Robust decision making and its applications in machine learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Decision making formulated as finding a strategy that maximizes a utility function depends critically on knowing the problem parameters precisely. The obtained strategy can be highly sub-optimal and/or infeasible when parameters are subject to uncertainty, a typical situation in practice. Robust optimization, and more generally robust decision making, addresses this issue by treating uncertain parameters as an arbitrary element of a pre-defined set and solving solutions based on a worst-case analysis. In this thesis we contribute to two closely related fields of robust decision making. First, we address two limitations of robust decision making. Namely, a lack of theoretical justification and conservatism in sequential decision making. Specifically, we provide an axiomatic justification of robust optimization based on the MaxMin Expected Utility framework from decision theory. Furthermore, we propose three less conservative decision criteria for sequential decision making tasks, which include: (1) In uncertain Markov decision processes we propose an alternative formulation of the parameter uncertainty -- the nested-set structured parameter uncertainty -- and find the strategy that achieves maxmin expected utility to mitigate the conservatism of the standard robust Markov decision processes. (2) We investigate uncertain Markov decision processes where each strategy is evaluated comparatively by its gap to the optimum value. Two formulations, namely minimax regret and mean-variance tradeoff of the regret, were proposed and their computational cost studied. (3) We propose a novel Kalman filter design based on trading-off the likely performance and the robustness under parameter uncertainty. Second, we apply robust decision making into machine learning both theoretically and algorithmically. Specifically, on the theoretical front, we show that the concept of robustness is essential to ''successful'' learning
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it