Multi Robot Object-Based SLAM. - Siddharth Choudhary; Luca Carlone; Carlos Nieto; John G. Rogers III; Zhen Liu 019; Henrik I. Christensen; Frank Dellaert (ISER, 2016)

One-Shot Learning for Semantic Segmentation. - Amirreza Shaban; Shray Bansal; Zhen Liu 019; Irfan Essa; Byron Boots (BMVC, 2017)

Deep Hyperspherical Learning. - Weiyang Liu; Yan-Ming Zhang; Xingguo Li; Zhen Liu 019; Bo Dai 001; Tuo Zhao; Le Song (NIPS, 2017)

Decoupled Networks. - Weiyang Liu; Zhen Liu 019; Zhiding Yu; Bo Dai 001; Rongmei Lin; Yisen Wang 001; James M. Rehg; Le Song (CVPR, 2018)

SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation. - Bo Dai 001; Albert E. Shaw; Lihong Li 001; Lin Xiao; Niao He; Zhen Liu 019; Jianshu Chen; Le Song (ICML, 2018)

Towards Black-box Iterative Machine Teaching. - Weiyang Liu; Bo Dai 001; Xingguo Li; Zhen Liu 019; James M. Rehg; Le Song (ICML, 2018)

Deep Forward and Inverse Perceptual Models for Tracking and Prediction. - Alexander Lambert; Amirreza Shaban; Amit Raj; Zhen Liu 019; Byron Boots (ICRA, 2018)

Coupled Variational Bayes via Optimization Embedding. - Bo Dai 001; Hanjun Dai; Niao He; Weiyang Liu; Zhen Liu 019; Jianshu Chen; Lin Xiao; Le Song (NeurIPS, 2018)

Learning towards Minimum Hyperspherical Energy. - Weiyang Liu; Rongmei Lin; Zhen Liu 019; Lixin Liu; Zhiding Yu; Bo Dai 001; Le Song (NeurIPS, 2018)

Exponential Family Estimation via Adversarial Dynamics Embedding. - Bo Dai 001; Zhen Liu 019; Hanjun Dai; Niao He; Arthur Gretton; Le Song; Dale Schuurmans (NeurIPS, 2019)

Neural Similarity Learning. - Weiyang Liu; Zhen Liu 019; James M. Rehg; Le Song (NeurIPS, 2019)

Compressive Hyperspherical Energy Minimization. - Rongmei Lin; Weiyang Liu; Zhen Liu 019; Chen Feng 002; Zhiding Yu; James M. Rehg; Li Xiong 001; Le Song (CoRR, 2019)

Regularizing Neural Networks via Minimizing Hyperspherical Energy. - Rongmei Lin; Weiyang Liu; Zhen Liu 019; Chen Feng 002; Zhiding Yu; James M. Rehg; Li Xiong 001; Le Song (CVPR, 2020)

Orthogonal Over-Parameterized Training. - Weiyang Liu; Rongmei Lin; Zhen Liu 019; James M. Rehg; Li Xiong 001; Le Song (CoRR, 2020)

Learning with Hyperspherical Uniformity. - Weiyang Liu; Rongmei Lin; Zhen Liu 019; Li Xiong 001; Bernhard Schölkopf; Adrian Weller (AISTATS, 2021)

Orthogonal Over-Parameterized Training. - Weiyang Liu; Rongmei Lin; Zhen Liu 019; James M. Rehg; Liam Paull; Li Xiong 001; Le Song; Adrian Weller (CVPR, 2021)

Iterative Teaching by Label Synthesis. - Weiyang Liu; Zhen Liu 019; Hanchen Wang 002; Liam Paull; Bernhard Schölkopf; Adrian Weller (NeurIPS, 2021)

A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning. - Mingde Zhao 001; Zhen Liu 019; Sitao Luan; Shuyuan Zhang; Doina Precup; Yoshua Bengio (NeurIPS, 2021)

Structural Causal 3D Reconstruction. - Weiyang Liu; Zhen Liu 019; Liam Paull; Adrian Weller; Bernhard Schölkopf (ECCV (1), 2022)

Generative Flow Networks for Discrete Probabilistic Modeling. - Dinghuai Zhang; Nikolay Malkin; Zhen Liu 019; Alexandra Volokhova; Aaron C. Courville; Yoshua Bengio (ICML, 2022)

Expressiveness and Learnability: A Unifying View for Evaluating Self-Supervised Learning. - Yuchen Lu; Zhen Liu 019; Aristide Baratin; Romain Laroche; Aaron C. Courville; Alessandro Sordoni (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)

MeshDiffusion: Score-based Generative 3D Mesh Modeling. - Zhen Liu 019; Yao Feng 001; Michael J. Black; Derek Nowrouzezahrai; Liam Paull; Weiyang Liu (ICLR, 2023)

Controlling Text-to-Image Diffusion by Orthogonal Finetuning. - Zeju Qiu; Weiyang Liu; Haiwen Feng; Yuxuan Xue; Yao Feng 001; Zhen Liu 019; Dan Zhang; Adrian Weller; Bernhard Schölkopf (NeurIPS, 2023)

Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods. - Yuchen Lu; Zhen Liu 019; Aristide Baratin; Romain Laroche; Aaron C. Courville; Alessandro Sordoni (Trans. Mach. Learn. Res., 2023)

Continual Learning by Modeling Intra-Class Variation. - Longhui Yu; Tianyang Hu; Lanqing Hong; Zhen Liu 019; Adrian Weller; Weiyang Liu (Trans. Mach. Learn. Res., 2023)

Ghost on the Shell: An Expressive Representation of General 3D Shapes. - Zhen Liu 019; Yao Feng 001; Yuliang Xiu; Weiyang Liu; Liam Paull; Michael J. Black; Bernhard Schölkopf (ICLR, 2024)

Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization. - Weiyang Liu; Zeju Qiu; Yao Feng 001; Yuliang Xiu; Yuxuan Xue; Longhui Yu; Haiwen Feng; Zhen Liu 019; Juyeon Heo; Songyou Peng; Yandong Wen; Michael J. Black; et al. (ICLR, 2024)

PuzzleAvatar: Assembling 3D Avatars from Personal Albums. - Yuliang Xiu; Yufei Ye; Zhen Liu 019; Dimitrios Tzionas; Michael J. Black (CoRR, 2024)

PuzzleAvatar: Assembling 3D Avatars from Personal Albums. - Yuliang Xiu; Yufei Ye; Zhen Liu 019; Dimitris Tzionas; Michael J. Black (ACM Trans. Graph., 2024)

ChatGarment: Garment Estimation, Generation and Editing via Large Language Models. - Siyuan Bian; Chenghao Xu; Yuliang Xiu; Artur Grigorev 002; Zhen Liu 019; Cewu Lu; Michael J. Black; Yao Feng 001 (CVPR, 2025)

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

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)