What is the submission deadline for NeurIPS 2026?

The paper submission deadline for NeurIPS 2026 (Conference on Neural Information Processing Systems) is 2026-05-04.

Where is NeurIPS 2026 held?

NeurIPS 2026 is held in Sydney, Australia.

When does NeurIPS 2026 take place?

NeurIPS 2026 takes place on 2026-12-06.

What is the ranking of NeurIPS?

NeurIPS is ranked CCF A, QUALIS A1. These are categorical rankings maintained by the respective bodies (China Computer Federation, CORE Inc., and Brazilian CAPES).

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

CCF: a QUALIS: a1 Viewed: 395425 Tracked: 357 Attend: 29

Call For Papers

NeurIPS 2026 (Conference on Neural Information Processing Systems) is a CCF 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 in 2026-04-10

Acceptance Ratio

Average acceptance rate: 23.4% over 12 years (2014–2025).

Year	Submitted	Accepted	Accepted(%)
2025	21575	5290	24.5%
2024	15671	4043	25.8%
2023	12343	3222	26.1%
2022	10411	2671	25.7%
2021	9122	2344	25.7%
2020	9454	1900	20.1%
2019	6743	1428	21.2%
2018	4856	1011	20.8%
2017	3240	678	20.9%
2016	2403	569	23.7%
2015	1838	403	21.9%
2014	1678	414	24.7%

Best Papers

Year	Best Papers
2025	Why Diffusion Models Don’t Memorize: The Role of Implicit Dynamical Regularization in Training
2025	1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities
2025	Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free
2025	Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)
2023	Privacy Auditing with One (1) Training Run
2023	Are Emergent Abilities of Large Language Models a Mirage?
2022	Beyond neural scaling laws: beating power law scaling via data pruning
2022	An empirical analysis of compute-optimal large language model training
2022	Gradient Estimation with Discrete Stein Operators
2022	Riemannian Score-Based Generative Modelling
2022	High-dimensional limit theorems for SGD: Effective dynamics and critical scaling
2022	A Neural Corpus Indexer for Document Retrieval
2022	Using natural language and program abstractions to instill human inductive biases in machines
2022	ProcTHOR: Large-Scale Embodied AI Using Procedural Generation
2022	Elucidating the Design Space of Diffusion-Based Generative Models
2022	Is Out-of-distribution Detection Learnable?
2022	On-Demand Sampling: Learning Optimally from Multiple Distributions
2022	Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
2021	ATOM3D: Tasks on Molecules in Three Dimensions
2021	Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research
2021	Moser Flow: Divergence-based Generative Modeling on Manifolds
2021	Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms
2021	MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers
2021	Deep Reinforcement Learning at the Edge of the Statistical Precipice
2021	On the Expressivity of Markov Reward
2021	A Universal Law of Robustness via Isoperimetry
2020	Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method
2020	No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium
2020	Language Models are Few-Shot Learners
2019	Distribution-Independent PAC Learning of Halfspaces with Massart Noise
2018	Non-delusional Q-learning and Value-iteration
2018	Optimal Algorithms for Non-Smooth Distributed Optimization in Networks
2018	Neural Ordinary Differential Equations
2018	Nearly Tight Sample Complexity Bounds for Learning Mixtures of Gaussians via Sample Compression Schemes
2017	Variance-based Regularization with Convex Objectives
2017	Safe and Nested Subgame Solving for Imperfect-Information Games
2017	A Linear-Time Kernel Goodness-of-Fit Test
2016	Value Iteration Networks
2015	Competitive Distribution Estimation: Why is Good-Turing Good
2015	Fast Convergence of Regularized Learning in Games
2014	Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)
2014	A* Sampling
2013	A memory frontier for complex synapses
2013	Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints
2013	Scalable Influence Estimation in Continuous-Time Diffusion Networks

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