Deep learning · optimization papers, 2023–2026

Key findings

• Deep learning · optimization fell from 256 papers in 2023 to 240 in 2026, so interest is cooling.
• NeurIPS publishes the most Deep learning · optimization work: 500 papers from 2023 to 2026, 52% of the four-venue total.
• The fastest-growing overlap is with Alignment and Safety: papers tagged with both rose from 5 in 2023 to 23 in 2026.
• Among the 11 topics under Deep Learning, it ranks #6 by 2026 paper count; the fastest grower in the group, Diffusion Models, is up 4.1x versus 0.9x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026Achieving Directional-Stationarity from a Single Random Direction Step
• ICML 2026Bottleneck Communication Delay Minimization for Communication-Efficient Decentralized Learning
• ICLR 2026From Sorting Algorithms to Scalable Kernels: Bayesian Optimization in High-Dimensional Permutation Spaces
• CVPR 2026Coverage Optimization for Camera View Selection
• NeurIPS 2025High-Performance Arithmetic Circuit Optimization via Differentiable Architecture Search
• ICML 2025Feature-Mapping Topology Optimization with Neural Heaviside Signed Distance Functions
• ICLR 2025Boosting Neural Combinatorial Optimization for Large-Scale Vehicle Routing Problems
• CVPR 2025HotSpot: Signed Distance Function Optimization with an Asymptotically Sufficient Condition
• NeurIPS 2024$\boldsymbol{\mu}\mathbf{P^2}$: Effective Sharpness Aware Minimization Requires Layerwise Perturbation Scaling
• ICML 2024Barrier Algorithms for Constrained Non-Convex Optimization
• ICLR 2024Bayesian Coreset Optimization for Personalized Federated Learning
• CVPR 2024Multi-Session SLAM with Differentiable Wide-Baseline Pose Optimization
• NeurIPS 2023$\textbf{A}^2\textbf{CiD}^2$: Accelerating Asynchronous Communication in Decentralized Deep Learning
• ICML 2023Buying Information for Stochastic Optimization
• ICLR 2023Alternating Differentiation for Optimization Layers
• CVPR 2023Visual-Language Prompt Tuning With Knowledge-Guided Context Optimization
• NeurIPS 2026Adaptive Conditional Gradient Sliding: Projection-Free and Line-Search-Free Acceleration
• ICML 2026Riemannian Optimization for Fair Spectral Clustering
• ICLR 2026Gradient-Based Diversity Optimization with Differentiable Top-$k$ Objective
• CVPR 2026Its Never Too Late Noise Optimization for Collapse Recovery in

