23 citations · 44 across the 9 of their papers we have counts for
9 papers
Interpreting Deep Forest through Feature Contribution and MDI Feature Importance
Yi-Xiao He, Shen-Huan Lyu, Yuan Jiang
Deep forest is a non-differentiable deep model which has achieved impressive empirical success across a wide variety of applications, especially on categorical/symbolic or mixed mo…
Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinking
Zichong Wang, Yang Zhou, David Lo +1
The increasing use of Machine Learning (ML) software can lead to unfair and unethical decisions, thus fairness bugs in software are becoming a growing concern. Addressing these fai…
Algorithmically Effective Differentially Private Synthetic Data
Yiyun He, Roman Vershynin, Yizhe Zhu
We present a highly effective algorithmic approach for generating -differentially private synthetic data in a bounded metric space with near-optimal utility guarantees…
Multi-Metric AutoRec for High Dimensional and Sparse User Behavior Data Prediction
Cheng Liang, Teng Huang, Yi He +3
User behavior data produced during interaction with massive items in the significant data era are generally heterogeneous and sparse, leaving the recommender system (RS) a large di…
Highly-Accurate Electricity Load Estimation via Knowledge Aggregation
Yuting Ding, Di Wu, Yi He +2
Mid-term and long-term electric energy demand prediction is essential for the planning and operations of the smart grid system. Mainly in countries where the power system operates…
An Online Sparse Streaming Feature Selection Algorithm
Feilong Chen, Di Wu, Jie Yang +1
Online streaming feature selection (OSFS), which conducts feature selection in an online manner, plays an important role in dealing with high-dimensional data. In many real applica…
SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles
Chejian Xu, Wenhao Ding, Weijie Lyu +6
As shown by recent studies, machine intelligence-enabled systems are vulnerable to test cases resulting from either adversarial manipulation or natural distribution shifts. This ha…
A Multi-Metric Latent Factor Model for Analyzing High-Dimensional and Sparse data
Di Wu, Peng Zhang, Yi He +1
High-dimensional and sparse (HiDS) matrices are omnipresent in a variety of big data-related applications. Latent factor analysis (LFA) is a typical representation learning method…
Graph-incorporated Latent Factor Analysis for High-dimensional and Sparse Matrices
Di Wu, Yi He, Xin Luo
A High-dimensional and sparse (HiDS) matrix is frequently encountered in a big data-related application like an e-commerce system or a social network services system. To perform hi…