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Philippe Formont

philippe.formont@mila.quebec
Doctorat
Superviseur⋅e principal⋅e
Pablo Piantanida
Github
Google Scholar

Publications

Is Meta-training Really Necessary for Molecular Few-Shot Learning ?
Philippe Formont
Hugo Jeannin
Pablo Piantanida
Ismail Ben Ayed
Few-shot learning has recently attracted significant interest in drug discovery, with a recent, fast-growing literature mostly involving con… (voir plus)voluted meta-learning strategies. We revisit the more straightforward fine-tuning approach for molecular data, and propose a regularized quadratic-probe loss based on the the Mahalanobis distance. We design a dedicated block-coordinate descent optimizer, which avoid the degenerate solutions of our loss. Interestingly, our simple fine-tuning approach achieves highly competitive performances in comparison to state-of-the-art methods, while being applicable to black-box settings and removing the need for specific episodic pre-training strategies. Furthermore, we introduce a new benchmark to assess the robustness of the competing methods to domain shifts. In this setting, our fine-tuning baseline obtains consistently better results than meta-learning methods.
2024-04-02
ArXiv (prépublication)
doi.org
arxiv.org
COSMIC: Mutual Information for Task-Agnostic Summarization Evaluation
Maxime Darrin
Philippe Formont
Jackie Chi Kit Cheung
Pablo Piantanida
Assessing the quality of summarizers poses significant challenges. In response, we propose a novel task-oriented evaluation approach that as… (voir plus)sesses summarizers based on their capacity to produce summaries that are useful for downstream tasks, while preserving task outcomes. We theoretically establish a direct relationship between the resulting error probability of these tasks and the mutual information between source texts and generated summaries. We introduce
2024-02-29
ArXiv (prépublication)
doi.org
arxiv.org