Knowledge Tree
International Conference on Machine Learning
Also known as: Forty-second International Conference on Machine Learning, 42nd International Conference on Machine Learning
Facts (20)
Sources
A Survey on the Theory and Mechanism of Large Language Models arxiv.org Mar 12, 2026 16 facts
referenceThe paper 'Weak-to-strong generalization even in random feature networks, provably' is cited in section 7.3.1 of 'A Survey on the Theory and Mechanism of Large Language Models' and was presented at the Forty-second International Conference on Machine Learning.
referenceThe paper 'SFT memorizes, RL generalizes: a comparative study of foundation model post-training' was published in the Proceedings of the 42nd International Conference on Machine Learning, Vol. 267, pp. 10818–10838.
referenceThe paper 'Learning fast approximations of sparse coding' was published in the Proceedings of the 27th international conference on international conference on machine learning, pp. 399–406.
referenceThe paper 'Looped transformers as programmable computers' was published in the International Conference on Machine Learning, pp. 11398–11442.
referenceThe paper 'Do NOT think that much for 2+3=? On the overthinking of long reasoning models' was published in the Proceedings of the 42nd International Conference on Machine Learning, volume 267, pages 9487–9499, edited by A. Singh, M. Fazel, D. Hsu, S. Lacoste-Julien, F. Berkenkamp, T. Maharaj, K. Wagstaff, and J. Zhu.
referenceThe paper 'Contextures: representations from contexts' was published in the Proceedings of the 42nd International Conference on Machine Learning (Vol. 267, pp. 74318–74347) and is cited in section 4.2.1 of 'A Survey on the Theory and Mechanism of Large Language Models'.
referenceThe paper 'Memorization sinks: isolating memorization during LLM training' was published in the Proceedings of the 42nd International Conference on Machine Learning, Vol. 267, pp. 19307–19326, edited by A. Singh, M. Fazel, D. Hsu, S. Lacoste-Julien, F. Berkenkamp, T. Maharaj, K. Wagstaff, and J. Zhu.
referenceThe paper 'How contaminated is your benchmark? Measuring dataset leakage in large language models with kernel divergence' was published in the Proceedings of the 42nd International Conference on Machine Learning, edited by A. Singh, M. Fazel, D. Hsu, S. Lacoste-Julien, F. Berkenkamp, T. Maharaj, K. Wagstaff, and J.
referenceThe paper 'On zero-initialized attention: optimal prompt and gating factor estimation' was published in the Proceedings of the 42nd International Conference on Machine Learning, Proceedings of Machine Learning Research, Vol. 267, pp. 13713–13745.
referenceThe paper 'Discrepancies are virtue: weak-to-strong generalization through lens of intrinsic dimension' was published in the Forty-second International Conference on Machine Learning.
referenceThe paper 'Scaling test-time compute without verification or RL is suboptimal' was published in the Forty-second International Conference on Machine Learning and is cited in 'A Survey on the Theory and Mechanism of Large Language Models'.
referenceThe paper 'Scaling laws for reward model overoptimization' was published in the International Conference on Machine Learning, pp. 10835–10866.
referenceThe paper 'A tale of tails: model collapse as a change of scaling laws' was published in the Proceedings of the 41st International Conference on Machine Learning, pp. 11165–11197.
referenceThe paper 'On the emergence of position bias in transformers' was presented at the Forty-second International Conference on Machine Learning.
referenceThe paper 'Relating misfit to gain in weak-to-strong generalization beyond the squared loss' was presented at the Forty-second International Conference on Machine Learning.
referenceThe paper 'On the training convergence of transformers for in-context classification of gaussian mixtures' was published in the Forty-second International Conference on Machine Learning and is cited in 'A Survey on the Theory and Mechanism of Large Language Models'.
Understanding LLM Understanding skywritingspress.ca Jun 14, 2024 2 facts
referencePathak, D., Agrawal, P., Efros, A. A., & Darrell, T. (2017) authored 'Curiosity-driven exploration by self-supervised prediction', published in the International conference on machine learning (pp. 2778-2787).
referenceAbbe, E., Bengio, S., Lotfi, A., & Rizk, K. (2023) published 'Generalization on the unseen, logic reasoning and degree curriculum' in the Proceedings of the International Conference on Machine Learning (ICML 2023), pp. 31-60.
The Synergy of Symbolic and Connectionist AI in LLM-Empowered ... arxiv.org Jul 11, 2024 2 facts
referenceKelvin Guu et al. published 'Retrieval augmented language model pre-training' in the International conference on machine learning, pages 3929–3938, in 2020.
referenceStanley Kok and Pedro Domingos presented a method for learning the structure of Markov Logic Networks at the 22nd International Conference on Machine Learning in 2005.