New representations and approximations for sequential decision making under uncertainty
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
This dissertation research addresses the challenge of scaling up algorithms for sequential decision making under uncertainty. In my dissertation, I developed new approximation strategies for planning and learning in the presence of uncertainty while maintaining useful theoretical properties that allow larger problems to be tackled than is practical with exact methods. In particular, my research tackles three outstanding issues in sequential decision making in uncertain environments: performing stable generalization during off-policy updates, balancing exploration with exploitation, and handling partial observability of the environment. The first key contribution of my thesis is the development of novel dual rep-resentations and algorithms for planning and learning in stochastic environments. This dual view I have developed offers a coherent and comprehensive approach to optimal sequential decision making problems, provides an alternative to standard value function based techniques, and opens new avenues for solving sequential de-cision making problems. In particular, I have shown that dual dynamic program-
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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