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Henry Lam

18 papers · 2013–2025 · 7 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (10) 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (8) 🌈 Renaissance Researcher (5) 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (7) πŸƒ Academic Marathon (12) πŸ—ƒοΈ Keyword Collector (79) ⚑ Prolific Year (7) πŸ’Ž Century Club (18) πŸ”₯ Unstoppable (6) ❓ The Questioner

Conferences

AISTATS (7) NIPS (4) ICLR (2) ICML (2) ACML (1) JMLR (1) UAI (1)

Top co-authors

Haofeng Zhang (5) Ziyi Huang (3) Ding Zhao (3) Haoxian Chen (3) Fengpei Li (3) Garud Iyengar (2) Tianyu Wang (2) Amirhossein Meisami (2) Yihang Yao (1) Asohan Amarasingham (1)

Research topics

Statistics (1)

Keywords

uncertainty quantification (3) ambiguity set (2) generalization bound (2) distributionally robust optimization (2) empirical risk minimization (2) bayesian inference (2) importance sampling (2) stochastic gradient descent (1) reinforcement learning (1) stochastic optimization (1) policy optimization (1) ensemble learning (1) approximate inference (1) model misspecification (1) online learning (1) neural tangent kernel (1) high-dimensional statistics (1) community detection (1) signal processing (1) statistical inference (1)

Papers

MallowsPO: Fine-Tune Your LLM with Preference Dispersions ICLR 2025
Dissecting the Impact of Model Misspecification in Data-Driven Optimization AISTATS 2025
Learning from Sparse Offline Datasets via Conservative Density Estimation ICLR 2024
Is Cross-validation the Gold Standard to Estimate Out-of-sample Model Performance? NIPS 2024
Detection of Short-Term Temporal Dependencies in Hawkes Processes with Heterogeneous Background Dynamics UAI 2023
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework NIPS 2023
Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks NIPS 2023
Hedging against Complexity: Distributionally Robust Optimization with Parametric Approximation AISTATS 2023
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables AISTATS 2023
Bootstrap in High Dimension with Low Computation ICML 2023
Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence JMLR 2023
Generalization Bounds with Minimal Dependency on Hypothesis Class via Distributionally Robust Optimization NIPS 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
Robust Importance Weighting for Covariate Shift AISTATS 2020
Constrained Reinforcement Learning via Policy Splitting ACML 2020
A Bayesian Framework for Online Classifier Ensemble ICML 2014
Why Steiner-tree type algorithms work for community detection AISTATS 2013