Jeremiah Z. Liu

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 16

Top Topics

Gaussian Process

Hypothesis Test

Manifold Learning

Image Reconstruction

Top Venues

EMNLP (Findings)

Publications

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 (2024)
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 (2024)
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)
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)
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)
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 (2023)
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 (2023)
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)
Ian D. Kivlichan, Zi Lin, Jeremiah Z. Liu, Lucy Vasserman
Measuring and Improving Model-Moderator Collaboration using Uncertainty Estimation. CoRR (2021)
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)
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)
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 (2020)
Jeremiah Z. Liu, John W. Paisley, Marianthi-Anna Kioumourtzoglou, Brent A. Coull
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning. NeurIPS (2019)
Bo Zhu, Jeremiah Z. Liu, Stephen F. Cauley, Bruce R. Rosen, Matthew S. Rosen
Image reconstruction by domain-transform manifold learning. Nat. 555 (7697) (2018)
Jeremiah Z. Liu, Brent A. Coull
Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes. NIPS (2017)
Bo Zhu, Jeremiah Z. Liu, Bruce R. Rosen, Matthew S. Rosen
Image reconstruction by domain transform manifold learning. CoRR (2017)