At COLT, related work is quantitative: the reviewer wants to know exactly which bound, in exactly which model, your result improves, matches, generalizes, or separates. Prose adjacency ("much work has studied bandits") is filler; a rates table is evidence. Use this skill to build the comparison and to keep it eligible under the current CFP's overlap rules.
For each of your main results, fill one row per nearest prior result:
| Prior result | Model / assumptions | Their bound | Your bound | Delta type |
|---|---|---|---|---|
| [Cite] Thm 4 | oblivious adversary, K arms | $O(\sqrt{TK\log K})$ | $O(\sqrt{TK})$ | log-factor removal |
| [Cite] Thm 1 | i.i.d., realizable | $O(d/\epsilon)$ | same rate, weaker assumption | assumption weakening |
| [Cite] | same model | lower bound $\Omega(\sqrt{TK})$ | upper matches | closes their gap |
Delta types COLT reviewers recognize as contributions: closing an upper/lower gap, removing a log factor with a new technique, weakening assumptions at the same rate, a new model with a separation from an old one, a simpler proof of a known result (yes — if genuinely simpler, say so plainly), and resolving a posed open problem.
| Lane | Where it lives | What the reviewer checks |
|---|---|---|
| COLT/ALT lineage | PMLR volumes (e.g., v247 = COLT 2024, v291 = COLT 2025), ALT proceedings | Whether you know the direct predecessors and cite the newest ones |
| CS theory | STOC, FOCS, SODA, ITCS | Whether complexity/lower-bound machinery is attributed correctly |
| ML theory tracks | NeurIPS, ICML, AISTATS theory papers | Whether a recent conference paper already claims your rate |
| Statistics and probability | Annals of Statistics, JMLR, Bernoulli, probability literature | Whether your "new" phenomenon is classical under another name |
| Open problems | COLT open-problem pieces (e.g., PMLR v65:4-7) | Whether you cite the problem you are solving — and claim credit for it |
A bibliography missing the statistics lane invites the deadliest COLT review sentence: "this is a known result in the empirical-process literature." Search old names for your objects (regret has cousins in sequential analysis; PAC bounds have cousins in empirical processes) before claiming firsts.
COLT maintains a tradition of published open problems (short, citable pieces in the proceedings — see the exemplars library for a verified instance). If your paper resolves or dents one:
The theory community posts to arXiv aggressively, so collisions are routine:
colt-submission for the anonymity sweep).Cite PMLR-published COLT papers by volume; the canonical form:
@inproceedings{rakhlin11beyond,
title = {Online Learning: Beyond Regret},
author = {Rakhlin, Alexander and Sridharan, Karthik and Tewari, Ambuj},
booktitle = {Proceedings of the 24th Annual Conference on Learning Theory},
series = {Proceedings of Machine Learning Research},
volume = {19},
pages = {559--594},
year = {2011},
publisher = {PMLR}
}
(Entry verified against proceedings.mlr.press/v19/rakhlin11a.html on 2026-07-08.) Cite theorem numbers, not just papers, when you invoke a specific bound — "[23, Thm 3.2]" is house style and lets referees check your usage in seconds.
[Ledger status] complete / rows missing for <results>
[Nearest prior work] <paper, theorem, exact bound>
[Delta type] gap-closing / log-removal / assumption-weakening / separation / simpler proof / open-problem resolution
[Lane coverage] COLT-ALT / CS-theory / ML-tracks / statistics / open-problems
[Overlap risk] none / concurrent arXiv / eligibility issue to declare