2023-04-11-chen23h.md

title	software	abstract	section	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation	https://github.com/livreQ/IMDA	We use information-theoretic tools to derive a novel analysis of Multi-source Domain Adaptation (MDA) from the representation learning perspective. Concretely, we study joint distribution alignment for supervised MDA with few target labels and unsupervised MDA with pseudo labels, where the latter is relatively hard and less commonly studied. We further provide algorithm-dependent generalization bounds for these two settings, where the generalization is characterized by the mutual information between the parameters and the data. Then we propose a novel deep MDA algorithm, implicitly addressing the target shift through joint alignment. Finally, the mutual information bounds are extended to this algorithm providing a non-vacuous gradient-norm estimation. The proposed algorithm has comparable performance to the state-of-the-art on target-shifted MDA benchmark with improved memory efficiency.	Regular Papers	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	chen23h	0	Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation	10368	10394	10368-10394	10368	false	Chen, Qi and Marchand, Mario	given family Qi Chen given family Mario Marchand	2023-04-11		Proceedings of The 26th International Conference on Artificial Intelligence and Statistics	206	inproceedings	date-parts 2023 4 11	https://proceedings.mlr.press/v206/chen23h/chen23h.pdf