Conference Information

NeurIPS 2026: Conference on Neural Information Processing Systems

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Submission Date:
2026-05-04
Notification Date:
2026-09-24
Conference Date:
2026-12-06
Location:
Sydney, Australia
Years:
40
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CCF: A   ICORE: A*   QUALIS: A1   Viewed: 444933   Tracked: 609   Attend: 55
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Conference Partner Index (CP-I)

92.5 / 100
Ranked #44 of 5,697 conferences · Top 1%

#5 of 741 in Artificial Intelligence & Machine Learning

Academic recognition (35%)
100
Submission selectivity (20%)
84
Editions held (20%)
100
Community attention (10%)
88
Public record completeness (15%)
80

Inputs used: Listed as CCF A, QUALIS A1 · Acceptance rate: 25.6% (mean of 5 editions on file) · Editions on record: 40 · Researchers following it here: 609 · Researchers who opened this page in the past 24 months: 76

Missing from the public record: Past editions (+3.0)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 100% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-10-12

Call For Papers

NeurIPS 2026 (Conference on Neural Information Processing Systems) is a CCF A / ICORE A* / QUALIS A1 conference held in Sydney, Australia on 2026-12-06. The paper submission deadline is 2026-05-04. Acceptance notifications are sent on 2026-09-24.

The Fortieth Annual Conference on Neural Information Processing Systems (NeurIPS 2026) is an interdisciplinary conference that brings together researchers in deep learning, generative AI, core machine learning, neuroscience, statistics, optimization, computer vision, natural language processing, life sciences, natural sciences, social sciences, and other adjacent fields. We invite submissions presenting new and original research on topics including but not limited to the following: Computer vision Language and multimodal language models Robotics, embodied systems, and engineering AI/ML for physical sciences AI/ML for health and biotechnology AI/ML for sustainability AI/ML for social sciences AI/ML for creatives Neuroscience and cognitive science Socio-technical aspects of AI Human interaction in AI systems Decision-making, reinforcement learning, and control Generalization and multi-task learning Optimization Probabilistic methods AI and network science Data-centric aspects of AI SysML Infrastructure Theory Deep learning General machine learning: core contributions in supervised and unsupervised methods Machine learning is a rapidly evolving field, and so we welcome interdisciplinary submissions that do not fit neatly into existing categories. We also encourage in-depth analysis of existing methods that provide new insights in terms of their limitations or behavior beyond the scope of the original work.
Last updated by Dou Sun on

Acceptance Ratio

Average acceptance rate: 24.7% over 20 years (2001–2025).

YearSubmittedAcceptedAccepted(%)
202521575529024.5%
202415671404325.8%
202312343322226.1%
202210411267125.7%
20219122234425.7%
20209454190020.1%
20196743142821.2%
20184856101120.8%
2017324067820.9%
2016240356923.7%
2015183840321.9%
2014167841424.7%
2013142036025.4%
2012145237025.5%
2009110526323.8%
200683320424.5%
200582220725.2%
200371719827.6%
200271022131.1%
200165019630.2%

Best Papers

YearBest Papers
2025Why Diffusion Models Don’t Memorize: The Role of Implicit Dynamical Regularization in Training
20251000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities
2025Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free
2025Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)
2024Stochastic Taylor Derivative Estimator: Efficient amortization for arbitrary differential operators
2024Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
2023Privacy Auditing with One (1) Training Run
2023Are Emergent Abilities of Large Language Models a Mirage?
2022Beyond neural scaling laws: beating power law scaling via data pruning
2022An empirical analysis of compute-optimal large language model training
2022Gradient Estimation with Discrete Stein Operators
2022Riemannian Score-Based Generative Modelling
2022High-dimensional limit theorems for SGD: Effective dynamics and critical scaling
2022A Neural Corpus Indexer for Document Retrieval
2022Using natural language and program abstractions to instill human inductive biases in machines
2022ProcTHOR: Large-Scale Embodied AI Using Procedural Generation
2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
2022Is Out-of-distribution Detection Learnable?
2022On-Demand Sampling: Learning Optimally from Multiple Distributions
2022Elucidating the Design Space of Diffusion-Based Generative Models
2021ATOM3D: Tasks on Molecules in Three Dimensions
2021Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research
2021Moser Flow: Divergence-based Generative Modeling on Manifolds
2021Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms
2021MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers
2021Deep Reinforcement Learning at the Edge of the Statistical Precipice
2021On the Expressivity of Markov Reward
2021A Universal Law of Robustness via Isoperimetry
2020Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method
2020Language Models are Few-Shot Learners
2020No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium
2019Distribution-Independent PAC Learning of Halfspaces with Massart Noise
2018Non-delusional Q-learning and Value-iteration
2018Optimal Algorithms for Non-Smooth Distributed Optimization in Networks
2018Neural Ordinary Differential Equations
2018Nearly Tight Sample Complexity Bounds for Learning Mixtures of Gaussians via Sample Compression Schemes
2017Safe and Nested Subgame Solving for Imperfect-Information Games
2017Variance-based Regularization with Convex Objectives
2017A Linear-Time Kernel Goodness-of-Fit Test
2016Value Iteration Networks
2015Competitive Distribution Estimation: Why is Good-Turing Good
2015Fast Convergence of Regularized Learning in Games
2014A* Sampling
2014Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)
2013Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints
2013Scalable Influence Estimation in Continuous-Time Diffusion Networks
2013A memory frontier for complex synapses

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