Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers. - Tim Z. Xiao; Aidan N. Gomez; Yarin Gal (CoRR, 2020)

Locally-Contextual Nonlinear CRFs for Sequence Labeling. - Harshil Shah; Tim Z. Xiao; David Barber (CoRR, 2021)

Improving VAE-based Representation Learning. - Mingtian Zhang; Tim Z. Xiao; Brooks Paige; David Barber (CoRR, 2022)

Out-of-Distribution Detection with Class Ratio Estimation. - Mingtian Zhang; Andi Zhang 001; Tim Z. Xiao; Yitong Sun; Steven McDonagh (CoRR, 2022)

Iterative Teaching by Data Hallucination. - Zeju Qiu; Weiyang Liu; Tim Z. Xiao; Zhen Liu 019; Umang Bhatt; Yucen Luo; Adrian Weller; Bernhard Schölkopf (AISTATS, 2023)

Trading Information between Latents in Hierarchical Variational Autoencoders. - Tim Z. Xiao; Robert Bamler (ICLR, 2023)

Upgrading VAE Training With Unlimited Data Plans Provided by Diffusion Models. - Tim Z. Xiao; Johannes Zenn; Robert Bamler (CoRR, 2023)

The SVHN Dataset Is Deceptive for Probabilistic Generative Models Due to a Distribution Mismatch. - Tim Z. Xiao; Johannes Zenn; Robert Bamler (CoRR, 2023)

A Compact Representation for Bayesian Neural Networks By Removing Permutation Symmetry. - Tim Z. Xiao; Weiyang Liu; Robert Bamler (CoRR, 2024)

Diffusion Model With Optimal Covariance Matching. - Zijing Ou; Mingtian Zhang; Andi Zhang 001; Tim Z. Xiao; Yingzhen Li; David Barber (CoRR, 2024)

Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector. - Andi Zhang 001; Tim Z. Xiao; Weiyang Liu; Robert Bamler; Damon Wischik (AISTATS, 2025)

Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets. - Zhen Liu 019; Tim Z. Xiao; Weiyang Liu; Yoshua Bengio; Dinghuai Zhang (ICLR, 2025)

Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matching. - Zijing Ou; Mingtian Zhang; Andi Zhang 001; Tim Z. Xiao; Yingzhen Li; David Barber (ICLR, 2025)

Can Large Language Models Understand Symbolic Graphics Programs? - Zeju Qiu; Weiyang Liu; Haiwen Feng; Zhen Liu 019; Tim Z. Xiao; Katherine M. Collins; Joshua B. Tenenbaum; Adrian Weller; Michael J. Black; Bernhard Schölkopf (ICLR, 2025)

Verbalized Machine Learning: Revisiting Machine Learning with Language Models. - Tim Z. Xiao; Robert Bamler; Bernhard Schölkopf; 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)

Generating Symbolic World Models via Test-time Scaling of Large Language Models. - Zhouliang Yu; Yuhuan Yuan; Tim Z. Xiao; Fuxiang Frank Xia; Jie Fu 001; Ge Zhang 009; Ge Lin; Weiyang Liu (Trans. Mach. Learn. Res., 2025)

Reparameterized LLM Training via Orthogonal Equivalence Transformation. - Zeju Qiu; Simon Buchholz; Tim Z. Xiao; Maximilian Dax; Bernhard Schölkopf; Weiyang Liu (CoRR, 2025)

Flipping Against All Odds: Reducing LLM Coin Flip Bias via Verbalized Rejection Sampling. - Tim Z. Xiao; Johannes Zenn; Zhen Liu 019; Weiyang Liu; Robert Bamler; Bernhard Schölkopf (CoRR, 2025)