The DOI matches its Crossref record.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Items 201–264 of 264
van der Zander, Q. E. W., Roumans, R., Kusters, C. H. J., Dehghani, N., Masclee, A. A. M., de With, P. H. N., van der Sommen, F., Snijders, C. C. P., & Schoon, E. J. (2024) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2024.06.029 · Cited by 27
Fast State Restoration in LLM Serving with HCache
Gao, S., Chen, Y., & Shu, J. (2025) · Proceedings of the Twentieth European Conference on Computer Systems · doi:10.1145/3689031.3696072 · Cited by 24
A novel framework for segmentation of small targets in medical images
Zhao, L., Wang, T., Chen, Y., Zhang, X., Tang, H., Lin, F., Li, C., Li, Q., Tan, T., Kang, D., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-94437-9 · Cited by 24
Relational memory-augmented language models
Liu, Q., Yogatama, D., & Blunsom, P. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00476 · Cited by 23
MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset
Meng, C., Trinh, L., Xu, N., & Liu, Y. (2021) · doi:10.21203/rs.3.rs-402058/v1 · Cited by 21
A comprehensive survey and taxonomy on privacy-preserving deep learning
Tran, A.-T., Luong, T.-D., & Huynh, V.-N. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127345 · Cited by 21
Gershman, S. J., Fiete, I., & Irie, K. (2025) · arXiv · doi:10.48550/arxiv.2501.02950 · Cited by 21
Thukral, M., Dhekane, S. G., Hiremath, S. K., Haresamudram, H., & Ploetz, T. (2024) · arXiv · doi:10.48550/arxiv.2405.12368 · Cited by 21
Carmichael, I., Song, A. H., Chen, R. J., Williamson, D. F. K., Chen, T. Y., & Mahmood, F. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-16434-7_38 · Cited by 20
Explainable AI in human motion: A comprehensive approach to analysis, modeling, and generation
Olivas-Padilla, B. E., Manitsaris, S., & Glushkova, A. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110418 · Cited by 19
Optimal policies for Bayesian olfactory search in turbulent flows
Heinonen, R. A., Biferale, L., Celani, A., & Vergassola, M. (2023) · Physical Review E · doi:10.1103/physreve.107.055105 · Cited by 19
Do protein language models learn phylogeny?
Tule, S., Foley, G., & Bodén, M. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf047 · Cited by 19
A comprehensive review of AI-Based detection of Arrhythmia using Electrocardiogram (ECG)
Ayyub, A., Politis, C., & Usman, M. A. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110594 · Cited by 18
Fahmy, H., Pastore, F., Briand, L., & Stifter, T. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3569935 · Cited by 17
Effective active learning in digital pathology: A case study in tumor infiltrating lymphocytes
Meirelles, A. L., Kurc, T., Saltz, J., & Teodoro, G. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106828 · Cited by 17
Accurate Conformation Sampling via Protein Structural Diffusion
Fan, J., Li, Z., Alcaide, E., Ke, G., Huang, H., & E, W. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00928 · Cited by 17
Mellot, A., Collas, A., Rodrigues, P. L. C., Engemann, D., & Gramfort, A. (2023) · Imaging Neuroscience · doi:10.1162/imag_a_00040 · Cited by 16
Multimodal fusion network for ICU patient outcome prediction
Wang, C., Yang, X., Sun, M., Gu, Y., Niu, J., & Zhang, W. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106672 · Cited by 16
Towards Robust QA Evaluation via Open LLMs
Kamalloo, E., Upadhyay, S., & Lin, J. (2024) · Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3626772.3657675 · Cited by 15
Wozniak, S., Janson, G., & Feig, M. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01682 · Cited by 14
Large language models transform biological research: from architecture to utilization
Wang, T., & Luo, Z. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4466-3 · Cited by 14
Drage, E., & Frabetti, F. (2024) · Science, Technology, & Human Values · doi:10.1177/01622439231164660 · Cited by 13
Reasoning about causality in games
Hammond, L., Fox, J., Everitt, T., Carey, R., Abate, A., & Wooldridge, M. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2023.103919 · Cited by 13
Salvatore, M., Horlacher, M., Marsico, A., Winther, O., & Andersson, R. (2023) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqad026 · Cited by 13
Reliable Text-to-SQL with Adaptive Abstention
Chen, K., Chen, Y., Koudas, N., & Yu, X. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3709719 · Cited by 12
Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology
MacDonald, S., Foley, H., Yap, M., Johnston, R. L., Steven, K., Koufariotis, L. T., Sharma, S., Wood, S., Addala, V., Pearson, J. V., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-31126-5 · Cited by 12
Data Assimilation in Chaotic Systems Using Deep Reinforcement Learning
Hammoud, M. A. E. R., Raboudi, N., Titi, E. S., Knio, O., & Hoteit, I. (2024) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2023ms004178 · Cited by 11
Automated invoice processing: Machine learning-based information extraction for long tail suppliers
Krieger, F., Drews, P., & Funk, B. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200285 · Cited by 11
Fan, X., Pan, H., Tian, A., Chung, W. K., & Shen, Y. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbac584 · Cited by 11
A novel detection fusion network for solid waste sorting
Yu, Y., Zou, S., & Yin, K. (2020) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881420941779 · Cited by 11
Reliable prediction of protein–protein binding affinity changes upon mutations with Pythia-PPI
Tao, F., Sun, J., Gao, P., Gao, G. F., & Wu, B. (2025) · National Science Review · doi:10.1093/nsr/nwaf231 · Cited by 10
