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Found 12 papers, 1 papers with code
Selective Reviews of Bandit Problems in AI via a Statistical View
Reinforcement Learning (RL) is a widely researched area in artificial intelligence that focuses on teaching agents decision-making through interactions with their environment.
Zero-Inflated Bandits
Many real-world bandit applications are characterized by sparse rewards, which can significantly hinder learning efficiency.
Tight Non-asymptotic Inference via Sub-Gaussian Intrinsic Moment Norm
In non-asymptotic learning, variance-type parameters of sub-Gaussian distributions are of paramount importance.
Multiplier Bootstrap-based Exploration
Despite the great interest in the bandit problem, designing efficient algorithms for complex models remains challenging, as there is typically no analytical way to quantify uncertainty.
High-dimensional Inference and FDR Control for Simulated Markov Random Fields
Identifying important features linked to a response variable is a fundamental task in various scientific domains.
Asymptotic in a class of network models with an increasing sub-Gamma degree sequence
For the differential privacy under the sub-Gamma noise, we derive the asymptotic properties of a class of network models with binary values with a general link function.
Smooth Tests for Normality in ANOVA
In this article, we propose an easy-to-use method to testing the normality assumption in ANOVA models by using smooth tests.
Nonparametric Tests of Conditional Independence for Time Series
We propose consistent nonparametric tests of conditional independence for time series data.
Heterogeneous Overdispersed Count Data Regressions via Double Penalized Estimations
This paper studies the non-asymptotic merits of the double $\ell_1$-regularized for heterogeneous overdispersed count data via negative binomial regressions.
Sharper Sub-Weibull Concentrations
Constant-specified and exponential concentration inequalities play an essential role in the finite-sample theory of machine learning and high-dimensional statistics area.
2-Step Sparse-View CT Reconstruction with a Domain-Specific Perceptual Network
Computed tomography is widely used to examine internal structures in a non-destructive manner.
Sparse Density Estimation with Measurement Errors
It shows that our method has potency and superiority of detecting the shape of multi-mode density compared with other conventional approaches.
