Neurips

You can beat SOTA Time Series Anomaly Detection methods with a 100 year old algorithm [R]

Recent advancements in Time Series Anomaly Detection (TSAD) have generated significant interest within leading AI conferences. However, a critical analysis reveals a surprising finding: established state-of-the-art (SOTA) methods are frequently outperformed by a century-old technique, Statistical Process Control (SPC). Testing benchmark datasets demonstrates SPC's remarkable ability to achieve perfect results in many cases, suggesting current benchmarks may be overly simplistic. This calls for introspection within the TSAD community regarding evaluation metrics and the true measure of progress.

*ACL Findings or TMLR? [D]

Navigating the conference publication landscape presents a strategic challenge. With NeurIPS appearing unlikely given current scores, the decision between Transactions on Machine Learning Research (TMLR) and *ACL Findings* warrants careful consideration. While both venues offer visibility, *ACL Findings* likely presents a higher probability of acceptance. Genuinely curious about industry perspectives: would you prioritize *ACL Findings* or TMLR on your publication record? For deeper insights into related AI discovery research, explore our article on "Autonomous Mathematical Discovery in an Open-World Multi-Agent Environment."

NeurIPS accepted papers leaked? [D]

A significant development has emerged: a GitHub repository containing approximately 7,000 papers, potentially representing the accepted submissions for NeurIPS 26, has surfaced. While some entries are anonymized, the level of detail suggests a high degree of accuracy. The early release raises questions about authenticity, and confirmation from the NeurIPS community is actively being sought. This situation highlights the increasing importance of responsible data handling and access. For further context on AI agent capabilities, explore our recent article, "You Never Told Your Agent What Done Means.

Where to submit stat/prob ML [D]

The dominance of large language models (LLMs) at top machine learning conferences has prompted a critical question: where does the statistical and probabilistic machine learning community find its home? While venues like NeurIPS and ICLR now largely focus on agentic LLM applications, researchers like Arnaud Doucet, Aapo Hyvärinen, and others continue to publish impactful work. AISTATS and UAI appear increasingly viable options, offering a more focused platform for stat/prob ML advancements.

How important is having an internship to get a good job for ML PhD in USA? [D]

Securing a strong industry role after an ML PhD in the USA, particularly for international students, is significantly impacted by internship experience. While not universally mandatory, internships demonstrably elevate candidacy, providing practical application of research and valuable networking opportunities. With many top universities suspending CPT programs, the challenge is real. However, a robust publication record—like the three papers in CVPR, 3DV, and ICRA, plus anticipated ICCV and NeurIPS submissions—remains a powerful asset.

NeurIPS 2026 Acceptance Calculator [P]

Navigating NeurIPS submissions can feel daunting. To help demystify the process, we’ve developed a NeurIPS 2026 Acceptance Calculator [P], a small model estimating acceptance probability based on scores and a projected acceptance rate. Explore it here: https://levilingsch.github.io/neurips-acceptance-estimator/. This tool offers a practical way to assess your submission's potential. For researchers looking to bolster their writing skills alongside their technical contributions, our "Best ML papers to pick up writing skills [D]" article provides valuable guidance.

Archival vs non archival workshop [R]

Understanding NeurIPS workshop archiving is crucial for maximizing the impact of your work, particularly for graduate school applications. A key distinction exists: NeurIPS workshops, like many others, are typically non-archival. Consequently, publication in a proceeding may carry less weight than a peer-reviewed journal. For context, consider how preprints and subsequent publications are handled—a discussion explored in our article, "How to cite/talk about preprint-subsequent works for a camera-ready version?". Prioritize venues that offer robust archival to strengthen your academic record.

AAAI 2027 Reviewer Bidding and Assignment Integrity [D]

Recent concerns regarding reviewer collusion at AAAI 2027, particularly within two-cycle review assignments, highlight a critical challenge in maintaining research integrity. The prevalence of submissions from a single geographic region increases the likelihood of these problematic pairings, potentially enabling unethical behavior.

