38 citations · 93 across the 12 of their papers we have counts for
5 papers · 1 filter
Towards AutoML in the presence of Drift: first results
Jorge G. Madrid, Hugo Jair Escalante, Eduardo F. Morales +5
Research progress in AutoML has lead to state of the art solutions that can cope quite wellwith supervised learning task, e.g., classification with AutoSklearn. However, so far the…
Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation
Wenjie Shang, Yang Yu, Qingyang Li +3
Reinforcement learning aims at searching the best policy model for decision making, and has been shown powerful for sequential recommendations. The training of the policy by reinfo…
On the Robustness of Median Sampling in Noisy Evolutionary Optimization
Chao Bian, Chao Qian, Yang Yu +1
Evolutionary algorithms (EAs) are a sort of nature-inspired metaheuristics, which have wide applications in various practical optimization problems. In these problems, objective ev…
Running Time Analysis of the (1+1)-EA for Robust Linear Optimization
Chao Bian, Chao Qian, Ke Tang +1
Evolutionary algorithms (EAs) have found many successful real-world applications, where the optimization problems are often subject to a wide range of uncertainties. To understand…
Cascaded Algorithm-Selection and Hyper-Parameter Optimization with Extreme-Region Upper Confidence Bound Bandit
Yi-Qi Hu, Yang Yu, Jun-Da Liao
An automatic machine learning (AutoML) task is to select the best algorithm and its hyper-parameters simultaneously. Previously, the hyper-parameters of all algorithms are joint as…