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Michael I. Jordan

Michael I. Jordan works on statistical machine learning, and the two papers here, from 2003 and 2009, apply probabilistic generative models to language. Both treat text as the visible output of hidden structure, either the themes running through a document collection or the alignment between words and the facts they describe, and both fit that structure without labelled examples.

The more cited paper, Latent Dirichlet Allocation (2003), written with David M. Blei and Andrew Y. Ng, introduces a three-level hierarchical Bayesian model of documents. Each document draws a mixture of topics from a Dirichlet prior, and each word is generated from one of those topics. The model gives compact representations of discrete data and, unlike earlier approaches, can assign probabilities to documents not seen during training.

The later paper, Learning Semantic Correspondences with Less Supervision (2009), with Percy Liang and Dan Klein, turns to grounded language. Given a world state stored as typed records and an unsegmented text about it, a hierarchical semi-Markov model trained with EM learns how the text splits into utterances and which records and fields each piece describes. Jordan's current affiliations are the University of California, Berkeley (since 1999) and Inria in France (since 2010); an earlier one is the Massachusetts Institute of Technology.

Michael I. Jordan has 2 papers in our library, in 2003–2009; often with Andrew Y. Ng, Dan Klein, David M. Blei.

2 papers in our library · 2003–2009

current affiliationInstitut national de recherche en sciences et technologies du numérique, Le Chesnay-Rocquencourt, France; University of California System, Oakland, United States; University of California, Berkeley, Berkeley, United States

OpenAlex · ORCID · h-index 159 · 989 works · 186 968 citations OpenAlex, 2026-10-14

2 works in our library · 2 256 citations in our library · 1 more work in our library · ⭐ Paper Star rank #38

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 61 more institutions on single bylines are not listed.

Works by year

2000s

20091 paper20031 paper

Papers

The most cited first (OpenAlex and our library).

  1. Latent Dirichlet Allocation (2003 · 28 297 citations)
    The paper addresses how to obtain compact, statistically meaningful representations of documents and other collections of discrete data.
  2. Learning Semantic Correspondences with Less Supervision (2009 · 455 citations)
    The paper addresses grounded language acquisition with very weak supervision. The input is a world state, stored as a set of typed records, and a raw, unsegmented text that describes it. The system must work out how the text splits into utterances, which records and fields are mentioned, and how text pieces align to…

Also in our library

1 more work of theirs is in our library without a page here yet; it will appear as we publish them.

Co-authors here

4 co-authors with a page, the most papers together first.

Cited by

Papers of our library cite their works 2 256 times (of the 140 884 papers whose bibliographies we read); 107 citing papers with a page here →.

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