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LIT-1623: Infinite Latent Feature Models and the Indian Buffet Process

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StatusActive — read 2026-10-27 (NOTE-1208) in full, from the NeurIPS proceedings' own PDF; worth reading as the paper that introduces the Indian buffet process, the limit of a finite beta–Bernoulli feature model taken on left-ordered equivalence classes, and its sequential buffet, which produce the same distribution (THEORY-1043). Read it knowing that its eight pages state the limit and defer the technical details to a technical report, that it gives K+ ~ Poisson(αH_N) only through the buffet, with no count for the finite model, and that its one demonstration is a single run on 100 staged photographs, in which three of the four main features code an object's absence.
Version2
Tagsprobabilistic-modeling, mathematical-statistics
Cited as “extends” byLIT-1556 — The Indian Buffet Process: An Introduction and Review, LIT-1577 — The Infinite Factorial Hidden Markov Model, LIT-1626 — Infinite Sparse Factor Analysis and Infinite Independent Components Analysis, LIT-1627 — Stick-breaking Construction for the Indian Buffet Process
Cited as “paper” byNOTE-1208 — Griffiths and Ghahramani — Infinite latent feature models and the Indian buffet process
Cited as “source” byTHEORY-1043 — The finite feature model in which each of K features has prevalence Beta(α/K, 1) has, as K grows, a limit on equivalence classes of binary matrices that ignore the order of columns, and the Indian buffet process, in which the i-th object takes each earlier feature with probability m/i and Poisson(α/i) new ones, produces the same distribution, so its objects are exchangeable and the number of features they hold is Poisson(αH_N)

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Griffiths, T. L. & Ghahramani, Z. (2005). Infinite latent feature models and the Indian buffet process. In Y. Weiss, B. Schölkopf & J. Platt (eds.), Advances in Neural Information Processing Systems 18. MIT Press.

Read in full on 2026-10-27 (NOTE-1208). One account was drawn from it: THEORY-1043.

Why this is here

The owner asked on 2026-10-27 to “file the next readings”, the works the readings of that day named and did not register. This is the first of them for the record’s latent-feature line: the paper that introduces the Indian buffet process. The record held the process only through the authors’ review of 2011 (LIT-1556, NOTE-1172), which follows this paper and its technical report, and through the works built on it: the Markov IBP (LIT-1577), the beta process (LIT-1537), the dependent IBP (LIT-1548) and the correlated-feature model (LIT-1544). CASE-165 quotes the process’s count of features, αH_N, and its photograph demonstration (item 4) from the review; both first appear here.

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