24 citations · 87 across the 23 of their papers we have counts for
14 papers · 1 filter
Model-based Reinforcement Learning with Multi-step Plan Value Estimation
Haoxin Lin, Yihao Sun, Jiaji Zhang +1
A promising way to improve the sample efficiency of reinforcement learning is model-based methods, in which many explorations and evaluations can happen in the learned models to sa…
Enhancing Neural Mathematical Reasoning by Abductive Combination with Symbolic Library
Yangyang Hu, Yang Yu
Mathematical reasoning recently has been shown as a hard challenge for neural systems. Abilities including expression translation, logical reasoning, and mathematics knowledge acqu…
A Note on Target Q-learning For Solving Finite MDPs with A Generative Oracle
Ziniu Li, Tian Xu, Yang Yu
Q-learning with function approximation could diverge in the off-policy setting and the target network is a powerful technique to address this issue. In this manuscript, we examine…
Rethinking ValueDice: Does It Really Improve Performance?
Ziniu Li, Tian Xu, Yang Yu +1
Since the introduction of GAIL, adversarial imitation learning (AIL) methods attract lots of research interests. Among these methods, ValueDice has achieved significant improvement…
Self-supervised learning for fast and scalable time series hyper-parameter tuning
Peiyi Zhang, Xiaodong Jiang, Ginger M Holt +4
Hyper-parameters of time series models play an important role in time series analysis. Slight differences in hyper-parameters might lead to very different forecast results for a gi…
Angular Embedding: A New Angular Robust Principal Component Analysis
Shenglan Liu, Yang Yu
As a widely used method in machine learning, principal component analysis (PCA) shows excellent properties for dimensionality reduction. It is a serious problem that PCA is sensiti…