In reinforcement learning and deep learning, softmax parameterization is commonly used to represent discrete probability distributions.In this work, we study three possible softmax …
We present a unified likelihood ratio-based confidence sequence (CS) for any (self-concordant) generalized linear model (GLM) that is guaranteed to be convex and numerically tight. …
Logistic bandit is a ubiquitous framework of modeling users' choices, e.g., click vs. no click for advertisement recommender system. We observe that the prior works overlook or …
We show that a simple trick of randomly corrupting the trajectories in Block MDPs allow for us to use the the clustering algorithm proposed of Jedra et al. (2023) for general …
Linear softmax parametrization (LSP) of a discrete probability distribution is ubiquitous in many areas, such as deep learning, RL, NLP, and social choice models. Instead of trying …
Proposes a framework for performing fair PCA in memory limited, streaming setting. Sample complexity results and empirical discussions show the superiority of our approach compared …
A novel problem setting where heterogeneous multi-agent bandits collaborate over a network to minimize their group regret. To deal with the high communication complexity of the …
First theoretical analysis of model estimation and reward-free RL of block MDP, without resorting to function approximation frameworks. Lower bounds and algorithms with …
We empirically validate the clustering algorithm proposed in (Jedra et al., 2022).
Inspired from (Wang et al., ICLR'22), we provide a preliminary statistical analysis of stochastic gradient noises (SGNs) of GIN and GCN in Cora node classification task.