colt-topic-selection

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.

1. Would a learning theorist care before seeing experiments? COLT reviewers

evaluate the result on the model's motivation and the bound's strength alone.

1. 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>