Search Results for author:
Found 30 papers, 2 papers with code
Fairness-aware Contextual Dynamic Pricing with Strategic Buyers
When buyers are able to learn the fairness of the price policy, this upper bound reduces to $O(\sqrt{T})$.
Low-Rank Contextual Reinforcement Learning from Heterogeneous Human Feedback
Reinforcement learning from human feedback (RLHF) has become a cornerstone for aligning large language models with human preferences.
Privacy-Preserving Dynamic Assortment Selection
With the growing demand for personalized assortment recommendations, concerns over data privacy have intensified, highlighting the urgent need for effective privacy-preserving strategies.
Dual Active Learning for Reinforcement Learning from Human Feedback
Through simulations and experiments on LLMs, we demonstrate the effectiveness of our algorithm and its superiority over state-of-the-arts.
Active Learning for Fair and Stable Online Allocations
We explore an active learning approach for dynamic fair resource allocation problems.
Low-Rank Online Dynamic Assortment with Dual Contextual Information
In this paper, we consider the dynamic assortment problem with dual contexts -- user and item features.
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.
Rate-Optimal Rank Aggregation with Private Pairwise Rankings
To address this, we propose an adaptive debiasing method for rankings from the randomized response mechanism, ensuring consistent estimation of true preferences and enhancing the utility of downstream rank aggregation.
Online Tensor Inference
Recent technological advances have led to contemporary applications that demand real-time processing and analysis of sequentially arriving tensor 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.
Utility Theory of Synthetic Data Generation
The former is defined as the generalization difference between models trained on synthetic and on real data.
Ranking Differential Privacy
This allows us to develop a multistage ranking algorithm to generate synthetic rankings while satisfying the developed $\epsilon$-ranking differential privacy.
Online Statistical Inference in Decision-Making with Matrix Context
The low-rank structure of the model parameter and the adaptive nature of the data collection process make this difficult: standard low-rank estimators are biased and cannot be obtained in a sequential manner while existing inference approaches in sequential decision-making algorithms fail to account for the low-rankness and are also biased.
Dynamic Matching Bandit For Two-Sided Online Markets
The key component of the proposed dynamic matching algorithm is an online estimation of the preference ranking with a statistical guarantee.
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.
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.
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.
