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Found 17 papers, 1 papers with code
Rate-Optimal Contextual Online Matching Bandit
Existing works focus on multi-armed bandit with static preference, but this is insufficient: the two-sided preference changes as along as one-side's contextual information updates, resulting in non-static matching.
Distribution-free Contextual Dynamic Pricing
The customer's valuation for the product is a linear function of contexts, including product and customer features, plus some random market noise.
Online Bootstrap Inference For Policy Evaluation in Reinforcement Learning
The recent emergence of reinforcement learning has created a demand for robust statistical inference methods for the parameter estimates computed using these algorithms.
Jointly Modeling and Clustering Tensors in High Dimensions
We consider the problem of jointly modeling and clustering populations of tensors by introducing a high-dimensional tensor mixture model with heterogeneous covariances.
Covariate-assisted Sparse Tensor Completion
An important by-product is that ad latent components from COSTCO reveal interesting ad clusters, which are useful for better ad targeting.
Stochastic Low-rank Tensor Bandits for Multi-dimensional Online Decision Making
We consider two settings, tensor bandits without context and tensor bandits with context.
Online Regularization towards Always-Valid High-Dimensional Dynamic Pricing
In this paper, we propose a novel approach for designing dynamic pricing policy based regularized online statistical learning with theoretical guarantees.
Partially Observed Dynamic Tensor Response Regression
In this article, we develop a regression model with partially observed dynamic tensor as the response and external covariates as the predictor.
Sparse Tensor Additive Regression
Tensors are becoming prevalent in modern applications such as medical imaging and digital marketing.
Provable Convex Co-clustering of Tensors
Our convex co-clustering (CoCo) estimator enjoys stability guarantees and its computational and storage costs are polynomial in the size of the data.
Dynamic Tensor Clustering
Existing tensor clustering methods either fail to account for the dynamic nature of the data, or are inapplicable to a general-order tensor.
Stability Enhanced Large-Margin Classifier Selection
Stability is an important aspect of a classification procedure because unstable predictions can potentially reduce users' trust in a classification system and also harm the reproducibility of scientific conclusions.
Simultaneous Clustering and Estimation of Heterogeneous Graphical Models
We consider joint estimation of multiple graphical models arising from heterogeneous and high-dimensional observations.
Tensor Graphical Model: Non-convex Optimization and Statistical Inference
We consider the estimation and inference of graphical models that characterize the dependency structure of high-dimensional tensor-valued data.
STORE: Sparse Tensor Response Regression and Neuroimaging Analysis
Motivated by applications in neuroimaging analysis, we propose a new regression model, Sparse TensOr REsponse regression (STORE), with a tensor response and a vector predictor.
Sparse Convex Clustering
Convex clustering, a convex relaxation of k-means clustering and hierarchical clustering, has drawn recent attentions since it nicely addresses the instability issue of traditional nonconvex clustering methods.
Provable Sparse Tensor Decomposition
We propose a novel sparse tensor decomposition method, namely Tensor Truncated Power (TTP) method, that incorporates variable selection into the estimation of decomposition components.
