Search Results for author:
Found 9 papers, 2 papers with code
A Doubly Robust Approach to Sparse Reinforcement Learning
We propose a new regret minimization algorithm for episodic sparse linear Markov decision process (SMDP) where the state-transition distribution is a linear function of observed features.
Pareto Front Identification with Regret Minimization
The sample complexity of our proposed algorithm is $\tilde{O}(d/\Delta^2)$, where $d$ is the dimension of contexts and $\Delta$ is a measure of problem complexity.
Improved Algorithms for Multi-period Multi-class Packing Problems with Bandit Feedback
We consider the linear contextual multi-class multi-period packing problem (LMMP) where the goal is to pack items such that the total vector of consumption is below a given budget vector and the total value is as large as possible.
Double Doubly Robust Thompson Sampling for Generalized Linear Contextual Bandits
We propose a novel contextual bandit algorithm for generalized linear rewards with an $\tilde{O}(\sqrt{\kappa^{-1} \phi T})$ regret over $T$ rounds where $\phi$ is the minimum eigenvalue of the covariance of contexts and $\kappa$ is a lower bound of the variance of rewards.
Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits
We propose a linear contextual bandit algorithm with $O(\sqrt{dT\log T})$ regret bound, where $d$ is the dimension of contexts and $T$ isthe time horizon.
Doubly Robust Thompson Sampling with Linear Payoffs
A challenging aspect of the bandit problem is that a stochastic reward is observed only for the chosen arm and the rewards of other arms remain missing.
Doubly robust Thompson sampling for linear payoffs
A challenging aspect of the bandit problem is that a stochastic reward is observed only for the chosen arm and the rewards of other arms remain missing.
Principled learning method for Wasserstein distributionally robust optimization with local perturbations
We show that our approximation and risk consistency results naturally extend to the cases when data are locally perturbed.
Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric
We consider the problem of learning a binary classifier from only positive and unlabeled observations (called PU learning).
