5 citations · 14 across the 6 of their papers we have counts for
7 papers
Speed Up the Cold-Start Learning in Two-Sided Bandits with Many Arms
Mohsen Bayati, Junyu Cao, Wanning Chen
Multi-armed bandit (MAB) algorithms are efficient approaches to reduce the opportunity cost of online experimentation and are used by companies to find the best product from period…
Fatigue-aware Bandits for Dependent Click Models
Junyu Cao, Wei Sun, Zuo-Jun +2
As recommender systems send a massive amount of content to keep users engaged, users may experience fatigue which is contributed by 1) an overexposure to irrelevant content, 2) bor…
Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit Problem
Junyu Cao, Wei Sun
Motivated by the phenomenon that companies introduce new products to keep abreast with customers' rapidly changing tastes, we consider a novel online learning setting where a profi…
Dynamic Learning of Sequential Choice Bandit Problem under Marketing Fatigue
Junyu Cao, Wei Sun
Motivated by the observation that overexposure to unwanted marketing activities leads to customer dissatisfaction, we consider a setting where a platform offers a sequence of messa…
Last-mile shared delivery: A discrete sequential packing approach
Junyu Cao, Mariana Olvera-Cravioto, Zuo-Jun Shen
We propose a model for optimizing the last-mile delivery of n packages, from a distribution center to their final recipients, using a strategy that combines the use of ride-sharing…
On a general class of inhomogeneous random digraphs
Junyu Cao, Mariana Olvera-Cravioto
We study a family of directed random graphs whose arcs are sampled independently of each other, and are present in the graph with a probability that depends on the attributes of th…