Tim Z. Xiao

According to our database, Tim Z. Xiao authored at least 19 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Flipping Against All Odds: Reducing LLM Coin Flip Bias via Verbalized Rejection Sampling.

Tim Z. Xiao, Johannes Zenn, Zhen Liu, Weiyang Liu, Robert Bamler, Bernhard Schölkopf

CoRR, June, 2025

Reparameterized LLM Training via Orthogonal Equivalence Transformation.

Zeju Qiu, Simon Buchholz, Tim Z. Xiao, Maximilian Dax, Bernhard Schölkopf, Weiyang Liu

CoRR, June, 2025

Generating Symbolic World Models via Test-time Scaling of Large Language Models.

Zhouliang Yu, Yuhuan Yuan, Tim Z. Xiao, Fuxiang Frank Xia, Jie Fu, Ge Zhang, Ge Lin, Weiyang Liu

Trans. Mach. Learn. Res., 2025

A Note on Generalization in Variational Autoencoders: How Effective Is Synthetic Data and Overparameterization?

Tim Z. Xiao, Johannes Zenn, Robert Bamler

Trans. Mach. Learn. Res., 2025

Verbalized Machine Learning: Revisiting Machine Learning with Language Models.

Tim Z. Xiao, Robert Bamler, Bernhard Schölkopf, Weiyang Liu

Trans. Mach. Learn. Res., 2025

Can Large Language Models Understand Symbolic Graphics Programs?

Zeju Qiu, Weiyang Liu, Haiwen Feng, Zhen Liu, Tim Z. Xiao, Katherine M. Collins, Joshua B. Tenenbaum, Adrian Weller, Michael J. Black, Bernhard Schölkopf

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matching.

Zijing Ou, Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yingzhen Li, David Barber

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets.

Zhen Liu, Tim Z. Xiao, Weiyang Liu, Yoshua Bengio, Dinghuai Zhang

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector.

Andi Zhang, Tim Z. Xiao, Weiyang Liu, Robert Bamler, Damon Wischik

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Diffusion Model With Optimal Covariance Matching.

Zijing Ou, Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yingzhen Li, David Barber

CoRR, 2024

A Compact Representation for Bayesian Neural Networks By Removing Permutation Symmetry.

Tim Z. Xiao, Weiyang Liu, Robert Bamler

CoRR, 2024

2023

The SVHN Dataset Is Deceptive for Probabilistic Generative Models Due to a Distribution Mismatch.

Tim Z. Xiao, Johannes Zenn, Robert Bamler

CoRR, 2023

Upgrading VAE Training With Unlimited Data Plans Provided by Diffusion Models.

Tim Z. Xiao, Johannes Zenn, Robert Bamler

CoRR, 2023

Trading Information between Latents in Hierarchical Variational Autoencoders.

Tim Z. Xiao, Robert Bamler

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Iterative Teaching by Data Hallucination.

Zeju Qiu, Weiyang Liu, Tim Z. Xiao, Zhen Liu, Umang Bhatt, Yucen Luo, Adrian Weller, Bernhard Schölkopf

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Out-of-Distribution Detection with Class Ratio Estimation.

Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yitong Sun, Steven McDonagh

CoRR, 2022

Improving VAE-based Representation Learning.

Mingtian Zhang, Tim Z. Xiao, Brooks Paige, David Barber

CoRR, 2022

2021

Locally-Contextual Nonlinear CRFs for Sequence Labeling.

Harshil Shah, Tim Z. Xiao, David Barber

CoRR, 2021

2020

Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers.

Tim Z. Xiao, Aidan N. Gomez, Yarin Gal

CoRR, 2020