Epistemic Intelligence in Machine Learning Neurips Workshop page limit? [D]

Submitting to the 3rd Workshop on Epistemic Intelligence in Machine Learning at Neurips requires careful preparation. While the organizers have yet to confirm a specific page limit, historical precedent suggests two likely scenarios: either mirroring the ICML workshop's 6-page limit or aligning with the main Neurips conference’s 9-page constraint. To ensure your paper meets submission guidelines, we recommend erring on the side of brevity. For further exploration of related topics, consider our recent analysis of EMNLP 26 cost considerations.

Looking for 1 teammate — RealPDE Competition (NeurIPS 2026)[D]

Ready to tackle a challenging AI problem? The RealPDE Competition (NeurIPS 2026) invites skilled machine learning practitioners to join a team of up to three and explore innovative solutions for fluid dynamics data – real PIV and CFD – across Sim2Real and LTTTA tracks. This competition offers a unique opportunity to transform your data handling skills. Interested? DM the poster to join. Registration closes August 20th. Learn more and register here: https://realpdecompetition.github.io.

NeurIPS 2026 Author Notifications Close to ICLR Deadline [D]

NeurIPS 2026 author notification deadlines—September 24th—are fast approaching, coinciding closely with the ICLR submission deadline. A common concern arises: are extended Area Chair and reviewer discussion phases typical? Many authors report frustration when rebuttals go unaddressed. Given this timing, researchers are strategically evaluating ICLR submissions as a contingency. As one example, our recent article, "Mathematical Experiments Are Becoming Abundant Through Human-Machine Teaming," explores related challenges in rigorous experimentation. Good luck navigating these crucial deadlines!

For the people who got reviews back from neurips, cvpr, eccv, etc and also tested their paper through an agentic reviewer like the stanford one, how different were the reviews? [D]

For those who recently received reviews from NeurIPS, CVPR, ECCV, or similar conferences, and also utilized agentic reviewer tools like the Stanford model, a compelling question arises: how do the reviews compare? We're exploring the divergence between human and LLM assessments, seeking insights into this evolving landscape. Early indications suggest significant variations, prompting a deeper understanding of how AI-assisted review impacts the peer review process. For further context on related challenges, see our article, "My Model Was Cheating on Its Own Test."

Neurips 2026: Modified date on reviews [D]

TMLR Relevance and Prestige [D]

Acceptance to *TMLR* signifies a notable achievement in machine learning research. While *NeurIPS*, *ICLR*, and *ICML* consistently rank as the highest-tier AI conferences, *TMLR* (Transactions on Machine Learning Research) holds considerable prestige as a respected journal. It’s generally considered on par with *JMLR* (Journal of Machine Learning Research) in terms of rigor and impact. Securing publication in *TMLR* demonstrates a commitment to well-validated, theoretically sound work. For further insights into transparency in algorithmic ranking, explore our article on X’s open-sourcing of its ranking algorithm.

2026 NeurIPS: Where are you going? [D]

Navigating the 2026 NeurIPS landscape presents a key decision for US-based attendees: Sydney or Atlanta? This year's conference offers compelling options, prompting many to consider logistical and professional priorities. We’ve observed considerable discussion around this choice, mirroring broader questions about the future of AI research and collaboration. For those seeking deeper understanding of underlying methodologies, our recent article, "I never understood positional encoding until I read this article," offers valuable insights. Ultimately, planning ahead ensures a productive and enriching NeurIPS experience.

73 NeurIPS workshops, and not a single one on Causality [R]

The absence of causality-focused workshops at NeurIPS 2026, evidenced by the list compiled by Danyal Jafferji, raises a pertinent question: has the field plateaued beyond venues like UAI, AISTATS, and CLeaR? While these remain excellent platforms, the rapid rise of LLMs and agent-based AI appears to have significantly impacted the visibility of several subfields within top-tier conferences. This shift underscores a broader trend in AI research.

