colt-experiments
De la autor
Use when deciding whether a COLT (Conference on Learning Theory) paper needs numerical content at all — COLT has no experiments requirement — and, when numerics genuinely help, designing small illustrative simulations that visualize a proved bound, a separation, or a phase transition without diluting the theory.
Instalare
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colt-experiments -a claude-code
Se instalează în .claude/skills/colt-experiments/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
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Verificări de siguranță pe SKILL.md
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De ce 88
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- stoc-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when judging whether computation belongs in a STOC (ACM Symposium on Theory of Computing) paper at all — STOC accepts on theorems, with no empirical-evaluation expectation — and when it does, scoping it as certified proof computation, constructive search, or clearly labeled illustration rather than benchmarking.Media★ 1.3K
- itcs-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when deciding what counts as evidence for an ITCS theory claim — proofs as the primary evidence, worked examples and separations that make a model concrete, and the rare, well-scoped illustrative computation or simulation — and how to keep any computational content checkable and subordinate to the mathematics.Media★ 1.3K
- focs-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when deciding whether and how computation appears in a FOCS (IEEE Symposium on Foundations of Computer Science) paper — a venue that accepts on theorems with no evaluation section expected — covering machine-verified case analyses, computer-discovered constructions, and honest illustrative plots.Media★ 1.3K
- corl-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing experiments for a CoRL robot-learning paper — seeds and evaluation-episode counts, task-suite breadth, real-robot versus simulation evidence, sim-to-real gap measurement, baseline fairness across BC/RL/VLA families, generalization splits, and statistics for success-rate claims.Știință★ 1.3K
- colm-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the empirical core of a COLM paper — contamination analysis for evaluation data, fair baselines under matched prompting and compute, pinned model versions and decoding parameters, uncertainty over runs and samples, scaling coverage, and honest reporting of API-model comparisons.Știință★ 1.3K
- popl-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing the empirical component of a POPL paper — deciding whether evidence should be a mechanization, a prototype, case studies, or benchmarks; reporting proof effort and case-study coverage honestly; and keeping performance numbers in a supporting role so the formal claim stays the paper's center of gravity.Știință★ 1.3K
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