Cited by 110Gaussian Processes and Bayesian InferenceStatistical Methods and InferenceBayesian Modeling and Causal Inference

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Predictive automatic relevance determination by expectation propagation

Qi, Yuan (Alan), Minka, Thomas P., Picard, Rosalind W., Ghahramani, Zoubin·2004·Twenty-first international conference on Machine learning - ICML '04

DOI 10.1145/1015330.1015418

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Abstract

In many real-world classification problems the input contains a large number of potentially ir-relevant features. This paper proposes a new Bayesian framework for determining the rele-vance of input features. This approach extends one of the most successful Bayesian methods for feature selection and sparse learning, known as Automatic Relevance Determination (ARD). ARD finds the relevance of features by optimiz-ing the model marginal likelihood, also known as the evidence. We show that this can lead to over-fitting. To address this problem, we propose Pre-dictive ARD based on estimating the predictive performance of the classifier. While the actual leave-one-out predictive performance is generally very costly to compute, the expectation propaga-tion (EP) algorithm proposed by Minka provides an estimate of this predictive performance as a side-effect of its iterations. We exploit this in our algorithm to do feature selection, and to select data points in a sparse Bayesian kernel classifier. Moreover, we provide two other improvements to previous algorithms, by replacing Laplace’s approximation with the generally more accurate EP, and by incorporating the fast optimization algorithm proposed by Faul and Tipping. Our experiments show that our method based on the EP estimate of predictive performance is more accurate on test data than relevance determina-tion by optimizing the evidence.

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Authors: Qi, Y. (., Minka, T. P., Picard, R. W., & Ghahramani, Z.
Affiliations: MIT Media Lab (US)
Microsoft Research (United Kingdom) (GB)
ORCID: Qi, Yuan (Alan)
Picard, Rosalind W.
Venue: Twenty-first international conference on Machine learning - ICML '04
Publisher: ACM Press
Published: 2004
Pages: 85
DOI: 10.1145/1015330.1015418
OpenAlex: W2156140776
Cited by: 110
References: 9
Citations by year: 2026: 2 · 2025: 1 · 2024: 3 · 2023: 2 · 2022: 3 · 2021: 3 · 2020: 4 · 2018: 1 · 2017: 2 · 2016: 1 · 2015: 6 · 2014: 3 · 2013: 9 · 2012: 9
FWCI: 5.83
Type: conference paper
Language: en
Open access: closed
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: International Conference on Machine Learning (publishedVersion)
Cambridge University Engineering Department Publications Database (submittedVersion)
Funders: MIT Media Lab
Gatsby Charitable Foundation
Defense Advanced Research Projects Agency
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
Keywords: automatic relevance determination, expectation propagation, feature selection, sparse Bayesian learning, overfitting, evidence, Laplace approximation

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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Predictive automatic relevance determination by expectation propagation

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Citations

Cites

Cited by

Related

Metadata

Record

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