colt-topic-selection

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

1. 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.
2. Would a learning theorist care before seeing experiments? COLT reviewers evaluate the result on the model's motivation and the bound's strength alone.
3. 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-work exists 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>