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Found 24 papers, 1 papers with code
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.
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.
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.
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.
Simultaneous Clustering and Estimation of Heterogeneous Graphical Models
We consider joint estimation of multiple graphical models arising from heterogeneous and high-dimensional observations.
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.
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.
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.
Sparse Tensor Additive Regression
Tensors are becoming prevalent in modern applications such as medical imaging and digital marketing.
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.
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.
Stochastic Low-rank Tensor Bandits for Multi-dimensional Online Decision Making
We consider two settings, tensor bandits without context and tensor bandits with context.
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.
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.
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.
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.
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.
Online Statistical Inference for Matrix Contextual Bandit
To fill in these gaps, in this work we consider a matrix contextual bandit framework where the true model parameter is a low-rank matrix, and propose a fully online procedure to simultaneously make sequential decision-making and conduct statistical inference.
Ranking Differential Privacy
This allows us to develop a multistage ranking algorithm to generate synthetic rankings while satisfying the developed $\epsilon$-ranking differential privacy.
Utility Theory of Synthetic Data Generation
The former is defined as the generalization difference between models trained on synthetic and on real data.
Contextual Dynamic Pricing with Strategic Buyers
We first prove that existing non-strategic pricing policies that neglect the buyers' strategic behavior result in a linear $\Omega(T)$ regret with $T$ the total time horizon, indicating that these policies are not better than a random pricing policy.
Online Tensor Inference
Recent technological advances have led to contemporary applications that demand real-time processing and analysis of sequentially arriving tensor data.
Rate-Optimal Rank Aggregation with Private Pairwise Rankings
Motivated from this, we propose a debiased randomized response mechanism to protect the raw pairwise rankings, ensuring consistent estimation of true preferences and rankings in downstream rank aggregation.
Pessimistic Causal Reinforcement Learning with Mediators for Confounded Offline Data
As a result, leveraging large observational datasets becomes a more attractive option for achieving high-quality policy learning.
