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Found 13 papers, 1 papers with code
Residual Bootstrap Exploration for Bandit Algorithms
In this paper, we propose a novel perturbation-based exploration method in bandit algorithms with bounded or unbounded rewards, called residual bootstrap exploration (\texttt{ReBoot}).
Online Batch Decision-Making with High-Dimensional Covariates
In such a scenario, our goal is to allocate a batch of treatments to maximize treatment efficacy based on observed high-dimensional user covariates.
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.
Online Forgetting Process for Linear Regression Models
Motivated by the EU's "Right To Be Forgotten" regulation, we initiate a study of statistical data deletion problems where users' data are accessible only for a limited period of time.
Optimum-statistical Collaboration Towards General and Efficient Black-box Optimization
In this paper, we make the key delineation on the roles of resolution and statistical uncertainty in hierarchical bandits-based black-box optimization algorithms, guiding a more general analysis and a more efficient algorithm design.
Residual Bootstrap Exploration for Stochastic Linear Bandit
We propose a new bootstrap-based online algorithm for stochastic linear bandit problems.
Federated Online Sparse Decision Making
This paper presents a novel federated linear contextual bandits model, where individual clients face different K-armed stochastic bandits with high-dimensional decision context and coupled through common global parameters.
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.
Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing
This paper presents a novel non-stationary dynamic pricing algorithm design, where pricing agents face incomplete demand information and market environment shifts.
Always Valid Risk Monitoring for Online Matrix Completion
Always-valid concentration inequalities are increasingly used as performance measures for online statistical learning, notably in the learning of generative models and supervised learning.
On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulation
Devising procedures for auditing generative model privacy-utility tradeoff is an important yet unresolved problem in practice.
Improve Fidelity and Utility of Synthetic Credit Card Transaction Time Series from Data-centric Perspective
Exploring generative model training for synthetic tabular data, specifically in sequential contexts such as credit card transaction data, presents significant challenges.
Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Models
Devising procedures for downstream task-oriented generative model selections is an unresolved problem of practical importance.
