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: 392034   Tracked: 357   Attend: 29

Call For Papers
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
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%
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)
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
2022Elucidating the Design Space of Diffusion-Based Generative Models
2022Is Out-of-distribution Detection Learnable?
2022On-Demand Sampling: Learning Optimally from Multiple Distributions
2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
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
2020No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium
2020Language Models are Few-Shot Learners
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
2017Variance-based Regularization with Convex Objectives
2017Safe and Nested Subgame Solving for Imperfect-Information Games
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
2014Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)
2014A* Sampling
2013A memory frontier for complex synapses
2013Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints
2013Scalable Influence Estimation in Continuous-Time Diffusion Networks
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