Henry Lam
19 papers · 2013–2026 · 7 venues · across top CS/AI venues
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Research topics
Machine Learning
(17)
Optimization & Theory
(15)
Artificial Intelligence
(6)
Mathematics & Optimization
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(6)
Core AI
(4)
Core Methods
(4)
Optimization
(4)
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(3)
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Bayesian & Probabilistic
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Data Science & Analytics
(1)
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Keywords
uncertainty quantification
(4)
stochastic gradient descent
(2)
importance sampling
(2)
bootstrap method
(2)
model misspecification
(2)
bayesian inference
(2)
regret bound
(2)
generalization bound
(2)
confidence interval
(2)
ambiguity set
(2)
distributionally robust optimization
(2)
empirical risk minimization
(2)
ensemble learning
(1)
policy optimization
(1)
approximate inference
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reinforcement learning
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stochastic optimization
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online learning
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Papers
Cheap Bootstrap for Fast Uncertainty Quantification of Stochastic Gradient Descent
JMLR 2026
Dissecting the Impact of Model Misspecification in Data-Driven Optimization
AISTATS 2025
MallowsPO: Fine-Tune Your LLM with Preference Dispersions
ICLR 2025
Is Cross-validation the Gold Standard to Estimate Out-of-sample Model Performance?
NeurIPS 2024
Learning from Sparse Offline Datasets via Conservative Density Estimation
ICLR 2024
Bootstrap in High Dimension with Low Computation
ICML 2023
Detection of Short-Term Temporal Dependencies in Hawkes Processes with Heterogeneous Background Dynamics
UAI 2023
Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence
JMLR 2023
Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks
NeurIPS 2023
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables
AISTATS 2023
Hedging against Complexity: Distributionally Robust Optimization with Parametric Approximation
AISTATS 2023
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework
NeurIPS 2023
Generalization Bounds with Minimal Dependency on Hypothesis Class via Distributionally Robust Optimization
NeurIPS 2022
Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems
AISTATS 2021
Learning Prediction Intervals for Regression: Generalization and Calibration
AISTATS 2021
Constrained Reinforcement Learning via Policy Splitting
ACML 2020
Robust Importance Weighting for Covariate Shift
AISTATS 2020
A Bayesian Framework for Online Classifier Ensemble
ICML 2014
Why Steiner-tree type algorithms work for community detection
AISTATS 2013