2025-01-14-pachal25a.md

title	booktitle	year	volume	series	month	publisher	pdf	url	openreview	abstract	layout	issn	id	tex_title	firstpage	lastpage	page	order	cycles	bibtex_editor	editor	bibtex_author	author	date	address	container-title	genre	issued	extras
GRU-M: A Joint Impute and Learn Approach for Sequential Prediction under Missing Data	Proceedings of the 16th Asian Conference on Machine Learning	2025	260	Proceedings of Machine Learning Research	0	PMLR	https://raw.githubusercontent.com/mlresearch/v260/main/assets/pachal25a/pachal25a.pdf	https://proceedings.mlr.press/v260/pachal25a.html	bo4YqR5rFt	Sequential Prediction in presence of missing data is an old research problem. Classically, researchers have tackled this by imputing data first and then building predictive models. This 2-stage process is typically prone to errors and to circumvent this, researchers have provided a variety of techniques which employ a joint impute and learn approach before prediction. Among these, Recurrent Neural Networks (RNNs) have been very popular given their natural ability to tackle sequential data efficiently. Existing state-of-art approaches either (i)do not impute (ii) do not completely factor the available information around a gap, (iii)ignore position information within a gap and so on. Our approach intelligently addresses these gaps by proposing a novel architecture which jointly imputes and learns by taking into account (i)information from either end of the gap (ii)proximity to the left/right-end of a gap (iii)the length of the gap. In context of this work, prediction means either sequence classification or forecasting. In this paper, we demonstrate the utility of the proposed architecture on forecasting tasks. We benchmark against a range of state-of-art baselines and in scenarios where data is either (a)naturally missing or (b)synthetically masked.	inproceedings	2640-3498	pachal25a	{GRU-M}: {A} Joint Impute and Learn Approach for Sequential Prediction under Missing Data	797	812	797-812	797	false	Nguyen, Vu and Lin, Hsuan-Tien	given family Vu Nguyen given family Hsuan-Tien Lin	Pachal, Soumen and Achar, Avinash and Bhutani, Nancy and Das, Akash	given family Soumen Pachal given family Avinash Achar given family Nancy Bhutani given family Akash Das	2025-01-14		Proceedings of the 16th Asian Conference on Machine Learning	inproceedings	date-parts 2025 1 14