Search Results for author:
Found 9 papers, 3 papers with code
From Bandits Model to Deep Deterministic Policy Gradient, Reinforcement Learning with Contextual Information
The problem of how to take the right actions to make profits in sequential process continues to be difficult due to the quick dynamics and a significant amount of uncertainty in many application scenarios.
Optimizing Trading Strategies in Quantitative Markets using Multi-Agent Reinforcement Learning
Quantitative markets are characterized by swift dynamics and abundant uncertainties, making the pursuit of profit-driven stock trading actions inherently challenging.
Thompson Sampling on Asymmetric $α$-Stable Bandits
In algorithm optimization in reinforcement learning, how to deal with the exploration-exploitation dilemma is particularly important.
Adaptive Sign Algorithm for Graph Signal Processing
Recently introduced graph adaptive least mean squares algorithm is unstable under non-Gaussian impulsive noise and has high computational complexity.
PAC-Bayes Information Bottleneck
Then, we build an IIW-based information bottleneck on the trade-off between accuracy and information complexity of NNs, namely PIB.
Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback
Counterfactual learning for dealing with missing-not-at-random data (MNAR) is an intriguing topic in the recommendation literature since MNAR data are ubiquitous in modern recommender systems.
A Generalized Gaussian Extension to the Rician Distribution for SAR Image Modeling
In this paper, we present a novel statistical model, $\textit{the generalized-Gaussian-Rician}$ (GG-Rician) distribution, for the characterization of synthetic aperture radar (SAR) images.
Black-Box Decision based Adversarial Attack with Symmetric $α$-stable Distribution
Many existing methods employ Gaussian random variables for exploring the data space to find the most adversarial (for attacking) or least adversarial (for defense) point.
Stable Graphical Models
Stable random variables are motivated by the central limit theorem for densities with (potentially) unbounded variance and can be thought of as natural generalizations of the Gaussian distribution to skewed and heavy-tailed phenomenon.
