2024-04-18-arya24b.md

title	software	abstract	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
Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models	https://github.com/Shivvrat/SS-CMPE	We propose a self-supervised learning approach for solving the following constrained optimization task in log-linear models or Markov networks. Let $f$ and $g$ be two log-linear models defined over the sets $X$ and $Y$ of random variables. Given an assignment $x$ to all variables in $X$ (evidence or observations) and a real number $q$, the constrained most-probable explanation (CMPE) task seeks to find an assignment $y$ to all variables in $Y$ such that $f(x, y)$ is maximized and $g(x, y) \leq q$. In our proposed self-supervised approach, given assignments $x$ to $X$ (data), we train a deep neural network that learns to output near-optimal solutions to the CMPE problem without requiring access to any pre-computed solutions. The key idea in our approach is to use first principles and approximate inference methods for CMPE to derive novel loss functions that seek to push infeasible solutions towards feasible ones and feasible solutions towards optimal ones. We analyze the properties of our proposed method and experimentally demonstrate its efficacy on several benchmark problems.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	arya24b	0	Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models	2791	2799	2791-2799	2791	false	Arya, Shivvrat and Rahman, Tahrima and Gogate, Vibhav	given family Shivvrat Arya given family Tahrima Rahman given family Vibhav Gogate	2024-04-18		Proceedings of The 27th International Conference on Artificial Intelligence and Statistics	238	inproceedings	date-parts 2024 4 18	https://proceedings.mlr.press/v238/arya24b/arya24b.pdf