Jeremiah Liu
11 papers · 2017–2023 · 4 venues · across top CS/AI venues
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Machine Learning
(11)
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(6)
Natural Language Processing
(3)
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uncertainty quantification
(4)
uncertainty estimation
(3)
model uncertainty
(3)
variable selection
(2)
bayesian neural network
(2)
bayesian inference
(2)
predictive uncertainty
(2)
bayesian nonparametrics
(2)
kernel methods
(2)
graph parsing
(2)
spectral normalization
(2)
feature importance
(1)
high-dimensional inference
(1)
out-of-distribution generalization
(1)
text summarization
(1)
ensemble learning
(1)
content moderation
(1)
model pruning
(1)
model calibration
(1)
nonparametric bayesian
(1)
Papers
On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study
EMNLP 2023
Retrieval-Augmented Parsing for Complex Graphs by Exploiting Structure and Uncertainty
EMNLP 2023
Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic
ACL 2023
Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification
EMNLP 2022
Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees
NeurIPS 2022
Measuring and Improving Model-Moderator Collaboration using Uncertainty Estimation
ACL 2021
Variable Selection with Rigorous Uncertainty Quantification using Deep Bayesian Neural Networks: Posterior Concentration and Bernstein-von Mises Phenomenon
AISTATS 2021
Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior
EMNLP 2020
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
NeurIPS 2020
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
NeurIPS 2019
Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes
NeurIPS 2017