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Machine unlearning is concerned with the task of removing knowledge learned from particular data points from a trained model. In the context of large language models (LLMs), unlearning has recently re...

Keynote Speaker Announcement 🔊 30.07.2025We are delighted to announce the keynote speech t`hat will happen at the special session!Speaker: Prof. Karen Livescu, Toyota Technological Institute at Ch...

As the capabilities of large-scale pre-trained models evolve, understanding the determinants of their outputs becomes more important. Feature attribution aims to reveal which parts of the input elemen...

Though English sentences are typically inflexible vis-à-vis word order, constituents often show far more variability in ordering. One prominent theory presents the notion that constituent ordering is ...

ICML 2025 - Vancouver

Introduction Audio and speech technology has recently achieved unprecedented success in real-world applications, driven primarily by self-supervised pre-training of large neural networks on massive da...