Yuheng Bu
18 papers · 2020–2026 · 7 venues · across top CS/AI venues
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Research topics
Machine Learning
(16)
Optimization & Theory
(11)
Artificial Intelligence
(10)
Core AI
(7)
Application Areas
(6)
Deep Learning
(5)
Core Methods
(5)
Learning Types
(5)
Mathematics & Optimization
(3)
Bayesian & Probabilistic Β· Artificial Intelligence
(3)
Mathematics
(3)
Natural Language Processing
(2)
Architectures
(2)
Computer Vision
(1)
Bayesian & Probabilistic Β· Machine Learning
(1)
Keywords
generalization error
(5)
uncertainty quantification
(4)
gibbs algorithm
(4)
information theory
(4)
mutual information
(3)
transfer learning
(2)
out-of-distribution detection
(2)
conditional mutual information
(2)
kl divergence
(2)
fairness criterion
(2)
bias-variance tradeoff
(2)
conformal prediction
(1)
gradient-based optimization
(1)
epistemic uncertainty
(1)
policy gradient
(1)
semi-supervised learning
(1)
learning theory
(1)
k-means clustering
(1)
markov decision process
(1)
robustness
(1)
Papers
A Reinforcement Learning Framework for Robust and Secure LLM Watermarking
EACL 2026
TrustEnergy: A Unified Framework for Accurate and Reliable User-level Energy Usage Prediction
AAAI 2026
Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective
ICML 2025
Image Watermarks are Removable using Controllable Regeneration from Clean Noise
ICLR 2025
Adaptive Text Watermark for Large Language Models
ICML 2024
Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?
NeurIPS 2024
Gibbs-Based Information Criteria and the Over-Parameterized Regime
AISTATS 2024
Information-Theoretic Opacity-Enforcement in Markov Decision Processes
IJCAI 2024
Operator SVD with Neural Networks via Nested Low-Rank Approximation
ICML 2024
How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm?
AISTATS 2023
On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation
ICML 2023
Post-hoc Uncertainty Learning Using a Dirichlet Meta-Model
AAAI 2023
Reliable Gradient-free and Likelihood-free Prompt Tuning
EACL 2023
Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm
AISTATS 2022
Selective Regression under Fairness Criteria
ICML 2022
An Exact Characterization of the Generalization Error for the Gibbs Algorithm
NeurIPS 2021
Fair Selective Classification Via Sufficiency
ICML 2021
Information-Theoretic Understanding of Population Risk Improvement with Model Compression
AAAI 2020