M
Michael I. Jordan
Researcher at University of California, Berkeley
Publications - 1110
Citations - 241763
Michael I. Jordan is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Computer science & Inference. The author has an hindex of 176, co-authored 1016 publications receiving 216204 citations. Previous affiliations of Michael I. Jordan include Stanford University & Princeton University.
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Discriminative machine learning with structure
TL;DR: This thesis shows how to extend the discriminative learning framework to exploit different types of structure: on one hand, the structure on outputs, such as the combinatorial structure in word alignment; on the other hand, a latent variable structure on inputs,such as in text document classification.
Proceedings Article
On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification
TL;DR: In this paper, the authors adopt the viewpoint of projection robust (PR) OT, which seeks to maximize the OT cost between two measures by choosing a $k$-dimensional subspace onto which they can be projected.
Journal ArticleDOI
The asymptotics of ranking algorithms
TL;DR: In this article, a new approach to supervised ranking based on aggregation of partial preferences is presented, and a $U$-statistic-based empirical risk minimization procedure is developed.
Journal ArticleDOI
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons
TL;DR: In this paper , the authors provide a theoretical framework for reinforcement learning with human feedback, and show that when the true reward function is linear, the widely used maximum likelihood estimator (MLE) converges under both the BTL model and the Plackett-Luce (PL) model.