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SkillsMP는 brycewang-stanford/Awesome-Journal-Skills에서 3,969개의 skill을 수집했습니다. skill을 열어 소스와 세부 정보를 확인하세요.
brycewang-stanford/Awesome-Journal-Skills수집된 skill 3,969개 중 40개를 표시합니다.
Use when deciding what evidence package a COLT (Conference on Learning Theory) paper needs, given that COLT runs no artifact-evaluation track or badges — the proof appendix is the artifact. Covers proof-verification passes, optional code companions for…
원문 언어: 영어
Use when drafting a COLT (Conference on Learning Theory) rebuttal after initial reviews, handling proof-correctness objections, tightness and novelty-of-technique challenges, the area-chair identity-reveal rule, and concession strategy for theorem-first…
원문 언어: 영어
Use when converting an accepted COLT (Conference on Learning Theory) paper into its PMLR proceedings version, covering de-anonymization, acknowledgements and funding restoration, appendix consolidation into the archival PDF, PMLR volume metadata and BibTeX…
원문 언어: 영어
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…
원문 언어: 영어
Use when positioning a COLT (Conference on Learning Theory) submission against prior bounds and models — building the known-versus-new comparison across COLT/ALT lineage, STOC/FOCS, NeurIPS-and-ICML theory tracks, statistics journals, and arXiv concurrency,…
원문 언어: 영어
Use when strengthening the reproducibility of a COLT (Conference on Learning Theory) paper, where reproducing means re-deriving — complete proofs, explicit assumptions, tracked constants, correctly invoked external results, self-contained notation — plus…
원문 언어: 영어
Use when reasoning about COLT (Conference on Learning Theory) peer review — the double-anonymous-with-informed-area-chair model, correctness-first evaluation by expert theorists, the rebuttal stage before decisions, single-track acceptance stakes, and how…
원문 언어: 영어
Use when auditing a COLT (Conference on Learning Theory) submission for CMT readiness, the 12-page PMLR-format main body, single-PDF assembly with unlimited appendix, double-anonymous formatting, the parallel-submission ban covering journals and proceedings…
원문 언어: 영어
Use when structuring the appendix of a COLT (Conference on Learning Theory) submission — the unlimited-length proof appendix inside the single PDF — covering body/appendix splitting for a 12-page limit, theorem restatement discipline, lemma ordering, notation…
원문 언어: 영어
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…
원문 언어: 영어
Use when planning a COLT (Conference on Learning Theory) submission calendar backward from the single AoE paper deadline — proof-completion and verification milestones, writing and appendix-assembly passes, CMT logistics, the rebuttal window, decision…
원문 언어: 영어
Use when revising a COLT (Conference on Learning Theory) paper for theorem-first exposition — formal setup before results, informal-then-formal theorem statements, proof overviews that sell the technique, quantifier and constant hygiene, and fitting the…
원문 언어: 영어
Use when packaging code, datasets, configs, and deployment evidence for a KDD paper, where the repository cited in the submission is the only artifact reviewers can reach because rebuttals ban links. Covers anonymized repo construction, scale-claim harnesses,…
원문 언어: 영어
Use when drafting KDD rebuttals on OpenReview, where authors answer each review under a no-hyperlink rule, cannot upload revisions, and write for the area chair who recommends Accept, Reject, or Resubmit. Covers evidence-anchoring without links, per-reviewer…
원문 언어: 영어
Use when preparing an accepted KDD paper for ACM proceedings, covering the 12-page proceedings budget with 9 content pages, the extra content page over the submission, the 3-page cap on references plus appendix, ACM e-rights and TAPS source processing, ACM…
원문 언어: 영어
Use when designing or auditing the empirical section of a KDD paper, where evidence combines quality deltas with scalability and efficiency measurements, temporal-leakage-safe splits, mechanism-isolating ablations, tuning-symmetric baselines, and, for the ADS…
원문 언어: 영어
Use when positioning a KDD submission against the data-mining lineage (prior KDD volumes, ICDM, SDM, WSDM, CIKM, WWW) and the ML flagships, handling venue misattribution traps, cross-cycle resubmission overlap, concurrent arXiv work, and the…
원문 언어: 영어
Use when hardening the reproducibility story of a KDD paper, where reproducibility is an explicit decision factor for area chairs. Covers claim-to-evidence tiers for mining pipelines, seeds and splits on large graphs and streams, baseline-tuning disclosure,…
원문 언어: 영어
Use when reasoning about how KDD papers get judged in its dual-cycle OpenReview process: per-review rebuttal, area-chair recommendations weighed on merit through reproducibility and ethics, PC-chair decisions, the Resubmit outcome feeding the next cycle, the…
원문 언어: 영어
Use when auditing a KDD submission for readiness in either cycle, covering Research vs ADS track declaration, OpenReview profile completeness for all authors, the ACM sigconf 8-content-page budget, double-blind checks, in-paper artifact references,…
원문 언어: 영어
