Mitigating Spurious Correlations in LLMs via Causality-Aware Post-Training.
CoRR, June, 2025
Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning.
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CoRR, June, 2025
Discovering Physics Laws of Dynamical Systems via Invariant Function Learning.
CoRR, February, 2025
FlowX: Towards Explainable Graph Neural Networks via Message Flows.
IEEE Trans. Pattern Anal. Mach. Intell., 2024
A Hierarchical Language Model For Interpretable Graph Reasoning.
CoRR, 2024
Geometry Informed Tokenization of Molecules for Language Model Generation.
CoRR, 2024
Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency.
Proceedings of the Forty-first International Conference on Machine Learning, 2024
Graph Structure Extrapolation for Out-of-Distribution Generalization.
Proceedings of the Forty-first International Conference on Machine Learning, 2024
Active Test-Time Adaptation: Theoretical Analyses and An Algorithm.
Proceedings of the Twelfth International Conference on Learning Representations, 2024
Explainability in Graph Neural Networks: A Taxonomic Survey.
IEEE Trans. Pattern Anal. Mach. Intell., May, 2023
Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization.
CoRR, 2023
Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
GOOD: A Graph Out-of-Distribution Benchmark.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
DIG: A Turnkey Library for Diving into Graph Deep Learning Research.
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J. Mach. Learn. Res., 2021
FeatureFlow: Robust Video Interpolation via Structure-to-Texture Generation.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020