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Unsupervised Event Outlier Detection in Continuous Time

S. Nath, K. Y. C. Lui, and S. Liu. Workshop at Conference on Neural Information Processing Systems (NeurIPS), 2024

LLM-TS Integrator: Integrating LLM for Enhanced Time Series Modeling

C. Chen, G. Oliveira, H. Sharifi, and T. Sylvain. Workshop at Conference on Neural Information Processing Systems (NeurIPS), 2024

Inference, Fast and Slow: Reinterpreting VAEs for OOD Detection

S. Huang, J. He, and K. Y. C. Lui. Workshop at Conference on Neural Information Processing Systems (NeurIPS), 2024

Do LLMs Build World Representations? Probing Through the Lens of State Abstraction

Z. Li, Y. Cao, and J. C.K. Cheung. Conference on Neural Information Processing Systems (NeurIPS), 2024

NeuZip: Memory-Efficient Training and Inference with Dynamic Compression of Neural Networks

Y. Hao, Y. Cao, and L. Mou. Workshop at Conference on Neural Information Processing Systems (NeurIPS), 2024

ClavaDDPM: Multi-relational Data Synthesis with Cluster-guided Diffusion Models

W. Pang, M. Shafieinejad, L. Liu, S. Hazlewood, and X. He. Conference on Neural Information Processing Systems (NeurIPS), 2024

Identifying and Addressing Delusions for Target-Directed Decision-Making

M. Zhao, T. Sylvain, D. Precup, and Y. Bengio. Workshop at Conference on Neural Information Processing System (NeurIPS), 2024

Leveraging Environment Interaction for Automated PDDL Generation and Planning with Large Language Models

S. Mahdavi, R. Aoki, K. Tang, and Y. Cao. Conference on Neural Information Processing System (NeurIPS), 2024

Jump Starting Bandits with LLM-Generated Prior Knowledge

P. A. Alamdari, Y. Cao, and K. Wilson. Conference on Empirical Methods in Natural Language Processing, 2024

AdaFlood: Adaptive Flood Regularization

W. Bae, Y. Ren, M. O. Ahmed, F. Tung, D. J. Sutherland, and G. Oliveira. Transactions on Machine Learning Research (TMLR), 2024

Interpretation for Variational Autoencoder Used to Generate Financial Synthetic Tabular Data

J. Wu, K. N. Plataniotis, *L. Z. Liu, *E. Amjadian, and Y. A. Lawryshyn. Special Issue Interpretability, Accountability and Robustness in Machine Learning (Algorithims), 2023

ATOM: Attention Mixer for Efficient Dataset Distillation

*S. Khaki, *A. Sajedi, K. Wang, L. Z. Liu, Y. A. Lawryshyn, and K. N. Plataniotis. Oral presentation at The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2024