26 citations · 67 across the 6 of their papers we have counts for
6 papers
Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RL
Rui Yang, Yiming Lu, Wenzhe Li +6
Solving goal-conditioned tasks with sparse rewards using self-supervised learning is promising because of its simplicity and stability over current reinforcement learning (RL) algo…
Offline Reinforcement Learning with Reverse Model-based Imagination
Jianhao Wang, Wenzhe Li, Haozhe Jiang +3
In offline reinforcement learning (offline RL), one of the main challenges is to deal with the distributional shift between the learning policy and the given dataset. To address th…
Estimating High Order Gradients of the Data Distribution by Denoising
Chenlin Meng, Yang Song, Wenzhe Li +1
The first order derivative of a data density can be estimated efficiently by denoising score matching, and has become an important component in many applications, such as image gen…
Tractable Computation of Expected Kernels
Wenzhe Li, Zhe Zeng, Antonio Vergari +1
Computing the expectation of kernel functions is a ubiquitous task in machine learning, with applications from classical support vector machines to exploiting kernel embeddings of…
Unifying Local and Global Change Detection in Dynamic Networks
Wenzhe Li, Dong Guo, Greg Ver Steeg +1
Many real-world networks are complex dynamical systems, where both local (e.g., changing node attributes) and global (e.g., changing network topology) processes unfold over time. L…
Scalable MCMC for Mixed Membership Stochastic Blockmodels
Wenzhe Li, Sungjin Ahn, Max Welling
We propose a stochastic gradient Markov chain Monte Carlo (SG-MCMC) algorithm for scalable inference in mixed-membership stochastic blockmodels (MMSB). Our algorithm is based on th…