Yang, M., Liu, S., Hao, T., Ma, C., Chen, H., Li, Y., Wu, C., Xie, J., Qiu, H., Li, J., et al. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102785 · Cited by 10
Fine-tuning neural network quantum states
Rende, R., Goldt, S., Becca, F., & Viteritti, L. L. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.043280 · Cited by 10
Bigger is not always better: The importance of human-scale language modeling for psycholinguistics
Wilcox, E. G., Hu, M. Y., Mueller, A., Warstadt, A., Choshen, L., Zhuang, C., Williams, A., Cotterell, R., & Linzen, T. (2025) · Journal of Memory and Language · doi:10.1016/j.jml.2025.104650 · Cited by 9
Revisiting Training-free NAS Metrics: An Efficient Training-based Method
Yang, T., Yang, L., Jin, X., & Chen, C. (2023) · 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv56688.2023.00473 · Cited by 9
Pseudo-perplexity in one fell swoop for protein fitness estimation
Kantroo, P., Wagner, G. P., & Machta, B. B. (2025) · PRX Life · doi:10.1103/zhx7-hcmm · Cited by 8
FOCUS: object-centric world models for robotic manipulation
Ferraro, S., Mazzaglia, P., Verbelen, T., & Dhoedt, B. (2025) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2025.1585386 · Cited by 9
Object-Centric Scene Representations Using Active Inference
Van de Maele, T., Verbelen, T., Mazzaglia, P., Ferraro, S., & Dhoedt, B. (2024) · Neural Computation · doi:10.1162/neco_a_01637 · Cited by 5
Designing diverse and high-performance proteins with a large language model in the loop
Gomez-Uribe, C. A., Gado, J., & Islamov, M. (2025) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1013119 · Cited by 6
Introspective perception for mobile robots
Rabiee, S., & Biswas, J. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2023.103999 · Cited by 6
Valencia, J. D., & Hendrix, D. A. (2023) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1011526 · Cited by 6
Combining Human and Machine Confidence in Truthfulness Assessment
Qu, Y., Roitero, K., Barbera, D. L., Spina, D., Mizzaro, S., & Demartini, G. (2023) · Journal of Data and Information Quality · doi:10.1145/3546916 · Cited by 6
Histopathology Slide Indexing and Search — Are We There Yet?
Shang, H. H., Nasr, M. S., Veerla, J. P., Saurav, J. R., Hajighasemi, A., Malidarreh, P., Huber, M., Moleta, C., Makker, J., & Luber, J. M. (2024) · NEJM AI · doi:10.1056/aics2300019 · Cited by 5
On edge deep learning implementation: approach to achieve 5G
Mukherje, D., & Anand, A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13712-3 · Cited by 5
Learning Program Behavioral Models from Synthesized Input–Output Pairs
Mammadov, T., Klakow, D., Koller, A., & Zeller, A. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3748720 · Cited by 5
Design and evaluation of a global workspace agent embodied in a realistic multimodal environment
Dossa, R. F. J., Arulkumaran, K., Juliani, A., Sasai, S., & Kanai, R. (2024) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2024.1352685 · Cited by 5
Predictions from deep learning propose substantial protein–carbohydrate interplay
Canner, S. W., Schnaar, R. L., & Gray, J. J. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2523342123 · Cited by 4
(2025) · doi:10.1145/3715275.3732087 · Cited by 4
AutoRAG: Grounding text and symbols
Schulz, T., Luttermann, M., & Möller, R. (2024) · KI - Künstliche Intelligenz · doi:10.1007/s13218-024-00850-z · Cited by 4
Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval
Mekonnen, K. A., Tang, Y., & de Rijke, M. (2025) · Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3726302.3730023 · Cited by 3
Source Code Plagiarism Detection Using Siamese BLSTM Network and Embedding Models
Manahi, M., Sulaiman, S., & Bakar, N. S. A. A. (2022) · Lecture Notes in Electrical Engineering · doi:10.1007/978-981-16-8515-6_31 · Cited by 3
RAG-ESM: Improving Pretrained Protein Language Models via Sequence Retrieval
Sgarbossa, D., & Bitbol, A.-F. (2025) · PRX Life · doi:10.1103/db1b-hy16 · Cited by 3
Confidence Estimation for Object Detection in Document Images
Boillet, M., Kermorvant, C., & Paquet, T. (2022) · doi:10.2139/ssrn.4109846 · Cited by 2
Towards Human-Like Emergent Communication via Utility, Informativeness, and Complexity
Tucker, M., Shah, J., Levy, R., & Zaslavsky, N. (2025) · Open Mind · doi:10.1162/opmi_a_00188 · Cited by 2
Shahiwala, S., & Rahul, D. R. (2025) · System · doi:10.1016/j.system.2025.103864 · Cited by 2
Quantitative prediction of intracellular dynamics and synaptic currents in a small neural circuit
Burghi, T. B., Schapiro, K., Ivanova, M., Wang, H., Marder, E., & O'Leary, T. (2025) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2025.1515194 · Cited by 2
Kang, G., Ge, Z., Hu, J., Zhang, X., Wang, L., & Zhan, J. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3718057.3718078 · Cited by 1
DCDN: estimating handover parameter adjusting effect with causal inference
Zhang, Y., Li, Q., Hua, X., Yu, R., Zhang, Z., Fan, X., Zhu, L., Sha, T., & Zhang, Y. (2023) · 2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall) · doi:10.1109/vtc2023-fall60731.2023.10333788 · Cited by 1
Exploring Optimal Prefetching Sizes in RaLMSpec to Enhance Retrieval-Augmented Generation Efficiency
Liu, S. (2025) · Highlights in Science, Engineering and Technology · doi:10.54097/hhff9g78
Rendering protein mutation movies with MutAmore
Weissenow, K., & Rost, B. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05610-8
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 ›
Are you an author, or do you see a problem with this page? Request a review
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.