124 citations · 177 across the 12 of their papers we have counts for
13 papers
Handling Inter-class and Intra-class Imbalance in Class-imbalanced Learning
Zhining Liu, Pengfei Wei, Zhepei Wei +5
Class-imbalance is a common problem in machine learning practice. Typical Imbalanced Learning (IL) methods balance the data via intuitive class-wise resampling or reweighting. Howe…
IMBENS: Ensemble Class-imbalanced Learning in Python
Zhining Liu, Jian Kang, Hanghang Tong +1
imbalanced-ensemble, abbreviated as imbens, is an open-source Python toolbox for leveraging the power of ensemble learning to address the class imbalance problem. It provides stand…
Graph Domain Adaptation: A Generative View
Ruichu Cai, Fengzhu Wu, Zijian Li +3
Recent years have witnessed tremendous interest in deep learning on graph-structured data. Due to the high cost of collecting labeled graph-structured data, domain adaptation is im…
Adaptive Multi-Source Causal Inference
Thanh Vinh Vo, Pengfei Wei, Trong Nghia Hoang +1
Data scarcity is a tremendous challenge in causal effect estimation. In this paper, we propose to exploit additional data sources to facilitate estimating causal effects in the tar…
Joint Intent Detection and Slot Filling with Wheel-Graph Attention Networks
Pengfei Wei, Bi Zeng, Wenxiong Liao
Intent detection and slot filling are two fundamental tasks for building a spoken language understanding (SLU) system. Multiple deep learning-based joint models have demonstrated e…
Learning Disentangled Semantic Representation for Domain Adaptation
Ruichu Cai, Zijian Li, Pengfei Wei +3
Domain adaptation is an important but challenging task. Most of the existing domain adaptation methods struggle to extract the domain-invariant representation on the feature space…
Cooperative Heterogeneous Deep Reinforcement Learning
Han Zheng, Pengfei Wei, Jing Jiang +3
Numerous deep reinforcement learning agents have been proposed, and each of them has its strengths and flaws. In this work, we present a Cooperative Heterogeneous Deep Reinforcemen…
An Improved Transfer Model: Randomized Transferable Machine
Pengfei Wei, Xinghua Qu, Yew Soon Ong +1
Feature-based transfer is one of the most effective methodologies for transfer learning. Existing studies usually assume that the learned new feature representation is \emph{domain…
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler
Zhining Liu, Pengfei Wei, Jing Jiang +3
Imbalanced learning (IL), i.e., learning unbiased models from class-imbalanced data, is a challenging problem. Typical IL methods including resampling and reweighting were designed…
Hierarchical Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection
Xinyi Xu, Tiancheng Huang, Pengfei Wei +2
This work is inspired by recent advances in hierarchical reinforcement learning (HRL) (Barto and Mahadevan 2003; Hengst 2010), and improvements in learning efficiency from heuristi…
Subdomain Adaptation with Manifolds Discrepancy Alignment
Pengfei Wei, Yiping Ke, Xinghua Qu +1
Reducing domain divergence is a key step in transfer learning problems. Existing works focus on the minimization of global domain divergence. However, two domains may consist of se…
Causal Modeling with Stochastic Confounders
Thanh Vinh Vo, Pengfei Wei, Wicher Bergsma +1
This work extends causal inference with stochastic confounders. We propose a new approach to variational estimation for causal inference based on a representer theorem with a rando…
Minimalistic Attacks: How Little it Takes to Fool a Deep Reinforcement Learning Policy
Xinghua Qu, Zhu Sun, Yew-Soon Ong +2
Recent studies have revealed that neural network-based policies can be easily fooled by adversarial examples. However, while most prior works analyze the effects of perturbing ever…