Publications

(2025). THOR: A Generic Energy Estimation Approach for On-Device Training. In Submission.

(2025). Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learning. In AISTATS 2025.

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(2024). Exploring Diffusion Models' Corruption Stage in Few-Shot Fine-tuning and Mitigating with Bayesian Neural Networks. In Submission.

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(2024). CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion. In CVPR.

(2024). SPOT: Harnessing Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior. In SIGKDD.

(2023). Information Bound and its Applications in Bayesian Neural Networks. In ECAI.

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(2023). All Frequency Components Matter: A Learnable Frequency Component Compression Framework for Adversarial Defense. In Submission.

(2023). Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples. In ICML, oral.

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(2022). Improving Bayesian Neural Networks by Adversarial Sampling. In AAAI.

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(2022). Hierarchical Satellite System Graph for Approximate Nearest Neighbor Search on Big Data. ACM TDS.

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(2021). Robust Bayesian Neural Networks by Spectral Expectation Bound Regularization. In CVPR.

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(2019). Generalized Tensor-based Multiple Feature Fusion Network using Block Decomposition. Preprint.

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