Jeremiah Z. Liu

According to our database, Jeremiah Z. Liu authored at least 36 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction Using the Local Lipschitz.

Danyal F. Bhutto, Bo Zhu, Jeremiah Z. Liu, Neha Koonjoo, Hongwei Bran Li, Bruce R. Rosen, Matthew S. Rosen

IEEE J. Biomed. Health Informatics, September, 2024

RRM: Robust Reward Model Training Mitigates Reward Hacking.

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

CoRR, 2024

Robust Disaster Assessment from Aerial Imagery Using Text-to-Image Synthetic Data.

Tarun Kalluri, Jihyeon Lee, Kihyuk Sohn, Sahil Singla, Manmohan Chandraker, Joseph Xu, Jeremiah Z. Liu

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness.

Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan

J. Mach. Learn. Res., 2023

Uncertainty Estimation for Deep Learning Image Reconstruction using a Local Lipschitz Metric.

Danyal F. Bhutto, Bo Zhu, Jeremiah Z. Liu, Neha Koonjoo, Bruce R. Rosen, Matthew S. Rosen

CoRR, 2023

Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play.

Jeremiah Zhe Liu, Krishnamurthy (Dj) Dvijotham, Jihyeon Lee, Quan Yuan, Martin Strobel, Balaji Lakshminarayanan, Deepak Ramachandran

CoRR, 2023

A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models.

James Urquhart Allingham, Jie Ren, Michael W. Dusenberry, Xiuye Gu, Yin Cui, Dustin Tran, Jeremiah Zhe Liu, Balaji Lakshminarayanan

Proceedings of the International Conference on Machine Learning, 2023

Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play.

Jeremiah Zhe Liu, Krishnamurthy (Dj) Dvijotham, Jihyeon Lee, Quan Yuan, Balaji Lakshminarayanan, Deepak Ramachandran

Proceedings of the Eleventh International Conference on Learning Representations, 2023

On Compositional Uncertainty Quantification for Seq2seq Graph Parsing.

Zi Lin, Du Phan, Panupong Pasupat, Jeremiah Zhe Liu, Jingbo Shang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study.

Polina Zablotskaia, Du Phan, Joshua Maynez, Shashi Narayan, Jie Ren, Jeremiah Z. Liu

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

Retrieval-Augmented Parsing for Complex Graphs by Exploiting Structure and Uncertainty.

Zi Lin, Quan Yuan, Panupong Pasupat, Jeremiah Z. Liu, Jingbo Shang

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic.

Connor Pryor, Quan Yuan, Jeremiah Z. Liu, Mehran Kazemi, Deepak Ramachandran, Tania Bedrax-Weiss, Lise Getoor

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

2022

Deep Classifiers with Label Noise Modeling and Distance Awareness.

Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Zhe Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou

Trans. Mach. Learn. Res., 2022

Plex: Towards Reliability using Pretrained Large Model Extensions.

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

CoRR, 2022

Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees.

Wenying Deng, Beau Coker, Jeremiah Zhe Liu, Brent A. Coull

CoRR, 2022

Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees.

Wenying Deng, Beau Coker, Rajarshi Mukherjee, Jeremiah Z. Liu, Brent A. Coull

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification.

Zi Lin, Jeremiah Z. Liu, Jingbo Shang

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

Bayesian Nonparametric Model Averaging Using Scalable Gaussian Process Representations.

John W. Paisley, Sebastian Rowland, Jeremiah Zhe Liu, Brent A. Coull, Marianthi-Anna Kioumourtzoglou

Proceedings of the IEEE International Conference on Big Data, 2022

Towards Collaborative Neural-Symbolic Graph Semantic Parsing via Uncertainty.

Zi Lin, Jeremiah Zhe Liu, Jingbo Shang

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2022, 2022

2021

Reliable Graph Neural Networks for Drug Discovery Under Distributional Shift.

Kehang Han, Balaji Lakshminarayanan, Jeremiah Z. Liu

CoRR, 2021

Measuring and Improving Model-Moderator Collaboration using Uncertainty Estimation.

Ian D. Kivlichan, Zi Lin, Jeremiah Z. Liu, Lucy Vasserman

CoRR, 2021

A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection.

Jie Ren, Stanislav Fort, Jeremiah Z. Liu, Abhijit Guha Roy, Shreyas Padhy, Balaji Lakshminarayanan

CoRR, 2021

Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning.

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

CoRR, 2021

Training independent subnetworks for robust prediction.

Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew Mingbo Dai, Dustin Tran

Proceedings of the 9th International Conference on Learning Representations, 2021

Variable Selection with Rigorous Uncertainty Quantification using Deep Bayesian Neural Networks: Posterior Concentration and Bernstein-von Mises Phenomenon.

Jeremiah Z. Liu

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks.

Shreyas Padhy, Zachary Nado, Jie Ren, Jeremiah Z. Liu, Jasper Snoek, Balaji Lakshminarayanan

CoRR, 2020

Semi-Supervised Class Discovery.

Jeremy Nixon, Jeremiah Z. Liu, David Berthelot

CoRR, 2020

Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness.

Jeremiah Z. Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior.

Zi Lin, Jeremiah Z. Liu, Zi Yang, Nan Hua, Dan Roth

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, 2020

2019

Gaussian Process Regression and Classification under Mathematical Constraints with Learning Guarantees.

Jeremiah Zhe Liu

CoRR, 2019

Accurate Uncertainty Estimation and Decomposition in Ensemble Learning.

Jeremiah Z. Liu, John W. Paisley, Marianthi-Anna Kioumourtzoglou, Brent A. Coull

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Personalized treatment for type 2 diabetes using weighted k-nearest neighbors.

Wenyu Song, Linying Zhang, Emily Gill, Jeremiah Zhe Liu, Adam Wright

Proceedings of the AMIA 2019, 2019

2018

Image reconstruction by domain-transform manifold learning.

Bo Zhu, Jeremiah Z. Liu, Stephen F. Cauley, Bruce R. Rosen, Matthew S. Rosen

Nat., 2018

Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process.

Jeremiah Zhe Liu, John W. Paisley, Marianthi-Anna Kioumourtzoglou, Brent A. Coull

CoRR, 2018

2017

Image reconstruction by domain transform manifold learning.

Bo Zhu, Jeremiah Z. Liu, Bruce R. Rosen, Matthew S. Rosen

CoRR, 2017

Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes.

Jeremiah Z. Liu, Brent A. Coull

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017