Jie Ren

According to our database, Jie Ren authored at least 31 papers between 2012 and 2024.

Collaborative distances:

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

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

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

Morse Neural Networks for Uncertainty Quantification.

Benoit Dherin, Huiyi Hu, Jie Ren, Michael W. Dusenberry, Balaji Lakshminarayanan

CoRR, 2023

Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image Models.

Yunhao Ge, Jie Ren, Jiaping Zhao, Kaifeng Chen, Andrew Gallagher, Laurent Itti, Balaji Lakshminarayanan

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

Out-of-Distribution Detection and Selective Generation for Conditional Language Models.

Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna, Mohammad Saleh, Balaji Lakshminarayanan, Peter J. Liu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Self-Evaluation Improves Selective Generation in Large Language Models.

Jie Ren, Yao Zhao, Tu Vu, Peter J. Liu, Balaji Lakshminarayanan

Proceedings of the Proceedings on "I Can't Believe It's Not Better: Failure Modes in the Age of Foundation Models" at NeurIPS 2023 Workshops, 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

Improving the Robustness of Summarization Models by Detecting and Removing Input Noise.

Kundan Krishna, Yao Zhao, Jie Ren, Balaji Lakshminarayanan, Jiaming Luo, Mohammad Saleh, Peter J. Liu

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

Improving Zero-shot Generalization and Robustness of Multi-Modal Models.

Yunhao Ge, Jie Ren, Andrew Gallagher, Yuxiao Wang, Ming-Hsuan Yang, Hartwig Adam, Laurent Itti, Balaji Lakshminarayanan, Jiaping Zhao

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Does your dermatology classifier know what it doesn't know? Detecting the long-tail of unseen conditions.

Abhijit Guha Roy, Jie Ren, Shekoofeh Azizi, Aaron Loh, Vivek Natarajan, Basil Mustafa, Nick Pawlowski, Jan Freyberg, Yuan Liu, Zachary Beaver, Nam Vo, Peggy Bui, Samantha Winter, Patricia MacWilliams, Gregory S. Corrado, Umesh Telang, Yun Liu, A. Taylan Cemgil, Alan Karthikesalingam, Balaji Lakshminarayanan, Jim Winkens

Medical Image Anal., 2022

A New Context Tree Inference Algorithm for Variable Length Markov Chain Model with Applications to Biological Sequence Analyses.

Shaokun An, Jie Ren, Fengzhu Sun, Lin Wan

J. Comput. Biol., 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

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

KIMI: Knockoff Inference for Motif Identification from molecular sequences with controlled false discovery rate.

Xin Bai, Jie Ren, Yingying Fan, Fengzhu Sun

Bioinform., 2021

Exploring the Limits of Out-of-Distribution Detection.

Stanislav Fort, Jie Ren, Balaji Lakshminarayanan

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Confidence intervals for Markov chain transition probabilities based on next generation sequencing reads data.

Lin Wan, Xin Kang, Jie Ren, Fengzhu Sun

Quant. Biol., 2020

Identifying viruses from metagenomic data using deep learning.

Jie Ren, Kai Song, Chao Deng, Nathan A. Ahlgren, Jed A. Fuhrman, Yi Li, Xiaohui Xie, Ryan Poplin, Fengzhu Sun

Quant. Biol., 2020

Predicting the Number of Bases to Attain Sufficient Coverage in High-Throughput Sequencing Experiments.

Chao Deng, Timothy Daley, Peter Calabrese, Jie Ren, Andrew D. Smith

J. Comput. Biol., 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

2019

SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders.

Peter J. Liu, Yu-An Chung, Jie Ren

CoRR, 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, Zachary Nado

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

Likelihood Ratios for Out-of-Distribution Detection.

Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan

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

2017

CAFE: aCcelerated Alignment-FrEe sequence analysis.

Yang Young Lu, Kujin Tang, Jie Ren, Jed A. Fuhrman, Michael S. Waterman, Fengzhu Sun

Nucleic Acids Res., 2017

Prediction of virus-host infectious association by supervised learning methods.

Mengge Zhang, Lianping Yang, Jie Ren, Nathan A. Ahlgren, Jed A. Fuhrman, Fengzhu Sun

BMC Bioinform., 2017

2016

Inference of Markovian properties of molecular sequences from NGS data and applications to comparative genomics.

Jie Ren, Kai Song, Minghua Deng, Gesine Reinert, Charles H. Cannon, Fengzhu Sun

Bioinform., 2016

2014

New developments of alignment-free sequence comparison: measures, statistics and next-generation sequencing.

Kai Song, Jie Ren, Gesine Reinert, Minghua Deng, Michael S. Waterman, Fengzhu Sun

Briefings Bioinform., 2014

2013

Alignment-Free Sequence Comparison Based on Next-Generation Sequencing Reads.

Kai Song, Jie Ren, Zhiyuan Zhai, Xuemei Liu, Minghua Deng, Fengzhu Sun

J. Comput. Biol., 2013

Multiple alignment-free sequence comparison.

Jie Ren, Kai Song, Fengzhu Sun, Minghua Deng, Gesine Reinert

Bioinform., 2013

2012

Alignment-Free Sequence Comparison Based on Next Generation Sequencing Reads: Extended Abstract.

Kai Song, Jie Ren, Zhiyuan Zhai, Xuemei Liu, Minghua Deng, Fengzhu Sun

Proceedings of the Research in Computational Molecular Biology, 2012