NeurIPS AI Assisted Review authors/reviewers? [D]

The NeurIPS AI Assisted Review experience, as shared by authors and reviewers, reveals a complex landscape. Discrepancies in review depth—ranging from detailed feedback to superficial assessments—highlight a need for greater consistency. Concerns around maintaining double-blind conditions and a lack of engagement with author rebuttals also surfaced. A key takeaway: clarity of foundational concepts remains paramount. As explored in "A Mechanistic Explanation of Prompt Injection," understanding underlying principles is vital for effective evaluation, even when leveraging AI assistance.

NeurIPS 2026 Main Track — Theory papers score tracking post Rebuttal [D]

Following the NeurIPS 2026 rebuttal period, a discussion has emerged regarding Theory paper score distributions. Early reports suggest scores may be trending lower across disciplines this year. To facilitate a clearer understanding of the landscape, authors are invited to share their scores (x/x/x), confidence levels (x/x/x), and whether scores shifted post-rebuttal, optionally specifying the broad area of research. One author reported a 4/4/4 score with 3/3/3 confidence.

NeurIPS 2026 Concept & Feasibility Track [D]

Navigating the NeurIPS 2026 Concept & Feasibility (C&F) Track presents unique challenges, particularly regarding reviewer engagement. Initial submissions often receive praise for originality, yet concerns about experimental scope—a permissible outcome per track guidelines—can stall progress. A recent discussion highlights a concerning lack of reviewer response even after rebuttal, raising questions about the track’s visibility and author experiences. Explore insights from fellow researchers and a deeper analysis of post-rebuttal score distributions, as detailed in our "NeurIPS 2026 post-rebuttal score distribution poll."

Completely dead NeurIPS review period from both ends? [D]

A concerning trend has emerged during the NeurIPS review period: unusually prolonged silence from both reviewers and authors. Reports indicate reviewers abandoning the process post-initial reviews, while authors remain unresponsive even without submitting rebuttals. This phenomenon, observed across multiple submissions, raises questions about shifts in academic publishing practices—potentially a strategy of widespread submission with limited follow-through. As noted in a related discussion on score tracking post-rebuttal for theory papers, NeurIPS 2026, maintaining engagement throughout the review cycle remains crucial.

NeurIPS 2026 post-rebuttal score distribution poll [D]

Curious about the NeurIPS 2026 post-rebuttal score distribution? With discussions surrounding potentially lower scores this year, a quick poll aims to gauge the average score breakdown after the rebuttal phase—excluding confidence weights. This is a preliminary look, acknowledging inherent self-selection bias. Share your vote here: [https://loppy.be/poll/yczuv8yo](https://loppy.be/poll/yczuv8yo). For deeper insights into NeurIPS trends, see our related article, "NeurIPS 2026 Main Track — Theory papers score tracking post Rebuttal [D]," for specific analysis.

A question on ICLR and NeurIPS deadlines, and OpenReview [D]

Navigating the complex conference submission landscape can be challenging, particularly with the recent uncertainty surrounding NeurIPS. Many are understandably confused by the sudden silence following initial reviews. Given that the ICLR abstract deadline precedes the NeurIPS results announcement, a critical question arises: can a submission be resubmitted to ICLR without triggering flags on OpenReview? We address this common concern and encourage users to explore the platform’s guidelines for clarity.

Do ACs also give scores? [D]

Navigating NeurIPS submissions can be confusing, especially for first-timers. Many authors wonder if Area Chairs (ACs) provide scores during Phase 2, the author-reviewer discussion. While you've received your meta-review, the absence of direct AC comments is a common query. It’s standard for ACs to remain largely silent during this phase, focusing on guiding the discussion. For more on navigating conference commitments, see our article, "Missed EMNLP commitment deadline, what can be done?". Focus on addressing reviewer concerns and refining your paper.

NeurIPS 2026: Tips that might convince AC? [D]

Navigating NeurIPS acceptance with initially positive reviews, followed by a score decrease despite addressing reviewer concerns, can be frustrating. Authors facing similar scenarios—particularly those with average reviewer scores around 3.5—often find the Area Chair (AC) plays a crucial role in final decisions. Focus your efforts on a compelling meta-review response, clearly articulating how your revisions mitigate identified weaknesses. While AC engagement can vary, proactive communication highlighting your responsiveness is key.