4 citations · 4 across the 6 of their papers we have counts for
6 papers
Density-Guided Robust Counterfactual Explanations on Tabular Data under Model Multiplicity
Jun Tan, Qing Guo, Zicheng Xu +3
Counterfactual explanations (CEs) are essential for actionable recourse, yet their reliability is often compromised in low-density regions, where classifiers exhibit high variance.…
Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting
Jinglin Li, Jun Tan, QI Fang +1
Effectively modeling non-stationary dynamics in probabilistic multivariate time series(MTS) forecasting requires balancing expressiveness with robustness. Existing parametric appro…
CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation
Ningxin Gui, Qianghuai Jia, Feijun Jiang +3
We introduce CRPE (Code Reasoning Process Enhancer), an innovative three-stage framework for data synthesis and model training that advances the development of sophisticated code r…
Non-stationary Diffusion For Probabilistic Time Series Forecasting
Weiwei Ye, Zhuopeng Xu, Ning Gui
Due to the dynamics of underlying physics and external influences, the uncertainty of time series often varies over time. However, existing Denoising Diffusion Probabilistic Models…
Ordering-Based Causal Discovery for Linear and Nonlinear Relations
Zhuopeng Xu, Yujie Li, Cheng Liu +1
Identifying causal relations from purely observational data typically requires additional assumptions on relations and/or noise. Most current methods restrict their analysis to dat…
Frequency Adaptive Normalization For Non-stationary Time Series Forecasting
Weiwei Ye, Songgaojun Deng, Qiaosha Zou +1
Time series forecasting typically needs to address non-stationary data with evolving trend and seasonal patterns. To address the non-stationarity, reversible instance normalization…