Lifan Chen — accepted AI-conference papers

2026

KeenKT: Knowledge Mastery-State Disambiguation for Knowledge Tracing

Zhifei Li, Lifan Chen, Jiali Yi, Xiaoju Hou, Yue Zhao, Wenxin Huang +3

AAAI 2026technical

Knowledge Tracing (KT) aims to dynamically model a student’s mastery of knowledge concepts based on their historical learning interactions. Most current methods rely on single-point estimates, which cannot distinguish true ability from outburst or carelessness, creating ambiguity in judging mastery.

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2021

Variance Reduced Training with Stratified Sampling for Forecasting Models

Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean Foster

ICML 2021spotlight

In large-scale time series forecasting, one often encounters the situation where the temporal patterns of time series, while drifting over time, differ from one another in the same dataset. In this paper, we provably show under such heterogeneity, training a forecasting model with commonly used stoc…

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