All Deep learning · optimization papers

191 papers, page 2 of 4

51. NeurIPS 2024EMR-Merging: Tuning-Free High-Performance Model Merging
52. NeurIPS 2024ESPACE: Dimensionality Reduction of Activations for Model Compression
53. NeurIPS 2024Exact, Tractable Gauss-Newton Optimization in Deep Reversible Architectures Reveal Poor Generalization
54. NeurIPS 2024Explicit Eigenvalue Regularization Improves Sharpness-Aware Minimization
55. NeurIPS 2024Exploring and Exploiting the Asymmetric Valley of Deep Neural Networks
56. NeurIPS 2024Fast Rates in Stochastic Online Convex Optimization by Exploiting the Curvature of Feasible Sets
57. NeurIPS 2024Faster Accelerated First-order Methods for Convex Optimization with Strongly Convex Function Constraints
58. NeurIPS 2024Fetch and Forge: Efficient Dataset Condensation for Object Detection
59. NeurIPS 2024Fine-grained Analysis of In-context Linear Estimation: Data, Architecture, and Beyond
60. NeurIPS 2024First-Order Methods for Linearly Constrained Bilevel Optimization
61. NeurIPS 2024First-Order Minimax Bilevel Optimization
62. NeurIPS 2024Fully Unconstrained Online Learning
63. NeurIPS 2024Functionally Constrained Algorithm Solves Convex Simple Bilevel Problem
64. NeurIPS 2024GACL: Exemplar-Free Generalized Analytic Continual Learning
65. NeurIPS 2024Generalized Fast Exact Conformalization
66. NeurIPS 2024Generic Unsupervised Optimization for a Latent Variable Model With Exponential Family Observables
67. NeurIPS 2024Geometry-aware training of factorized layers in tensor Tucker format
68. NeurIPS 2024Global Convergence in Training Large-Scale Transformers
69. NeurIPS 2024Globally Q-linear Gauss-Newton Method for Overparameterized Non-convex Matrix Sensing
70. NeurIPS 2024GO4Align: Group Optimization for Multi-Task Alignment
71. NeurIPS 2024Gradient Rewiring for Editable Graph Neural Network Training
72. NeurIPS 2024Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution
73. NeurIPS 2024Guiding Neural Collapse: Optimising Towards the Nearest Simplex Equiangular Tight Frame
74. NeurIPS 2024Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models
75. NeurIPS 2024How does Gradient Descent Learn Features --- A Local Analysis for Regularized Two-Layer Neural Networks
76. NeurIPS 2024Implicit Optimization Bias of Next-token Prediction in Linear Models
77. NeurIPS 2024Implicit Regularization of Sharpness-Aware Minimization for Scale-Invariant Problems
78. NeurIPS 2024Improved Regret for Bandit Convex Optimization with Delayed Feedback
79. NeurIPS 2024Improving Deep Learning Optimization through Constrained Parameter Regularization
80. NeurIPS 2024Improving Equivariant Model Training via Constraint Relaxation
81. NeurIPS 2024Improving Generalization and Convergence by Enhancing Implicit Regularization
82. NeurIPS 2024Improving robustness to corruptions with multiplicative weight perturbations
83. NeurIPS 2024Improving Visual Prompt Tuning by Gaussian Neighborhood Minimization for Long-Tailed Visual Recognition
84. NeurIPS 2024In-Context Learning with Representations: Contextual Generalization of Trained Transformers
85. NeurIPS 2024Incorporating Surrogate Gradient Norm to Improve Offline Optimization Techniques
86. NeurIPS 2024Infinite Limits of Multi-head Transformer Dynamics
87. NeurIPS 2024Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?
88. NeurIPS 2024Jointly Modeling Inter- & Intra-Modality Dependencies for Multi-modal Learning
89. NeurIPS 2024Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization
90. NeurIPS 2024Learn more, but bother less: parameter efficient continual learning
91. NeurIPS 2024Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate
92. NeurIPS 2024Learning Generalized Linear Programming Value Functions
93. NeurIPS 2024Learning to Solve Quadratic Unconstrained Binary Optimization in a Classification Way
94. NeurIPS 2024Learning with Fitzpatrick Losses
95. NeurIPS 2024Learning-Augmented Dynamic Submodular Maximization
96. NeurIPS 2024LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch
97. NeurIPS 2024Linguistic Collapse: Neural Collapse in (Large) Language Models
98. NeurIPS 2024LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning
99. NeurIPS 2024Localized Zeroth-Order Prompt Optimization
100. NeurIPS 2024LoQT: Low-Rank Adapters for Quantized Pretraining

FAQ

How many Deep learning · optimization papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

953 in total: 256 in 2023, 294 in 2024, 163 in 2025, 240 in 2026.

How many Deep learning · optimization papers are at NeurIPS 2026?

NeurIPS 2026 has 103 Deep learning · optimization papers, versus 62 the year before.

How many Deep learning · optimization papers are at ICLR 2026?

ICLR 2026 has 44 Deep learning · optimization papers, versus 51 the year before.

How many Deep learning · optimization papers are at ICML 2026?

ICML 2026 has 86 Deep learning · optimization papers, versus 44 the year before.

How many Deep learning · optimization papers are at CVPR 2026?

CVPR 2026 has 7 Deep learning · optimization papers, versus 6 the year before.

Which conference publishes the most Deep learning · optimization papers?

NeurIPS, with 500 papers from 2023 to 2026.