Structure learning in polynomial time: Greedy algorithms, Bregman
information, and exponential families

Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families

10 October 2021

Goutham Rajendran

Papers citing "Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families"

11 / 11 papers shown

Title
On the Origins of Linear Representations in Large Language Models Yibo Jiang Goutham Rajendran Pradeep Ravikumar Bryon Aragam Victor Veitch 59 24 0 06 Mar 2024
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models Goutham Rajendran Simon Buchholz Bryon Aragam Bernhard Schölkopf Pradeep Ravikumar AI4CE 85 21 0 14 Feb 2024
Effective Causal Discovery under Identifiable Heteroscedastic Noise Model Naiyu Yin Tian Gao Yue Yu Qiang Ji CML 21 1 0 20 Dec 2023
An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis Goutham Rajendran Patrik Reizinger Wieland Brendel Pradeep Ravikumar CML 32 8 0 29 Nov 2023
Distributionally Robust Skeleton Learning of Discrete Bayesian Networks Yeshu Li Brian D. Ziebart OOD 21 0 0 10 Nov 2023
Heteroscedastic Causal Structure Learning Bao Duong T. Nguyen CML 11 2 0 16 Jul 2023
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing Simon Buchholz Goutham Rajendran Elan Rosenfeld Bryon Aragam Bernhard Schölkopf Pradeep Ravikumar CML 34 57 0 04 Jun 2023
Causal Structure Learning: a Combinatorial Perspective C. Squires Caroline Uhler CML 20 46 0 02 Jun 2022
Optimal estimation of Gaussian DAG models Ming Gao W. Tai Bryon Aragam 30 9 0 25 Jan 2022
Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions D. Geiger David Heckerman 114 195 0 05 May 2021
Learning Sparse Nonparametric DAGs Xun Zheng Chen Dan Bryon Aragam Pradeep Ravikumar Eric P. Xing CML 106 258 0 29 Sep 2019