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ORCIDJeremiah Z. Liu
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2020 – today
- 2024
[j4]Danyal F. Bhutto
, Bo Zhu, Jeremiah Z. Liu, Neha Koonjoo, Hongwei Bran Li, Bruce R. Rosen, Matthew S. Rosen:
Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction Using the Local Lipschitz. IEEE J. Biomed. Health Informatics 28(9): 5422-5434 (2024)- [c18]Tarun Kalluri, Jihyeon Lee, Kihyuk Sohn, Sahil Singla, Manmohan Chandraker, Joseph Xu, Jeremiah Z. Liu:
Robust Disaster Assessment from Aerial Imagery Using Text-to-Image Synthetic Data. CVPR Workshops 2024: 7449-7459 - [i26]Connor Pryor, Quan Yuan, Jeremiah Z. Liu, Mehran Kazemi, Deepak Ramachandran, Tania Bedrax-Weiss, Lise Getoor:
Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic. CoRR abs/2403.17853 (2024) - [i25]Tarun Kalluri, Jihyeon Lee, Kihyuk Sohn, Sahil Singla, Manmohan Chandraker, Joseph Xu, Jeremiah Z. Liu:
Robust Disaster Assessment from Aerial Imagery Using Text-to-Image Synthetic Data. CoRR abs/2405.13779 (2024) - [i24]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:
RRM: Robust Reward Model Training Mitigates Reward Hacking. CoRR abs/2409.13156 (2024) - 2023
- [j3]Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan:
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. J. Mach. Learn. Res. 24: 42:1-42:63 (2023) - [c17]Connor Pryor, Quan Yuan, Jeremiah Z. Liu, Mehran Kazemi, Deepak Ramachandran, Tania Bedrax-Weiss, Lise Getoor:
Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic. ACL (1) 2023: 7631-7652 - [c16]Polina Zablotskaia, Du Phan, Joshua Maynez, Shashi Narayan, Jie Ren, Jeremiah Z. Liu:
On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study. EMNLP (Findings) 2023: 2980-2992 - [c15]Zi Lin, Quan Yuan, Panupong Pasupat, Jeremiah Z. Liu, Jingbo Shang:
Retrieval-Augmented Parsing for Complex Graphs by Exploiting Structure and Uncertainty. EMNLP (Findings) 2023: 6330-6345 - [c14]Zi Lin, Du Phan, Panupong Pasupat, Jeremiah Zhe Liu, Jingbo Shang:
On Compositional Uncertainty Quantification for Seq2seq Graph Parsing. ICLR 2023 - [c13]Jeremiah Zhe Liu, Krishnamurthy (Dj) Dvijotham, Jihyeon Lee, Quan Yuan, Balaji Lakshminarayanan, Deepak Ramachandran:
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play. ICLR 2023 - [c12]James Urquhart Allingham, Jie Ren, Michael W. Dusenberry, Xiuye Gu, Yin Cui, Dustin Tran, Jeremiah Zhe Liu, Balaji Lakshminarayanan:
A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models. ICML 2023: 547-568 - [i23]Zi Lin, Jeremiah Z. Liu, Jingbo Shang:
Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification. CoRR abs/2301.11459 (2023) - [i22]Jeremiah Zhe Liu, Krishnamurthy (Dj) Dvijotham, Jihyeon Lee, Quan Yuan, Martin Strobel, Balaji Lakshminarayanan, Deepak Ramachandran:
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play. CoRR abs/2302.05807 (2023) - [i21]James Urquhart Allingham, Jie Ren, Michael W. Dusenberry, Jeremiah Zhe Liu, Xiuye Gu, Yin Cui, Dustin Tran, Balaji Lakshminarayanan:
A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models. CoRR abs/2302.06235 (2023) - [i20]Polina Zablotskaia, Du Phan, Joshua Maynez, Shashi Narayan, Jie Ren, Jeremiah Z. Liu:
On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study. CoRR abs/2304.08653 (2023) - [i19]Danyal F. Bhutto, Bo Zhu, Jeremiah Z. Liu, Neha Koonjoo, Bruce R. Rosen, Matthew S. Rosen:
Uncertainty Estimation for Deep Learning Image Reconstruction using a Local Lipschitz Metric. CoRR abs/2305.07618 (2023) - 2022
- [j2]Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Zhe Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou:
Deep Classifiers with Label Noise Modeling and Distance Awareness. Trans. Mach. Learn. Res. 2022 (2022) - [c11]Zi Lin, Jeremiah Zhe Liu, Jingbo Shang:
Towards Collaborative Neural-Symbolic Graph Semantic Parsing via Uncertainty. ACL (Findings) 2022: 4160-4173 - [c10]John W. Paisley, Sebastian Rowland, Jeremiah Zhe Liu, Brent A. Coull, Marianthi-Anna Kioumourtzoglou:
Bayesian Nonparametric Model Averaging Using Scalable Gaussian Process Representations. IEEE Big Data 2022: 55-64 - [c9]Zi Lin, Jeremiah Z. Liu, Jingbo Shang:
Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification. EMNLP 2022: 4759-4776 - [c8]Wenying Deng, Beau Coker, Rajarshi Mukherjee, Jeremiah Z. Liu, Brent A. Coull:
Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees. NeurIPS 2022 - [i18]Wenying Deng, Beau Coker, Jeremiah Zhe Liu, Brent A. Coull:
Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees. CoRR abs/2204.07293 (2022) - [i17]Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zack Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan:
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. CoRR abs/2205.00403 (2022) - [i16]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:
Plex: Towards Reliability using Pretrained Large Model Extensions. CoRR abs/2207.07411 (2022) - 2021
- [c7]Jeremiah Z. Liu:
Variable Selection with Rigorous Uncertainty Quantification using Deep Bayesian Neural Networks: Posterior Concentration and Bernstein-von Mises Phenomenon. AISTATS 2021: 3124-3132 - [c6]Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew Mingbo Dai, Dustin Tran:
Training independent subnetworks for robust prediction. ICLR 2021 - [i15]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:
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. CoRR abs/2106.04015 (2021) - [i14]Jie Ren, Stanislav Fort, Jeremiah Z. Liu, Abhijit Guha Roy, Shreyas Padhy, Balaji Lakshminarayanan:
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection. CoRR abs/2106.09022 (2021) - [i13]Ian D. Kivlichan, Zi Lin, Jeremiah Z. Liu, Lucy Vasserman:
Measuring and Improving Model-Moderator Collaboration using Uncertainty Estimation. CoRR abs/2107.04212 (2021) - [i12]Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Z. Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou:
Deep Classifiers with Label Noise Modeling and Distance Awareness. CoRR abs/2110.02609 (2021) - [i11]Kehang Han, Balaji Lakshminarayanan, Jeremiah Z. Liu:
Reliable Graph Neural Networks for Drug Discovery Under Distributional Shift. CoRR abs/2111.12951 (2021) - 2020
- [c5]Zi Lin, Jeremiah Z. Liu, Zi Yang, Nan Hua, Dan Roth:
Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior. EMNLP (Findings) 2020: 719-730 - [c4]Jeremiah Z. Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan:
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. NeurIPS 2020 - [i10]Jeremy Nixon, Jeremiah Z. Liu, David Berthelot:
Semi-Supervised Class Discovery. CoRR abs/2002.03480 (2020) - [i9]Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan:
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. CoRR abs/2006.10108 (2020) - [i8]Shreyas Padhy, Zachary Nado, Jie Ren, Jeremiah Z. Liu, Jasper Snoek, Balaji Lakshminarayanan:
Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks. CoRR abs/2007.05134 (2020) - [i7]Zi Lin, Jeremiah Zhe Liu, Zi Yang, Nan Hua, Dan Roth:
Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior. CoRR abs/2010.01791 (2020) - [i6]Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M. Dai, Dustin Tran:
Training independent subnetworks for robust prediction. CoRR abs/2010.06610 (2020)
2010 – 2019
- 2019
- [c3]Wenyu Song, Linying Zhang, Emily Gill, Jeremiah Zhe Liu, Adam Wright:
Personalized treatment for type 2 diabetes using weighted k-nearest neighbors. AMIA 2019 - [c2]Jeremiah Z. Liu, John W. Paisley, Marianthi-Anna Kioumourtzoglou, Brent A. Coull:
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning. NeurIPS 2019: 8950-8961 - [i5]Jeremiah Zhe Liu:
Gaussian Process Regression and Classification under Mathematical Constraints with Learning Guarantees. CoRR abs/1904.09632 (2019) - [i4]Jeremiah Zhe Liu, John W. Paisley, Marianthi-Anna Kioumourtzoglou, Brent A. Coull:
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning. CoRR abs/1911.04061 (2019) - [i3]Jeremiah Zhe Liu:
Variable Selection with Rigorous Uncertainty Quantification using Deep Bayesian Neural Networks: Posterior Concentration and Bernstein-von Mises Phenomenon. CoRR abs/1912.01189 (2019) - 2018
- [j1]Bo Zhu, Jeremiah Z. Liu, Stephen F. Cauley, Bruce R. Rosen, Matthew S. Rosen:
Image reconstruction by domain-transform manifold learning. Nat. 555(7697): 487-492 (2018) - [i2]Jeremiah Zhe Liu, John W. Paisley, Marianthi-Anna Kioumourtzoglou, Brent A. Coull:
Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process. CoRR abs/1812.03350 (2018) - 2017
- [c1]Jeremiah Z. Liu, Brent A. Coull:
Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes. NIPS 2017: 795-803 - [i1]Bo Zhu, Jeremiah Z. Liu, Bruce R. Rosen, Matthew S. Rosen:
Image reconstruction by domain transform manifold learning. CoRR abs/1704.08841 (2017)
Coauthor Index
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