26 citations · 30 across the 5 of their papers we have counts for
9 papers · 1 filter
Publicly-Verifiable Certificates for Statistical Algorithms
Michael Ngo, Michael P. Kim
Following Goldwasser, Rothblum, Shafer, and Yehudayoff, who defined a framework for interactive proofs of learning [ITCS'21], we initiate the study of non-interactive proofs of lea…
Oracle-efficient Hybrid Learning with Constrained Adversaries
Princewill Okoroafor, Robert Kleinberg, Michael P. Kim
The Hybrid Online Learning Problem, where features are drawn i.i.d. from an unknown distribution but labels are generated adversarially, is a well-motivated setting positioned betw…
Loss Minimization through the Lens of Outcome Indistinguishability
Parikshit Gopalan, Lunjia Hu, Michael P. Kim +2
We present a new perspective on loss minimization and the recent notion of Omniprediction through the lens of Outcome Indistingusihability. For a collection of losses and hypothesi…
Outcome Indistinguishability
Cynthia Dwork, Michael P. Kim, Omer Reingold +2
Prediction algorithms assign numbers to individuals that are popularly understood as individual "probabilities" -- what is the probability of 5-year survival after cancer diagnosis…
A Distributional Framework for Data Valuation
Amirata Ghorbani, Michael P. Kim, James Zou
Shapley value is a classic notion from game theory, historically used to quantify the contributions of individuals within groups, and more recently applied to assign values to data…
Tracking and Improving Information in the Service of Fairness
Sumegha Garg, Michael P. Kim, Omer Reingold
As algorithmic prediction systems have become widespread, fears that these systems may inadvertently discriminate against members of underrepresented populations have grown. With t…