---
title: "ICML 2005 — accepted papers — Elamur Research"
description: "ICML 2005 — accepted papers: 1 paper in the Elamur Research library, with our summaries, ranked by citations."
canonical: "https://research.elamur.ai/free/en/venues/international-conference-on-machine-learning/2005/"
---
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# ICML 2005 — accepted papers

ICML 2005 is the 2005 edition of the International Conference on Machine Learning (ICML). OpenAlex lists 140 papers of it; our library holds 1 with our summaries. Below: the most cited papers, every accepted paper, and ours with their topics, authors and institutions.

[**140** papers in the proceedings](#all) · [**1** work in our library](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/2005/#works) · [**2,906** citations of these works](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/2005/#works)

## Most cited papers

By their OpenAlex citations; a paper with our summary links to its page here, the others to the original.

1. **[Learning to rank using gradient descent](https://md.research.elamur.ai/free/en/papers/burges-2005-learning-rank-using-gradient/)**Chris J. C. Burges, Tal Shaked, Erin Renshaw et al.*2,757 citations · our summary*
2. **[Predicting good probabilities with supervised learning ↗](https://doi.org/10.1145/1102351.1102430)**Alexandru Niculescu-Mizil, Rich Caruana*1,766 citations*
3. **[Fast maximum margin matrix factorization for collaborative prediction ↗](https://doi.org/10.1145/1102351.1102441)**Jasson D. M. Rennie, Nathan Srebro*939 citations*
4. **[A support vector method for multivariate performance measures ↗](https://doi.org/10.1145/1102351.1102399)**Thorsten Joachims*786 citations*
5. **[Comparing clusterings ↗](https://doi.org/10.1145/1102351.1102424)**Marina Meilă*589 citations*
6. **[Non-negative tensor factorization with applications to statistics and computer vision ↗](https://doi.org/10.1145/1102351.1102451)**Amnon Shashua, Tamir Hazan*490 citations*
7. **[Near-optimal sensor placements in Gaussian processes ↗](https://doi.org/10.1145/1102351.1102385)**Carlos Guestrin, Andreas Krause, Ajit Paul Singh*454 citations*
8. **[Learning structured prediction models ↗](https://doi.org/10.1145/1102351.1102464)**Ben Taskar, Vassil Chatalbashev, Daphne Koller et al.*444 citations*
9. **[Beyond the point cloud ↗](https://doi.org/10.1145/1102351.1102455)**Vikas Sindhwani, Partha Niyogi, Mikhail Belkin*439 citations*
10. **[Learning from labeled and unlabeled data on a directed graph ↗](https://doi.org/10.1145/1102351.1102482)**Dengyong Zhou, Jiayuan Huang, Bernhard Schölkopf*403 citations*

## All accepted papers (140)

The most cited first; 1 with our summary link to their page here, the others to the original.

