Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes. - Jeremiah Z. Liu; Brent A. Coull (NIPS, 2017)

Image reconstruction by domain transform manifold learning. - Bo Zhu 004; Jeremiah Z. Liu; Bruce R. Rosen; Matthew S. Rosen (CoRR, 2017)

Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process. - Jeremiah Zhe Liu; John W. Paisley; Marianthi-Anna Kioumourtzoglou; Brent A. Coull (CoRR, 2018)

Image reconstruction by domain-transform manifold learning. - Bo Zhu 004; Jeremiah Z. Liu; Stephen F. Cauley; Bruce R. Rosen; Matthew S. Rosen (Nat., 2018)

Personalized treatment for type 2 diabetes using weighted k-nearest neighbors. - Wenyu Song; Linying Zhang; Emily Gill; Jeremiah Zhe Liu; Adam Wright (AMIA, 2019)

Accurate Uncertainty Estimation and Decomposition in Ensemble Learning. - Jeremiah Z. Liu; John W. Paisley; Marianthi-Anna Kioumourtzoglou; Brent A. Coull (NeurIPS, 2019)

Gaussian Process Regression and Classification under Mathematical Constraints with Learning Guarantees. - Jeremiah Zhe Liu (CoRR, 2019)

Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior. - Zi Lin; Jeremiah Z. Liu; Zi Yang; Nan Hua; Dan Roth (EMNLP (Findings), 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 (NeurIPS, 2020)

Semi-Supervised Class Discovery. - Jeremy Nixon; Jeremiah Z. Liu; David Berthelot (CoRR, 2020)

Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks. - Shreyas Padhy; Zachary Nado; Jie Ren 006; Jeremiah Z. Liu; Jasper Snoek; Balaji Lakshminarayanan (CoRR, 2020)

Variable Selection with Rigorous Uncertainty Quantification using Deep Bayesian Neural Networks: Posterior Concentration and Bernstein-von Mises Phenomenon. - Jeremiah Z. Liu (AISTATS, 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 (ICLR, 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; et al. (CoRR, 2021)

A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection. - Jie Ren 006; Stanislav Fort; Jeremiah Z. Liu; Abhijit Guha Roy; Shreyas Padhy; Balaji Lakshminarayanan (CoRR, 2021)

Measuring and Improving Model-Moderator Collaboration using Uncertainty Estimation. - Ian D. Kivlichan; Zi Lin; Jeremiah Z. Liu; Lucy Vasserman (CoRR, 2021)

Reliable Graph Neural Networks for Drug Discovery Under Distributional Shift. - Kehang Han; Balaji Lakshminarayanan; Jeremiah Z. Liu (CoRR, 2021)

Towards Collaborative Neural-Symbolic Graph Semantic Parsing via Uncertainty. - Zi Lin; Jeremiah Zhe Liu; Jingbo Shang (ACL (Findings), 2022)

Bayesian Nonparametric Model Averaging Using Scalable Gaussian Process Representations. - John W. Paisley; Sebastian Rowland; Jeremiah Zhe Liu; Brent A. Coull; Marianthi-Anna Kioumourtzoglou (IEEE Big Data, 2022)

Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification. - Zi Lin; Jeremiah Z. Liu; Jingbo Shang (EMNLP, 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 (NeurIPS, 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)

Plex: Towards Reliability using Pretrained Large Model Extensions. - Dustin Tran; Jeremiah Z. Liu; Michael W. Dusenberry; Du Phan; Mark Collier; Jie Ren 006; Kehang Han; Zi Wang; Zelda Mariet; Huiyi Hu; Neil Band; Tim G. J. Rudner; et al. (CoRR, 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)

Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic. - Connor Pryor; Quan Yuan 001; Jeremiah Z. Liu; Mehran Kazemi; Deepak Ramachandran; Tania Bedrax-Weiss; Lise Getoor (ACL (1), 2023)

Retrieval-Augmented Parsing for Complex Graphs by Exploiting Structure and Uncertainty. - Zi Lin; Quan Yuan 001; Panupong Pasupat; Jeremiah Z. Liu; Jingbo Shang (EMNLP (Findings), 2023)

On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study. - Polina Zablotskaia; Du Phan; Joshua Maynez; Shashi Narayan; Jie Ren 006; Jeremiah Z. Liu (EMNLP (Findings), 2023)

On Compositional Uncertainty Quantification for Seq2seq Graph Parsing. - Zi Lin; Du Phan; Panupong Pasupat; Jeremiah Zhe Liu; Jingbo Shang (ICLR, 2023)

Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play. - Jeremiah Zhe Liu; Krishnamurthy (Dj) Dvijotham; Jihyeon Lee; Quan Yuan 001; Balaji Lakshminarayanan; Deepak Ramachandran (ICLR, 2023)

A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models. - James Urquhart Allingham; Jie Ren 006; Michael W. Dusenberry; Xiuye Gu; Yin Cui; Dustin Tran; Jeremiah Zhe Liu; Balaji Lakshminarayanan (ICML, 2023)

Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play. - Jeremiah Zhe Liu; Krishnamurthy (Dj) Dvijotham; Jihyeon Lee; Quan Yuan 001; Martin Strobel 001; Balaji Lakshminarayanan; Deepak Ramachandran (CoRR, 2023)

Uncertainty Estimation for Deep Learning Image Reconstruction using a Local Lipschitz Metric. - Danyal F. Bhutto; Bo Zhu 004; Jeremiah Z. Liu; Neha Koonjoo; Bruce R. Rosen; Matthew S. Rosen (CoRR, 2023)

A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. - Jeremiah Zhe Liu; Shreyas Padhy; Jie Ren 006; Zi Lin; Yeming Wen; Ghassen Jerfel; Zachary Nado; Jasper Snoek; Dustin Tran; Balaji Lakshminarayanan (J. Mach. Learn. Res., 2023)

Robust Disaster Assessment from Aerial Imagery Using Text-to-Image Synthetic Data. - Tarun Kalluri; Jihyeon Lee; Kihyuk Sohn; Sahil Singla 005; Manmohan Chandraker; Joseph Xu; Jeremiah Z. Liu (CVPR Workshops, 2024)

RRM: Robust Reward Model Training Mitigates Reward Hacking. - Tianqi Liu 002; Wei Xiong 015; Jie Ren 006; Lichang Chen; Junru Wu; Rishabh Joshi; Yang Gao; Jiaming Shen; Zhen Qin 001; Tianhe Yu; Daniel Sohn; Anastasiia Makarova; et al. (CoRR, 2024)

Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction Using the Local Lipschitz. - Danyal F. Bhutto; Bo Zhu 011; Jeremiah Z. Liu; Neha Koonjoo; Hongwei Bran Li; Bruce R. Rosen; Matthew S. Rosen (IEEE J. Biomed. Health Informatics, 2024)

RRM: Robust Reward Model Training Mitigates Reward Hacking. - Tianqi Liu 002; Wei Xiong 015; Jie Ren 006; Lichang Chen; Junru Wu; Rishabh Joshi; Yang Gao; Jiaming Shen; Zhen Qin 001; Tianhe Yu; Daniel Sohn; Anastasia Makarova; et al. (ICLR, 2025)