Results 81 to 90 of about 2,925,819 (300)

Thompson Sampling Algorithms for Cascading Bandits

J. Mach. Learn. Res., 2018
Motivated by the pressing need for efficient optimization in online recommender systems, we revisit the cascading bandit model proposed by Kveton et al. (2015). While Thompson sampling (TS) algorithms have been shown to be empirically superior to Upper Confidence Bound (UCB) algorithms for cascading bandits, theoretical guarantees are only known for ...
Zixin Zhong, Wang Chi Chueng, Vincent Y. F. Tan   +2 more
openaire   +4 more sources

Rational Expectations and Market Timing: An Event Study of the China–United States Phase One Trade Deal

Applied Economic Perspectives and Policy, EarlyView.
ABSTRACT Rational actors constantly incorporate information into their decision‐making behavior. Since there is often a time lag between the announcement of a policy and its implementation, an important question arises: when do rational actors incorporate new information into their market behavior, at the announcement or at the implementation of a ...
Tim Ölkers, Oliver Mußhoff
wiley   +1 more source

Value of Information of Improved Traceability in Fresh Produce Markets

Agribusiness, EarlyView.
ABSTRACT Traceability plays an important role in promoting a safe food supply by fostering transparent information exchange along the food supply chain. New technological innovations have the potential to improve traceability outcomes, as greater transparency along the food supply chain can aid in pinpointing precise origins of the contamination ...
Kelsey Vourazeris, Mark R. Manfredo, Michael N. Kozicki   +2 more
wiley   +1 more source

Comparison of Video Recommendation Effects of Etc, Ucb, and Thompson Sampling Algorithms on Short-Video Platforms [PDF]

ITM Web of Conferences
This paper comprehensively compares the performance of three multi-armed bandit (MAB) algorithms, Epsilon-Then-Commit (ETC), upper confidence bound (UCB), and Thompson sampling (TS), for video recommendation in dynamic environments.
Li Shouchuan
doaj   +1 more source

Thompson Sampling for Repeated Newsvendor

CoRR
In this paper, we investigate the performance of Thompson Sampling (TS) for online learning with censored feedback, focusing primarily on the classic repeated newsvendor model--a foundational framework in inventory management--and demonstrating how our techniques can be naturally extended to a broader class of problems.
Weizhou Zhang, Chen Li, Hanzhang Qin, Yunbei Xu, Ruihao Zhu   +4 more
openaire   +3 more sources

OPTIMAL RANDOM SAMPLING DESIGNS IN RANDOM FIELD SAMPLING [PDF]

A Horvitz-Thompson predictor is proposed for spatial sampling when the characteristic of interest is modeled as a random field. Optimal sampling designs are deduced under this context. Fixed and variable sample size are considered.
Fernando Ávila, José E. Rodríguez
core  

Factorization Machine with Iterative Quantum Reverse Annealing: A Python Package for Batch Black‐Box Optimization With Reverse Quantum Annealing

Advanced Intelligent Discovery, EarlyView.
Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley   +1 more source

Autonomous AI‐Driven Design for Skin Product Formulations

Advanced Intelligent Discovery, EarlyView.
This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang, Yongrui Xiao, Chunlin Chen, Dimitrios Tsaoulidis, Kai Yang, Tao Chen   +5 more
wiley   +1 more source

Constrained Linear Thompson Sampling

Advances in Neural Information Processing Systems 38
We study safe linear bandits (SLBs), where an agent selects actions from a convex set to maximize an unknown linear objective subject to unknown linear constraints in each round. Existing methods for SLBs provide strong regret guarantees, but require solving expensive optimization problems (e.g., second-order cones, NP hard programs).
Aditya Gangrade, Venkatesh Saligrama
openaire   +2 more sources

Thompson Sampling for Unsupervised Sequential Selection

CoRR, 2020
Thompson Sampling has generated significant interest due to its better empirical performance than upper confidence bound based algorithms. In this paper, we study Thompson Sampling based algorithm for Unsupervised Sequential Selection (USS) problem. The USS problem is a variant of the stochastic multi-armed bandits problem, where the loss of an arm can
Arun Verma, Manjesh Kumar Hanawal, Nandyala Hemachandra   +2 more
openaire   +4 more sources