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Found 6 papers, 1 papers with code
A Correction of Pseudo Log-Likelihood Method
Pseudo log-likelihood is a type of maximum likelihood estimation (MLE) method used in various fields including contextual bandits, influence maximization of social networks, and causal bandits.
Sample-Efficient Multi-Agent RL: An Optimization Perspective
We study multi-agent reinforcement learning (MARL) for the general-sum Markov Games (MGs) under the general function approximation.
How Over-Parameterization Slows Down Gradient Descent in Matrix Sensing: The Curses of Symmetry and Initialization
This linear convergence result in the over-parameterization case is especially significant because one can apply the asymmetric parameterization to the symmetric setting to speed up from $\Omega (1/T^2)$ to linear convergence.
A General Framework for Sequential Decision-Making under Adaptivity Constraints
We take the first step in studying general sequential decision-making under two adaptivity constraints: rare policy switch and batch learning.
Combinatorial Causal Bandits without Graph Skeleton
This paper studies the CCB problem without the graph structure on binary general causal models and BGLMs.
Combinatorial Pure Exploration of Causal Bandits
The combinatorial pure exploration of causal bandits is the following online learning task: given a causal graph with unknown causal inference distributions, in each round we choose a subset of variables to intervene or do no intervention, and observe the random outcomes of all random variables, with the goal that using as few rounds as possible, we can output an intervention that gives the best (or almost best) expected outcome on the reward variable $Y$ with probability at least $1-\delta$, where $\delta$ is a given confidence level.
