Alignment-Free Sequence Comparison Based on Next Generation Sequencing Reads: Extended Abstract. - Kai Song; Jie Ren 006; Zhiyuan Zhai; Xuemei Liu; Minghua Deng; Fengzhu Sun (RECOMB, 2012)

Multiple alignment-free sequence comparison. - Jie Ren 006; Kai Song; Fengzhu Sun; Minghua Deng; Gesine Reinert (Bioinform., 2013)

Alignment-Free Sequence Comparison Based on Next-Generation Sequencing Reads. - Kai Song; Jie Ren 006; Zhiyuan Zhai; Xuemei Liu; Minghua Deng; Fengzhu Sun (J. Comput. Biol., 2013)

New developments of alignment-free sequence comparison: measures, statistics and next-generation sequencing. - Kai Song; Jie Ren 006; Gesine Reinert; Minghua Deng; Michael S. Waterman; Fengzhu Sun (Briefings Bioinform., 2014)

Inference of Markovian properties of molecular sequences from NGS data and applications to comparative genomics. - Jie Ren 006; Kai Song; Minghua Deng; Gesine Reinert; Charles H. Cannon; Fengzhu Sun (Bioinform., 2016)

Prediction of virus-host infectious association by supervised learning methods. - Mengge Zhang; Lianping Yang; Jie Ren 006; Nathan A. Ahlgren; Jed A. Fuhrman; Fengzhu Sun (BMC Bioinform., 2017)

CAFE: aCcelerated Alignment-FrEe sequence analysis. - Yang Young Lu; Kujin Tang; Jie Ren 006; Jed A. Fuhrman; Michael S. Waterman; Fengzhu Sun (Nucleic Acids Res., 2017)

Likelihood Ratios for Out-of-Distribution Detection. - Jie Ren 006; Peter J. Liu; Emily Fertig; Jasper Snoek; Ryan Poplin; Mark A. DePristo; Joshua V. Dillon; Balaji Lakshminarayanan (NeurIPS, 2019)

Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift. - Jasper Snoek; Yaniv Ovadia; Emily Fertig; Balaji Lakshminarayanan; Sebastian Nowozin; D. Sculley; Joshua V. Dillon; Jie Ren 006; Zachary Nado (NeurIPS, 2019)

SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders. - Peter J. Liu; Yu-An Chung; Jie Ren 006 (CoRR, 2019)

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)

Predicting the Number of Bases to Attain Sufficient Coverage in High-Throughput Sequencing Experiments. - Chao Deng; Timothy Daley; Peter Calabrese; Jie Ren 006; Andrew D. Smith (J. Comput. Biol., 2020)

Identifying viruses from metagenomic data using deep learning. - Jie Ren 006; Kai Song; Chao Deng; Nathan A. Ahlgren; Jed A. Fuhrman; Yi Li; Xiaohui Xie; Ryan Poplin; Fengzhu Sun (Quant. Biol., 2020)

Confidence intervals for Markov chain transition probabilities based on next generation sequencing reads data. - Lin Wan; Xin Kang; Jie Ren 006; Fengzhu Sun (Quant. Biol., 2020)

Exploring the Limits of Out-of-Distribution Detection. - Stanislav Fort; Jie Ren 006; Balaji Lakshminarayanan (NeurIPS, 2021)

KIMI: Knockoff Inference for Motif Identification from molecular sequences with controlled false discovery rate. - Xin Bai 005; Jie Ren 006; Yingying Fan; Fengzhu Sun (Bioinform., 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)

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)

A New Context Tree Inference Algorithm for Variable Length Markov Chain Model with Applications to Biological Sequence Analyses. - Shaokun An; Jie Ren 006; Fengzhu Sun; Lin Wan (J. Comput. Biol., 2022)

Does your dermatology classifier know what it doesn't know? Detecting the long-tail of unseen conditions. - Abhijit Guha Roy; Jie Ren 006; Shekoofeh Azizi; Aaron Loh; Vivek Natarajan; Basil Mustafa; Nick Pawlowski; Jan Freyberg; Yuan Liu; Zachary Beaver; Nam Vo; Peggy Bui; et al. (Medical Image Anal., 2022)

Improving Zero-shot Generalization and Robustness of Multi-Modal Models. - Yunhao Ge; Jie Ren 006; Andrew Gallagher; Yuxiao Wang 001; Ming-Hsuan Yang 001; Hartwig Adam; Laurent Itti; Balaji Lakshminarayanan; Jiaping Zhao (CVPR, 2023)

Improving the Robustness of Summarization Models by Detecting and Removing Input Noise. - Kundan Krishna; Yao Zhao; Jie Ren 006; Balaji Lakshminarayanan; Jiaming Luo; Mohammad Saleh; Peter J. Liu (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)

Self-Evaluation Improves Selective Generation in Large Language Models. - Jie Ren 006; Yao Zhao; Tu Vu; Peter J. Liu; Balaji Lakshminarayanan (ICBINB, 2023)

Out-of-Distribution Detection and Selective Generation for Conditional Language Models. - Jie Ren 006; Jiaming Luo; Yao Zhao; Kundan Krishna; Mohammad Saleh; Balaji Lakshminarayanan; Peter J. Liu (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)

Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image Models. - Yunhao Ge; Jie Ren 006; Jiaping Zhao; Kaifeng Chen; Andrew Gallagher; Laurent Itti; Balaji Lakshminarayanan (CoRR, 2023)

Morse Neural Networks for Uncertainty Quantification. - Benoit Dherin; Huiyi Hu; Jie Ren 006; Michael W. Dusenberry; Balaji Lakshminarayanan (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)

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)

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)