28 citations · 39 across the 6 of their papers we have counts for
6 papers
Learn Dynamic-Aware State Embedding for Transfer Learning
Kaige Yang
Transfer reinforcement learning aims to improve the sample efficiency of solving unseen new tasks by leveraging experiences obtained from previous tasks. We consider the setting wh…
Differentiable Linear Bandit Algorithm
Kaige Yang, Laura Toni
Upper Confidence Bound (UCB) is arguably the most commonly used method for linear multi-arm bandit problems. While conceptually and computationally simple, this method highly relie…
Laplacian-regularized graph bandits: Algorithms and theoretical analysis
Kaige Yang, Xiaowen Dong, Laura Toni
We consider a stochastic linear bandit problem with multiple users, where the relationship between users is captured by an underlying graph and user preferences are represented as…
Error Analysis on Graph Laplacian Regularized Estimator
Kaige Yang, Xiaowen Dong, Laura Toni
We provide a theoretical analysis of the representation learning problem aimed at learning the latent variables (design matrix) of observations with the knowledge of the co…
Data Driven Chiller Plant Energy Optimization with Domain Knowledge
Hoang Dung Vu, Kok Soon Chai, Bryan Keating +5
Refrigeration and chiller optimization is an important and well studied topic in mechanical engineering, mostly taking advantage of physical models, designed on top of over-simplif…
Graph-Based Recommendation System
Kaige Yang, Laura Toni
In this work, we study recommendation systems modelled as contextual multi-armed bandit (MAB) problems. We propose a graph-based recommendation system that learns and exploits the…