Venue Targeting

Venue Targeting

Choosing the wrong venue is one of the most expensive mistakes in academic publishing — a misfit paper gets desk-rejected or triaged by reviewers who don't understand the contribution.

The goal is not to aim for the most prestigious venue. The goal is to find the venue where your contribution is most legible to the reviewers who will read it.

---

Workflow

1. Characterize the contribution

Before matching to venues, classify the paper across these dimensions:

Contribution type

• New method (architecture, algorithm, loss, training procedure)
• New analysis or theory (bounds, complexity, expressiveness, generalization)
• New dataset or benchmark
• Empirical study or survey (what works, what doesn't, at what scale)
• Application or system (deployed system, real-world impact)
• Negative result or replication study

Domain

• ML theory / optimization
• Deep learning (supervised, self-supervised, generative)
• NLP / language models
• Computer vision
• Reinforcement learning
• Graph learning
• Multimodal / audio / speech
• Robotics / embodied AI
• Data mining / knowledge discovery
• AI systems / efficiency / inference

Evaluation style

• Synthetic + real benchmarks with ablations
• Single large-scale benchmark (leaderboard)
• Theoretical proof + small empirical illustration
• Large-scale human evaluation
• Deployment metrics (latency, cost, real users)

Paper length and scope

• Full paper (8–12 pages)
• Short / findings paper (4 pages)
• Workshop paper (4–6 pages)
• Journal (no page limit)

---

2. Match to venue families

Use this as a rough guide. Venue norms shift — always check the current CFP and recent accepted papers before submitting.

Top-tier ML conferences (theory + methods + empirical)

NLP / language

Computer vision

AI / data mining / applied

Journals (when to use)

• JMLR: Long-form theoretical or empirical ML work; no page limit; slower but respected.
• TMLR: Fast-turnaround ML journal; accepts solid contributions rejected from conferences; no venue prestige bias.
• TACL: NLP journal; fast-turnaround; proceedings published at ACL/EMNLP.
• AIJ: AI research; broad scope; slower.

Use journals when: the paper is too long for a conference, it's a negative result or replication study, it needs more pages to do the contribution justice, or it was rejected multiple times and the community hasn't found a home for it.

---

3. Assess fit and tier realism

For the top 2–3 candidate venues, assess:

Contribution fit

• Do the top papers at this venue look like this contribution?
• Would reviewers in this community understand and value the core claim?
• Is the evaluation protocol what this community expects?

Tier realism

• What is the acceptance rate? (NeurIPS ~25%, ICLR ~30%, ICML ~27%, ACL ~25%, CVPR ~25%)
• What is the weakest accepted paper at this venue in recent years?
• What is the strongest objection a reviewer at this venue will have, and can the paper answer it?

Deadline fit

• What is the next submission deadline?
• How much revision is needed before the paper is ready?
• Is there a workshop option for an earlier feedback cycle?

---

4. Workshop vs. main track

Workshops are appropriate when:

• The paper has a core idea but insufficient experiments for a main paper.
• The contribution is interdisciplinary and may get confused at a single-domain venue.
• The paper targets a niche community that has a dedicated workshop.
• You want feedback before polishing for a main submission.

Workshops are not appropriate when:

• The paper is already main-track quality — workshop acceptance adds nothing.
• The paper will be rejected from the workshop too if the idea isn't solid.

Workshop proceedings are generally non-archival; you can still submit to a main venue after.

---

5. Resubmission strategy after rejection

After a rejection, before resubmitting:

1. Read the reviews. Identify the strongest objection — not the harshest, the most substantive. 2. Decide: fix or reframe. Some papers need more experiments; others need a different framing for a different community. 3. Consider venue change. If three reviewers at NeurIPS thought the paper "lacks theoretical depth," consider ICML with a stronger theory framing, or JMLR for a long-form treatment. 4. Never bare-resubmit. A paper that goes in unchanged will fail for the same reasons.

Common resubmission paths:

• Rejected from NeurIPS → revise → ICLR (same cycle if timing allows)
• Rejected from ICML → add experiments → NeurIPS
• Rejected from ACL → revise framing → EMNLP
• Rejected from main track → shorten → Findings
• Rejected from Findings → journal (TMLR or TACL for NLP)

---

Output format

# Venue Targeting: [Paper title / one-line description]

## Contribution profile
- Type: [method / theory / dataset / empirical study / system / negative result]
- Domain: [field]
- Evaluation: [benchmark types]
- Length: [full paper / short / journal-length]

## Recommended venues (ranked)

### 1. [Venue]
- Fit: [why this community will understand and value the contribution]
- Risks: [what reviewers will push back on]
- Next deadline: [approximate]
- Tier realism: [honest assessment]

### 2. [Venue]
...

### 3. [Venue]
...

## Workshop options (if applicable)
- [Workshop] at [Venue]: [why]

## If rejected from #1
[Concrete resubmission path and what to fix]

## Open questions
[What the user needs to clarify before a final venue decision]

---

Quality bar

A good venue recommendation should prevent a mismatch that wastes 3–6 months of revision cycles. It should be honest about tier realism, specific about reviewer expectations, and give a concrete plan for what happens if the first choice rejects.