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Found 12 papers, 8 papers with code
Covariance-Adaptive Least-Squares Algorithm for Stochastic Combinatorial Semi-Bandits
We address the problem of stochastic combinatorial semi-bandits, where a player can select from P subsets of a set containing d base items.
Sequential Counterfactual Risk Minimization
Counterfactual Risk Minimization (CRM) is a framework for dealing with the logged bandit feedback problem, where the goal is to improve a logging policy using offline data.
Nested bandits
In many online decision processes, the optimizing agent is called to choose between large numbers of alternatives with many inherent similarities; in turn, these similarities imply closely correlated losses that may confound standard discrete choice models and bandit algorithms.
Efficient Kernel UCB for Contextual Bandits
While standard methods require a O(CT^3) complexity where T is the horizon and the constant C is related to optimizing the UCB rule, we propose an efficient contextual algorithm for large-scale problems.
Counterfactual Learning of Stochastic Policies with Continuous Actions: from Models to Offline Evaluation
Counterfactual reasoning from logged data has become increasingly important for many applications such as web advertising or healthcare.
Optimistic mirror descent in saddle-point problems: Going the extra(-gradient) mile
Owing to their connection with generative adversarial networks (GANs), saddle-point problems have recently attracted considerable interest in machine learning and beyond.
Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images
Supervised deep learning algorithms have enabled significant performance gains in medical image classification tasks.
Adversarially Learned Anomaly Detection
Anomaly detection is a significant and hence well-studied problem.
Manifold regularization with GANs for semi-supervised learning
Generative Adversarial Networks are powerful generative models that are able to model the manifold of natural images.
Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile
Owing to their connection with generative adversarial networks (GANs), saddle-point problems have recently attracted considerable interest in machine learning and beyond.
Semi-Supervised Learning with GANs: Revisiting Manifold Regularization
GANS are powerful generative models that are able to model the manifold of natural images.
Efficient GAN-Based Anomaly Detection
However, few works have explored the use of GANs for the anomaly detection task.
