colt-topic-selection
GitHub辅助判断机器学习理论成果是否适合投稿COLT会议，通过定理优先性测试和路由表区分ALT、STOC/FOCS等 venue，并指导选择完整论文或开放问题短文。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colt-topic-selection -g -y
SKILL.md
Frontmatter
{
"name": "colt-topic-selection",
"description": "Use when deciding whether a result is COLT-shaped (Conference on Learning Theory) — a theorem-first learning-theory contribution — versus better routed to ALT, STOC\/FOCS, NeurIPS\/ICML\/AISTATS, JMLR, or a statistics journal, and whether the right vehicle is a full paper or a COLT open-problem piece."
}
COLT Topic Selection
Use this before writing begins. COLT solicits papers on theoretical aspects of machine learning, described in the 2026 CFP (checked 2026-07-08) as a subject at the intersection of computer science, statistics, and applied mathematics, with an explicitly inclusive view that includes theory shedding light on empirical phenomena. The practical bar: the contribution must be a theorem — a rate, a separation, a characterization, a hardness result, or an algorithm whose guarantee is the point.
The three-question fit test
- Is the headline sentence a mathematical statement? "We prove the first $O(\sqrt{T})$ regret bound for X" is COLT-shaped. "We propose a method that empirically improves X" is not, regardless of how much analysis decorates it.
- Would a learning theorist care before seeing experiments? COLT reviewers evaluate the result on the model's motivation and the bound's strength alone.
- Does the proof carry the weight? If the technique is assembly of known parts, the result must be strong enough to stand without technique credit; if the result is modest, the technique must be the contribution — one of the two must be true.
Routing table
| Signal in the project | Best venue reading |
|---|---|
| Regret/sample-complexity/oracle-complexity bound, new or improved rate | Core COLT |
| Matching lower bound via a new instance construction | Core COLT |
| Theory explaining a deep-learning phenomenon, theorem-first | COLT (in-scope by the CFP's inclusive view) or ML-conference theory track |
| Learning theory with a long, self-contained development (60+ pages of ideas, not just proofs) | JMLR or Annals of Statistics — journal-length exposition |
| Algorithmic result where combinatorial/complexity machinery dominates the learning content | STOC / FOCS / SODA |
| Learning theory, but the community fit is the smaller algorithmic-learning-theory circuit | ALT (sister venue, autumn deadline cycle — verify) |
| Theorems plus substantial experiments as co-equal evidence | AISTATS or NeurIPS/ICML |
| Statistical methodology with inference guarantees and applied audience | AISTATS or a statistics journal |
| Probabilistic/Bayesian modeling contribution, uncertainty-first | UAI |
| A precise, motivated question you cannot answer | COLT Open Problem piece (see below) |
The open-problem vehicle
COLT has a tradition of publishing short open-problem pieces in its proceedings — citable, reviewed, and historically influential (verified instance: Agarwal, Krishnamurthy, Langford, Luo & Schapire, "Open Problem: First-Order Regret Bounds for Contextual Bandits," COLT 2017, PMLR v65:4-7; several such problems have been resolved by later full papers). In the 2025 cycle the format was: at most 4 pages excluding references, title beginning "Open Problem:", non-anonymous, submitted via CMT on its own timeline. Whether and how the track runs in your cycle: 待核实 in the current CFP.
Choose the open-problem route when you can state the question with full formality, prove the easy directions, explain why standard techniques fail, and ideally attach a modest prize of honor (tradition, not requirement). It converts a stalled project into community agenda-setting.
Scope self-interrogation
Q1. State the main claim as: quantifier prefix + model + bound/separation.
-> Cannot? The project is not yet a COLT project; it is a research direction.
Q2. Name the nearest prior theorem and your delta type
(gap-closing / log-removal / assumption-weakening / new-model separation).
-> No nameable neighbor? Either the model is unmotivated or the search
is incomplete -- both are pre-writing problems.
Q3. Is every experiment you are planning deletable without weakening the claim?
-> If deleting them guts the paper, route to AISTATS/NeurIPS/ICML instead.
Q4. Will the proof survive a hostile expert with unlimited appendix access?
-> "Probably" means the verification pass comes before the venue decision.
Vignette: three fates for one project
A team analyzes gradient descent on a two-layer network and can prove convergence to a global minimum under an over-parameterization condition.
- As stands — plausible COLT: the headline is a theorem about a practical algorithm, in-scope under the CFP's "theory that sheds light on empirical phenomena." The COLT version leads with the convergence rate, the over-parameterization threshold, and the technique that beats prior NTK-style arguments; the experiment section shrinks to one illustrative training curve.
- Weakened theory, strong benchmarks — misroute: if the honest version needs assumptions no practical network meets and the interesting content is empirical, the NeurIPS/ICML framing (empirical contribution, theory as support) is both more honest and more likely to succeed.
- Question sharpened, proof missing — open problem: if the threshold conjecture resists proof but can be stated exactly, a COLT open-problem piece stating the conjecture, the partial results, and why current techniques fail converts the stall into a citable contribution.
Common misroutes seen at COLT
- The "theory-flavored systems paper": an algorithm with a convergence guarantee under assumptions the target application violates, pitched as theory. Reviewers ask what the theorem teaches; have an answer that is not the benchmark table.
- The "known result in new clothes": a bound classical in sequential analysis or
empirical-process theory, rediscovered. The statistics lane of
colt-related-workexists to catch this before a reviewer does. - The "journal paper in a 12-page costume": a development whose value is the full
landscape, mutilated to fit. If the body cannot carry the spine (see
colt-writing-style), choose JMLR. - The "two-community orphan": too applied for COLT, too theoretical for an applied venue. Usually a framing failure — pick the community whose open question you actually answer and write for it alone.
Timing considerations
- COLT's single annual deadline (February 4, 2026 for the 39th edition) sits between the autumn ML-conference cluster and summer; a NeurIPS reject in September leaves comfortable repair time, an ICML reject usually does not — plan the cascade.
- ALT and COLT deadlines are roughly anti-phased, making ALT the natural same-community fallback; verify the current ALT cycle before promising the team.
Cycle-volatility warnings
- Scope emphasis and the topics list are re-issued every cycle; the intersection framing above is the 2026 wording (待核实 later).
- Open-problem track existence, format, and deadline: current CFP only.
Output format
[Fit] core COLT / plausible COLT / misroute
[Headline claim] <quantifiers + model + bound/separation, one line>
[Delta type] <vs. nearest prior theorem>
[Alternative vehicle] full paper / open-problem piece / ALT / JMLR / AISTATS / STOC-FOCS
[Pre-writing blocker] <verification, motivation, or search gap to close first>
Version History
- 9f86f09 Current 2026-07-19 14:41
