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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On Thompson Sampling with Langevin Algorithms
TL;DR: Two Markov Chain Monte Carlo methods tailored to Thompson sampling are proposed, which take advantage of both posterior concentration and a sample reuse mechanism to ensure that only a constant number of iterations and a constant amount of data is needed in each round.
Journal Article
A Lyapunov Analysis of Accelerated Methods in Optimization
Journal ArticleDOI
Conformal prediction under feedback covariate shift for biomolecular design
TL;DR: This work introduces a method to construct confidence sets for predictions in such settings, which account for the dependence between the training and test data and has finite-sample guarantees that hold for any regression model, even when it is used to choose the test-time input distribution.
Posted Content
On Identifying and Mitigating Bias in the Estimation of the COVID-19 Case Fatality Rate
TL;DR: It is shown that collection of randomized data by testing the contacts of infectious individuals regardless of the presence of symptoms would mitigate bias by limiting the covariance between diagnosis and death.
Posted Content
The Power of Batching in Multiple Hypothesis Testing
TL;DR: Algorithms for controlling the FDR when a possibly infinite sequence of batches of hypotheses is tested by repeated application of one of the most widely used offline algorithms, the Benjamini-Hochberg (BH) method or Storey's improvement of the BH method are introduced.