Jeremiah Z. Liu

28 papers A* 4A 1Journal 17Unranked 6

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	RRM: Robust Reward Model Training Mitigates Reward Hacking. CoRR Tianqi Liu, Wei Xiong, Jie Ren, Lichang Chen, Junru Wu, Rishabh Joshi, Yang Gao, Jiaming Shen, Zhen Qin, Tianhe Yu, Daniel Sohn, Anastasiia Makarova, Jeremiah Z. Liu, Yuan Liu, Bilal Piot, Abe Ittycheriah, Aviral Kumar, Mohammad Saleh
2024	—	`conf`	Robust Disaster Assessment from Aerial Imagery Using Text-to-Image Synthetic Data. CVPR Workshops Tarun Kalluri, Jihyeon Lee, Kihyuk Sohn, Sahil Singla, Manmohan Chandraker, Joseph Xu, Jeremiah Z. Liu
2024	J	`jnl`	Robust Disaster Assessment from Aerial Imagery Using Text-to-Image Synthetic Data. CoRR Tarun Kalluri, Jihyeon Lee, Kihyuk Sohn, Sahil Singla, Manmohan Chandraker, Joseph Xu, Jeremiah Z. Liu
2024	J	`jnl`	Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction Using the Local Lipschitz. IEEE J. Biomed. Health Informatics Danyal F. Bhutto, Bo Zhu, Jeremiah Z. Liu, Neha Koonjoo, Hongwei Bran Li, Bruce R. Rosen, Matthew S. Rosen
2024	J	`jnl`	Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic. CoRR Connor Pryor, Quan Yuan, Jeremiah Z. Liu, Mehran Kazemi, Deepak Ramachandran, Tania Bedrax-Weiss, Lise Getoor
2023	J	`jnl`	Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification. CoRR Zi Lin, Jeremiah Z. Liu, Jingbo Shang
2023	—	`conf`	On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study. EMNLP (Findings) Polina Zablotskaia, Du Phan, Joshua Maynez, Shashi Narayan, Jie Ren, Jeremiah Z. Liu
2023	J	`jnl`	On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study. CoRR Polina Zablotskaia, Du Phan, Joshua Maynez, Shashi Narayan, Jie Ren, Jeremiah Z. Liu
2023	—	`conf`	Retrieval-Augmented Parsing for Complex Graphs by Exploiting Structure and Uncertainty. EMNLP (Findings) Zi Lin, Quan Yuan, Panupong Pasupat, Jeremiah Z. Liu, Jingbo Shang
2023	J	`jnl`	Uncertainty Estimation for Deep Learning Image Reconstruction using a Local Lipschitz Metric. CoRR Danyal F. Bhutto, Bo Zhu, Jeremiah Z. Liu, Neha Koonjoo, Bruce R. Rosen, Matthew S. Rosen
2023	—	`conf`	Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic. ACL (1) Connor Pryor, Quan Yuan, Jeremiah Z. Liu, Mehran Kazemi, Deepak Ramachandran, Tania Bedrax-Weiss, Lise Getoor
2022	A*	`conf`	Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification. EMNLP Zi Lin, Jeremiah Z. Liu, Jingbo Shang
2022	J	`jnl`	Plex: Towards Reliability using Pretrained Large Model Extensions. CoRR Dustin Tran, Jeremiah Z. Liu, Michael W. Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, Kelly Buchanan, Kevin Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan
2022	A*	`conf`	Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees. NeurIPS Wenying Deng, Beau Coker, Rajarshi Mukherjee, Jeremiah Z. Liu, Brent A. Coull
2021	J	`jnl`	A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection. CoRR Jie Ren, Stanislav Fort, Jeremiah Z. Liu, Abhijit Guha Roy, Shreyas Padhy, Balaji Lakshminarayanan
2021	J	`jnl`	Deep Classifiers with Label Noise Modeling and Distance Awareness. CoRR Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Z. Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou
2021	J	`jnl`	Measuring and Improving Model-Moderator Collaboration using Uncertainty Estimation. CoRR Ian D. Kivlichan, Zi Lin, Jeremiah Z. Liu, Lucy Vasserman
2021	J	`jnl`	Reliable Graph Neural Networks for Drug Discovery Under Distributional Shift. CoRR Kehang Han, Balaji Lakshminarayanan, Jeremiah Z. Liu
2021	J	`jnl`	Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. CoRR Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Z. Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, Dustin Tran
2021	A	`conf`	Variable Selection with Rigorous Uncertainty Quantification using Deep Bayesian Neural Networks: Posterior Concentration and Bernstein-von Mises Phenomenon. AISTATS Jeremiah Z. Liu
2020	—	`conf`	Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior. EMNLP (Findings) Zi Lin, Jeremiah Z. Liu, Zi Yang, Nan Hua, Dan Roth
2020	J	`jnl`	Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks. CoRR Shreyas Padhy, Zachary Nado, Jie Ren, Jeremiah Z. Liu, Jasper Snoek, Balaji Lakshminarayanan
2020	J	`jnl`	Semi-Supervised Class Discovery. CoRR Jeremy Nixon, Jeremiah Z. Liu, David Berthelot
2020	A*	`conf`	Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. NeurIPS Jeremiah Z. Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan
2019	A*	`conf`	Accurate Uncertainty Estimation and Decomposition in Ensemble Learning. NeurIPS Jeremiah Z. Liu, John W. Paisley, Marianthi-Anna Kioumourtzoglou, Brent A. Coull
2018	J	`jnl`	Image reconstruction by domain-transform manifold learning. Nat. Bo Zhu, Jeremiah Z. Liu, Stephen F. Cauley, Bruce R. Rosen, Matthew S. Rosen
2017	J	`jnl`	Image reconstruction by domain transform manifold learning. CoRR Bo Zhu, Jeremiah Z. Liu, Bruce R. Rosen, Matthew S. Rosen
2017	—	`conf`	Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes. NIPS Jeremiah Z. Liu, Brent A. Coull