Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Statistical Methodologies for Decision-Making and Uncertainty Reduction in Machine Learning
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
A research-statement paper summarizing the author's work on data-driven contextual optimization (ETO vs IEO comparisons, bandits with approximate inference) and uncertainty quantification/reduction (neural network UQ, prediction intervals, variance reduction for simulation).
Problem
Translating ML predictions into actionable decisions with risk understanding, including comparing estimate-then-optimize and integrated estimation-optimization, bandit exploration under approximate inference, and quantifying epistemic and aleatoric uncertainty.
Approach
Summarizes a line of work that uses a stochastic-dominance regret framework to compare ETO and IEO, theory for approximate-inference Bayesian bandits, procedural-noise-correcting networks for neural UQ, learned prediction intervals, and doubly robust control variate plus importance sampling for joint uncertainty.
Key results
- Elmachtoub et al. (2023) show that under well-specified models ETO stochastically dominates IEO in regret asymptotically, while IEO can be better under misspecification.
- Huang et al. (2023) show EBUCB achieves O(log T) regret if inference error in two alpha-divergences is below a constant.
- Lam and Zhang (2023) show a doubly robust control variate plus importance sampling framework reduces MSE where existing kernelized Stein methods degenerate under joint aleatoric-epistemic uncertainty.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use ETO instead of IEO when the model is well-specified | Contextual optimization · Well-specified model | regret stochastic dominance | Helped |
| use IEO instead of ETO when the model is mis-specified | Contextual optimization · Mis-specified model | regret | Helped |
| allow approximate inference with error below a constant | Enhanced Bayesian UCB (EBUCB) · Bandits | regret order: O(log T) | Helped |
| use approximate inference | Linear Thompson Sampling and Linear Bayesian UCB · Contextual linear bandits | regret | Helped |
| remove training procedural uncertainty using a procedural-noise-correcting network | Neural networks · Neural-network uncertainty quantification | statistical guarantees | Helped |
| apply control variate or importance sampling alone | Monte Carlo computation with joint aleatoric-epistemic uncertainty · Stochastic simulation | performance | Hurt |
| combine control variate and importance sampling with a doubly robust framework | Monte Carlo computation with joint aleatoric-epistemic uncertainty · Stochastic simulation | mean squared error uncertainty | Helped |
Surprising findings
- Under a well-specified model, ETO outperforms IEO in the strong stochastic dominance sense of regret, contrary to the common belief favoring IEO.
Limitations
- Paper is a short research statement; results are summarized from cited works without details of experiments.
Experimental setup
Builds on: estimate-then-optimize sample average approximation Stein's identity kernelized variance reduction
Closest papers by meaning
- What Data Enables Optimal Decisions? An Exact Characterization for Linear Optimization (NeurIPS 2025 · similarity 0.86)
- Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents (ICML 2025 · similarity 0.84)
- The Bias-Variance Tradeoff in Data-Driven Optimization: A Local Misspecification Perspective (NeurIPS 2025 · similarity 0.84)
- Online Algorithms with Uncertainty-Quantified Predictions (ICML 2024 · similarity 0.84)
- Contextual Optimization Under Model Misspecification: A Tractable and Generalizable Approach (ICML 2025 · similarity 0.84)
- Data Mixture Optimization: A Multi-fidelity Multi-scale Bayesian Framework (NeurIPS 2025 · similarity 0.84)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.