Search Results for author:
Found 14 papers, 5 papers with code
Doubly-Robust Off-Policy Evaluation with Estimated Logging Policy
We introduce a novel doubly-robust (DR) off-policy evaluation (OPE) estimator for Markov decision processes, DRUnknown, designed for situations where both the logging policy and the value function are unknown.
Wasserstein Geodesic Generator for Conditional Distributions
The conditional distributions given unobserved intermediate domains are on the Wasserstein geodesic between conditional distributions given two observed domain labels.
Bandit-supported care planning for older people with complex health and care needs
Long-term care service for old people is in great demand in most of the aging societies.
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.
Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization
Non-stationarity is ubiquitous in human behavior and addressing it in the contextual bandits is challenging.
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.
Kernel-convoluted Deep Neural Networks with Data Augmentation
The Mixup method (Zhang et al. 2018), which uses linearly interpolated data, has emerged as an effective data augmentation tool to improve generalization performance and the robustness to adversarial examples.
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.
Doubly-Robust Lasso Bandit
Contextual multi-armed bandit algorithms are widely used in sequential decision tasks such as news article recommendation systems, web page ad placement algorithms, and mobile health.
Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model
We prove that the high-probability upper bound of the regret incurred by the proposed algorithm has the same order as the Thompson sampling algorithm for linear reward models.
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).
Uncertainty quantification using Bayesian neural networks in classification: Application to ischemic stroke lesion segmentation
Most recent research of neural networks in the field of computer vision has focused on improving accuracy of point predictions by developing various network architectures or learning algorithms.
General Classification
Ischemic Stroke Lesion Segmentation
+2
