Zobrazeno 1 - 10
of 1 796
pro vyhledávání: '"Tajer, A."'
Autor:
Yan, Zirui, Tajer, Ali
Designing causal bandit algorithms depends on two central categories of assumptions: (i) the extent of information about the underlying causal graphs and (ii) the extent of information about interventional statistical models. There have been extensiv
Externí odkaz:
http://arxiv.org/abs/2411.02383
Causal inference provides an analytical framework to identify and quantify cause-and-effect relationships among a network of interacting agents. This paper offers a novel framework for analyzing cascading failures in power transmission networks. This
Externí odkaz:
http://arxiv.org/abs/2410.19179
Autor:
Mukherjee, Arpan, Ubaru, Shashanka, Murugesan, Keerthiram, Shanmugam, Karthikeyan, Tajer, Ali
This paper considers the problem of combinatorial multi-armed bandits with semi-bandit feedback and a cardinality constraint on the super-arm size. Existing algorithms for solving this problem typically involve two key sub-routines: (1) a parameter e
Externí odkaz:
http://arxiv.org/abs/2410.10679
Causal interactions among a group of variables are often modeled by a single causal graph. In some domains, however, these interactions are best described by multiple co-existing causal graphs, e.g., in dynamical systems or genomics. This paper addre
Externí odkaz:
http://arxiv.org/abs/2406.08666
Despite the multifaceted recent advances in interventional causal representation learning (CRL), they primarily focus on the stylized assumption of single-node interventions. This assumption is not valid in a wide range of applications, and generally
Externí odkaz:
http://arxiv.org/abs/2406.05937
This paper investigates the robustness of causal bandits (CBs) in the face of temporal model fluctuations. This setting deviates from the existing literature's widely-adopted assumption of constant causal models. The focus is on causal systems with l
Externí odkaz:
http://arxiv.org/abs/2405.07795
This paper considers the problem of sequentially detecting a change in the joint distribution of multiple data sources under a sampling constraint. Specifically, the channels or sources generate observations that are independent over time, but not ne
Externí odkaz:
http://arxiv.org/abs/2403.16297
This paper considers causal bandits (CBs) for the sequential design of interventions in a causal system. The objective is to optimize a reward function via minimizing a measure of cumulative regret with respect to the best sequence of interventions i
Externí odkaz:
http://arxiv.org/abs/2403.00233
This paper addresses intervention-based causal representation learning (CRL) under a general nonparametric latent causal model and an unknown transformation that maps the latent variables to the observed variables. Linear and general transformations
Externí odkaz:
http://arxiv.org/abs/2402.00849
This paper considers the sequential design of remedial control actions in response to system anomalies for the ultimate objective of preventing blackouts. A physics-guided reinforcement learning (RL) framework is designed to identify effective sequen
Externí odkaz:
http://arxiv.org/abs/2401.09640