Protein Optimization Feedback Shift
Summary
protein-optimization-feedback-shift is Vipin’s AI4S project on uncertainty-aware closed-loop protein sequence optimization under feedback distribution shift. Its thesis is that calibration and decision quality can separate once the optimization loop changes its own candidate distribution.
Exact local project-name entry: uncertaintyprotein-ai4s.
Key Facts
- EXTRACTED: The local root is
D:/Research/UncertaintyProtein-AI4S. - EXTRACTED: The executable codebase is
D:/Research/UncertaintyProtein-AI4S/protein_bo_conformal. - EXTRACTED: The project asks when calibration stops tracking decision quality, when weighted conformal acts as recovery under shift, and when proposal/batch policy explains observed UQ effects.
- EXTRACTED: The main panel has 12 tasks: 3 FLIP, 3 FLIP2, and 6 ProteinGym tasks.
- EXTRACTED: The appendix robustness panel has 10 tasks.
- EXTRACTED: Strong supported conclusions include that greedy is a strong baseline, raw UCB is not universally better, improved coverage does not automatically imply improved decisions, and weighted conformal is condition-dependent.
- EXTRACTED: The project is in final paper-preparation mode, with the remaining major task described as
essay/-level LaTeX manuscript assembly and final server-side asset refresh.
Relationships
- Related to analog-agent through closed-loop optimization and simulator/oracle-backed decision loops.
- Related to uncertainty through calibration, decision quality, and uncertainty semantics.
- Adds the AI4S branch of Vipin’s research map.
Open Questions
- Which figures/tables need final server refresh before writing?
- How should weighted conformal be framed so it is not overstated as a universal optimization improvement?
- Which local data/provenance details should remain out of the public wiki?