Use when splitting a KDD paper across its 8 content pages, the optional appendix after the references, and the cited repository, under the camera-ready rule capping references plus appendix at 3 of 12 pages. Covers appendix triage, reviewer-discretion…
원문 언어: 영어
Use when deciding whether a project belongs at KDD and in which track — Research vs Applied Data Science vs Datasets and Benchmarks vs AI for Sciences — or whether it routes to ICDM, SDM, WSDM, CIKM, WWW, VLDB, or an ML flagship. Covers the…
원문 언어: 영어
Use when planning a KDD project calendar across the venue's two submission cycles per year, including cycle choice, abstract-then-paper deadline pairs a week apart, rebuttal capacity planning, the Resubmit-to-next-cycle loop, ADS evidence lead times, and ACM…
원문 언어: 영어
Use when revising a KDD paper into the venue's register, where the first page names a data regime, a mechanism, and scale evidence, efficiency adjectives trace to design decisions, and two-column sigconf pages punish sprawl. Covers Research vs ADS voice,…
원문 언어: 영어
Use when packaging an accepted MLSys paper's code, configs, and measurement scripts for the venue's post-acceptance artifact evaluation, targeting the Availability, Functional, and Reproducible badges, writing the Artifact Appendix, handling hardware that AE…
원문 언어: 영어
Use when drafting MLSys author responses on OpenReview under the venue's compressed rebuttal window, prioritizing systems-reviewer objections about workload representativeness, baseline tuning, and missing measurements, deciding whether to run new experiments…
원문 언어: 영어
Use when preparing an accepted MLSys paper for publication on proceedings.mlsys.org, covering de-anonymization of the research-track PDF, restoring company and repository identity, reconciling promised rebuttal edits, the reserved-ticket registration window…
원문 언어: 영어
Use when designing or auditing the evaluation of an MLSys paper, selecting representative workloads and hardware, tuning baselines symmetrically, reporting throughput, latency tails, memory, cost, and quality together, structuring ablations that attribute…
원문 언어: 영어
Use when positioning an MLSys submission against the fast-moving ML-systems literature scattered across OSDI, SOSP, NSDI, ASPLOS, and ML venues, handling arXiv-first and open-source-first prior work, comparing against production systems that have no paper,…
원문 언어: 영어
Use when hardening the reproducibility of MLSys performance claims, pinning the full system layer from driver to interconnect, separating ML randomness from systems noise, choosing repetition counts and variance reporting for throughput and latency numbers,…
원문 언어: 영어
Use when reasoning about how MLSys peer review works, covering the OpenReview workflow, the mixed ML-and-systems reviewer pool and how each half scores differently, the compressed response window, industrial-track review expectations, decision dynamics, and…
원문 언어: 영어
Use when auditing an MLSys submission for OpenReview readiness, covering the two-column 10-page body excluding references, the separate appendix upload, double-blind rules that still permit arXiv posting, research-versus-industrial track requirements,…
원문 언어: 영어
Use when deciding what goes into an MLSys appendix versus the 10-page body, exploiting the venue's unlimited separately-uploaded appendix that reviewers are not required to read, organizing configs, traces, extended results, and anonymized code pointers, and…
원문 언어: 영어
Use when deciding whether a project belongs at MLSys rather than OSDI, SOSP, NSDI, ASPLOS, ATC, EuroSys, or the ML conferences, testing for genuine ML-systems co-design, choosing between the research and industrial tracks, and sharpening the systems-for-ML or…
원문 언어: 영어
Use when planning an MLSys submission cycle end to end, from venue-fit and track choice through evaluation freeze, the October deadline, the four-day January response, notification, camera-ready, the March artifact-evaluation submission, and the May…
원문 언어: 영어
Use when revising an MLSys paper's prose and structure, building the measured-bottleneck opening, naming the mechanism instead of listing optimizations, writing evaluation sections as answers to research questions, quantifying every performance claim with…
원문 언어: 영어
Use when packaging code, data, samplers, solvers, and logs for a UAI submission's 50 MB supplementary ZIP or a public post-acceptance release, making probabilistic-inference claims independently runnable while keeping every file double-blind, given that UAI…
원문 언어: 영어
Use when drafting UAI author responses during the OpenReview rebuttal window, turning reviewer objections about technical correctness, novelty, evidential backing, or clarity into anonymous, evidence-anchored replies that survive the reviewer-and-area-chair…
원문 언어: 영어
Use when converting an accepted UAI paper into its PMLR proceedings version, covering de-anonymization, final formatting against the UAI template, metadata for the PMLR volume, restoring public code and data links, poster and spotlight preparation for the…
원문 언어: 영어
Use when designing or auditing UAI experiments where inference quality is the endpoint, covering calibration and coverage measurement, posterior diagnostics, structure-recovery metrics for causal and graphical models, seeded repeated runs, baselines from both…
원문 언어: 영어