Compression with Bayesian Implicit Neural Representations. - Zongyu Guo; Gergely Flamich; Jiajun He 003; Zhibo Chen 001; José Miguel Hernández-Lobato (NeurIPS, 2023)

RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations. - Jiajun He 003; Gergely Flamich; Zongyu Guo; José Miguel Hernández-Lobato (ICLR, 2024)

Accelerating Relative Entropy Coding with Space Partitioning. - Jiajun He 003; Gergely Flamich; José Miguel Hernández-Lobato (NeurIPS, 2024)

Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models. - Fengzhe Zhang; Jiajun He 003; Laurence I. Midgley; Javier Antorán; José Miguel Hernández-Lobato (CoRR, 2024)

Getting Free Bits Back from Rotational Symmetries in LLMs. - Jiajun He 003; Gergely Flamich; José Miguel Hernández-Lobato (CoRR, 2024)

Training Neural Samplers with Reverse Diffusive KL Divergence. - Jiajun He 003; Wenlin Chen; Mingtian Zhang; David Barber; José Miguel Hernández-Lobato (AISTATS, 2025)

No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers. - Jiajun He 003; Yuanqi Du; Francisco Vargas 001; Dinghuai Zhang; Shreyas Padhy; Ruikang Ouyang; Carla P. Gomes; José Miguel Hernández-Lobato (CoRR, 2025)

Towards Training One-Step Diffusion Models Without Distillation. - Mingtian Zhang; Jiajun He 003; Wenlin Chen; Zijing Ou; José Miguel Hernández-Lobato; Bernhard Schölkopf; David Barber (CoRR, 2025)

FEAT: Free energy Estimators with Adaptive Transport. - Jiajun He 003; Yuanqi Du; Francisco Vargas 001; Yuanqing Wang; Carla P. Gomes; José Miguel Hernández-Lobato; Eric Vanden-Eijnden (CoRR, 2025)

Progressive Tempering Sampler with Diffusion. - Severi Rissanen; Ruikang Ouyang; Jiajun He 003; Wenlin Chen; Markus Heinonen; Arno Solin; José Miguel Hernández-Lobato (CoRR, 2025)

RNE: a plug-and-play framework for diffusion density estimation and inference-time control. - Jiajun He 003; José Miguel Hernández-Lobato; Yuanqi Du; Francisco Vargas 001 (CoRR, 2025)