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Proceedings of the 24th international conference on Machine learning
Abstract
This volume contains the papers accepted to the 24th International Conference on Machine Learning (ICML 2007), which was held at Oregon State University in Corvalis, Oregon, from June 20th to 24th, 2007. ICML is the annual conference of the International Machine Learning Society (IMLS), and provides a venue for the presentation and discussion of current research in the field of machine learning. These proceedings can also be found online at: http://www.machinelearning.org. This year there were 522 submissions to ICML. There was a very thorough review process, in which each paper was reviewed by three program committee (PC) members. Authors were able to respond to the initial reviews, and the PC members could then modify their reviews based on online discussions and the content of this author response. For the first time this year there were two discussion periods led by the senior program committee (SPC), one just before and one after the submission of author responses. At the end of the second discussion period, the SPC members gave their recommendations and provided a summary review for each of their papers. Also for the first time, authors were asked to submit a list of changes with their final accepted papers, which was checked by the SPCs to ensure that reviewer comments had been addressed. Apart from the length restrictions on papers and the compressed time frame, the review process for ICML resembles that of many journal publications. In total, 150 papers were accepted to ICML this year, including a very small number of papers which were initially conditionally accepted, yielding an overall acceptance rate of 29%. ICML attracts submissions from machine learning researchers around the globe. The 150 accepted papers this year were geographically distributed as follows: 66 papers had a first author from the US, 32 from Europe, 19 from China or Hong Kong, 11 from Canada, 6 from India, 5 each from Australia and Japan, 3 from Israel, and 1 each from Korea, Russia and Taiwan. In addition to the main program of accepted papers, which includes both a talk and poster presentation for each paper, the ICML program included 3 workshops and 8 tutorials on machine learning topics which are currently of broad interest. We were also extremely pleased to have David Heckerman (Microsoft Research), Joshua Tenenbaum (Massachussetts Institute of Technology), and Bernhard Schölkopf (Max Planck Institute for Biological Cybernetics) as the invited speakers this year. Thanks to sponsorship by the Machine Learning Journal, we were able to award a number of outstanding student paper prizes. We were fortunate this year that ICML was co-located with the International Conference on Inductive Logic Programming (ILP 2007). ICML and ILP held joint sessions on the first day of ICML 2007.
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A survey on few-shot class-incremental learning
Tian, S., Li, L., Li, W., Ran, H., Ning, X., & Tiwari, P. (2024) · Neural Networks · doi:10.1016/j.neunet.2023.10.039 · Cited by 220
Generative Artificial Intelligence in Education: Advancing Adaptive and Personalized Learning
Guettala, M., Bourekkache, S., Kazar, O., & Harous, S. (2024) · Acta Informatica Pragensia · doi:10.18267/j.aip.235 · Cited by 218
Social Simulacra: Creating Populated Prototypes for Social Computing Systems
Park, J. S., Popowski, L., Cai, C., Morris, M. R., Liang, P., & Bernstein, M. S. (2022) · Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology · doi:10.1145/3526113.3545616 · Cited by 212
Metadata
- Publisher
- ACM
- Published
- 2007-06-20
- OpenAlex
- W1583837637
- Cited by
- 11,630
- References
- 0
- Type
- paratext
- Language
- en
- Open access
- closed
About these checks
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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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
