Who Can Win a Single-Elimination Tournament? SIAM J. Discret. Math. 31 (3) (2017)
Úrsula Hébert-Johnson, Michael P. Kim, Omer Reingold, Guy N. Rothblum
Calibration for the (Computationally-Identifiable) Masses. CoRR (2017)
Michael P. Kim, Warut Suksompong, Virginia Vassilevska Williams
Who Can Win a Single-Elimination Tournament? AAAI (2016)
Michael P. Kim, Warut Suksompong, Virginia Vassilevska Williams
Who Can Win a Single-Elimination Tournament? CoRR (2015)
Michael P. Kim, Virginia Vassilevska Williams
Fixing Tournaments for Kings, Chokers, and More. IJCAI (2015)