1. **[Learning to rank using gradient descent](https://md.research.elamur.ai/free/en/papers/burges-2005-learning-rank-using-gradient/)**Chris J. C. Burges, Tal Shaked, Erin Renshaw et al.*2,757 citations · our summary*
2. **[Predicting good probabilities with supervised learning ↗](https://doi.org/10.1145/1102351.1102430)**Alexandru Niculescu-Mizil, Rich Caruana*1,766 citations*
3. **[Fast maximum margin matrix factorization for collaborative prediction ↗](https://doi.org/10.1145/1102351.1102441)**Jasson D. M. Rennie, Nathan Srebro*939 citations*
4. **[A support vector method for multivariate performance measures ↗](https://doi.org/10.1145/1102351.1102399)**Thorsten Joachims*786 citations*
5. **[Comparing clusterings ↗](https://doi.org/10.1145/1102351.1102424)**Marina Meilă*589 citations*
6. **[Non-negative tensor factorization with applications to statistics and computer vision ↗](https://doi.org/10.1145/1102351.1102451)**Amnon Shashua, Tamir Hazan*490 citations*
7. **[Near-optimal sensor placements in Gaussian processes ↗](https://doi.org/10.1145/1102351.1102385)**Carlos Guestrin, Andreas Krause, Ajit Paul Singh*454 citations*
8. **[Learning structured prediction models ↗](https://doi.org/10.1145/1102351.1102464)**Ben Taskar, Vassil Chatalbashev, Daphne Koller et al.*444 citations*
9. **[Beyond the point cloud ↗](https://doi.org/10.1145/1102351.1102455)**Vikas Sindhwani, Partha Niyogi, Mikhail Belkin*439 citations*
10. **[Learning from labeled and unlabeled data on a directed graph ↗](https://doi.org/10.1145/1102351.1102482)**Dengyong Zhou, Jiayuan Huang, Bernhard Schölkopf*403 citations*
11. **[High speed obstacle avoidance using monocular vision and reinforcement learning ↗](https://doi.org/10.1145/1102351.1102426)**Jeff Michels, Ashutosh Saxena, Andrew Ng*394 citations*
12. **[Learning Gaussian processes from multiple tasks ↗](https://doi.org/10.1145/1102351.1102479)**Kai Yu, Volker Tresp, Anton Schwaighofer*380 citations*
13. **[Preference learning with Gaussian processes ↗](https://doi.org/10.1145/1102351.1102369)**Wei Chu, Zoubin Ghahramani*361 citations*
14. **[Bayesian hierarchical clustering ↗](https://doi.org/10.1145/1102351.1102389)**Katherine Heller, Zoubin Ghahramani*357 citations*
15. **[Reinforcement learning with Gaussian processes ↗](https://doi.org/10.1145/1102351.1102377)**Yaakov Engel, Shie Mannor, Ron Meir*335 citations*
16. **[The cross entropy method for classification ↗](https://doi.org/10.1145/1102351.1102422)**Shie Mannor, Dori Peleg A, Reuven Y. Rubinstein*312 citations*
17. **[Naive Bayes models for probability estimation ↗](https://doi.org/10.1145/1102351.1102418)**Daniel Lowd, Pedro Domingos*304 citations*
18. **[Learning the structure of Markov logic networks ↗](https://doi.org/10.1145/1102351.1102407)**Stanley Kok, Pedro Domingos*298 citations*
19. **[Modeling word burstiness using the Dirichlet distribution ↗](https://doi.org/10.1145/1102351.1102420)**Rasmus Elsborg Madsen, David Kauchak, Charles Elkan*280 citations*
20. **[New approaches to support vector ordinal regression ↗](https://doi.org/10.1145/1102351.1102370)**Wei Chu, S. Sathiya Keerthi*277 citations*
21. **[Using additive expert ensembles to cope with concept drift ↗](https://doi.org/10.1145/1102351.1102408)**J. Zico Kolter, Marcus A. Maloof*261 citations*
22. **[Identifying useful subgoals in reinforcement learning by local graph partitioning ↗](https://doi.org/10.1145/1102351.1102454)**Özgür Şimşek, Alicia P. Wolfe, Andrew G. Barto*238 citations*
23. **[Predictive low-rank decomposition for kernel methods ↗](https://doi.org/10.1145/1102351.1102356)**Francis Bach, Michael I. Jordan*238 citations*
24. **[Exploration and apprenticeship learning in reinforcement learning ↗](https://doi.org/10.1145/1102351.1102352)**Pieter Abbeel, Andrew Ng*224 citations*
25. **[Supervised versus multiple instance learning ↗](https://doi.org/10.1145/1102351.1102439)**Soumya Ray, Mark W. Craven*222 citations*

[See all 140 papers in ICML 2005 — accepted papers →](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/2005/papers/)

## Papers with our summary in our library (1)

The most cited first (OpenAlex citations plus our library's).

1. **[Learning to Rank using Gradient Descent](https://md.research.elamur.ai/free/en/papers/burges-2005-learning-rank-using-gradient/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Chris Burges, Tal Shaked, Erin Renshaw, et al.*2005 · 2,906 citations*

## Top topics

Topics of its works in our library, with the number of works on each.

[Information retrieval & search (1)](https://md.research.elamur.ai/free/en/topics/information-retrieval/)

## Fields

[Computer Science (1)](https://md.research.elamur.ai/free/en/shelves/computer-science/)

## Top authors

Authors with a page who published here most often: their works here, and the citations of those works.

[🧑‍🔬**Erin Renshaw**1 work here · 2,906 citations](https://md.research.elamur.ai/free/en/authors/erin-renshaw/)

## Top institutions

Institutions on the bylines of its works here, by the number of works.

[🏢**Microsoft** Redmond · United States 1 work here](https://md.research.elamur.ai/free/en/companies/microsoft/)

works and citations from OpenAlex and our library.

- [← previous ICML 1999](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/1999/)
- [next → ICML 2006](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/2006/)
- [↑ back to ICML](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)