578 citations · 2.7k across the 91 of their papers we have counts for
6 papers · 1 filter
TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed Datasets
Yuanying Cai, Chuheng Zhang, Li Zhao +6
We consider an offline reinforcement learning (RL) setting where the agent need to learn from a dataset collected by rolling out multiple behavior policies. There are two challenge…
Learning Causal Semantic Representation for Out-of-Distribution Prediction
Chang Liu, Xinwei Sun, Jindong Wang +5
Conventional supervised learning methods, especially deep ones, are found to be sensitive to out-of-distribution (OOD) examples, largely because the learned representation mixes th…
Differential Equations for Modeling Asynchronous Algorithms
Li He, Qi Meng, Wei Chen +2
Asynchronous stochastic gradient descent (ASGD) is a popular parallel optimization algorithm in machine learning. Most theoretical analysis on ASGD take a discrete view and prove u…
Train Feedfoward Neural Network with Layer-wise Adaptive Rate via Approximating Back-matching Propagation
Huishuai Zhang, Wei Chen, Tie-Yan Liu
Stochastic gradient descent (SGD) has achieved great success in training deep neural network, where the gradient is computed through back-propagation. However, the back-propagated…
-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space
Qi Meng, Shuxin Zheng, Huishuai Zhang +3
It is well known that neural networks with rectified linear units (ReLU) activation functions are positively scale-invariant. Conventional algorithms like stochastic gradient desce…
Convergence Analysis of Distributed Stochastic Gradient Descent with Shuffling
Qi Meng, Wei Chen, Yue Wang +2
When using stochastic gradient descent to solve large-scale machine learning problems, a common practice of data processing is to shuffle the training data, partition the data acro…