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

953 papers, page 2 of 20

51. ICLR 2026Towards Learned Optimization Free Lunch
52. ICML 2026A Constrained Optimization Perspective of Unrolled Transformers
53. ICML 2026A Fully First-Order Layer for Differentiable Optimization
54. ICML 2026A Geometry-Aware Efficient Algorithm for Compositional Entropic Risk Minimization
55. ICML 2026A Linearly Convergent Proximal Subgradient Algorithm for Sparse Portfolio Optimization with Transaction Cost
56. ICML 2026A Risk Decomposition Framework for Pre-hoc Fine-tuning Prediction
57. ICML 2026Accelerated Dual Method for Distributed Optimization: An Inexact-Gradient View of Local Updates
58. ICML 2026An Embarrassingly Simple Way to Optimize Orthogonal Matrices at Scale
59. ICML 2026ASAP: Exploiting the Satisficing Generalization Edge in Neural Combinatorial Optimization
60. ICML 2026Asymmetric Perturbation in Solving Bilinear Saddle-Point Optimization
61. ICML 2026Black-Box Combinatorial Optimization with Order-Invariant Reinforcement Learning
62. ICML 2026Bottleneck Communication Delay Minimization for Communication-Efficient Decentralized Learning
63. ICML 2026Certificate-Guided Pruning for Stochastic Lipschitz Optimization
64. ICML 2026Complexity of Decentralized Optimization with Mixed Affine Constraints
65. ICML 2026Conformal Prediction for Early Stopping in Mixed Integer Optimization
66. ICML 2026Context Tuning for In-Context Optimization
67. ICML 2026Convergence Analysis of Decentralized Hessian-/Jacobian-Free Algorithm for Nonconvex Stochastic Bilevel Optimization
68. ICML 2026Convergence Rate of the Last Iterate of Stochastic Proximal Algorithms
69. ICML 2026CSPO: Constraint-Sensitive Policy Optimization for Safe Reinforcement Learning
70. ICML 2026DDSVM: A Differentiable Framework for Deep Support Vector Machines with Iterative Geometry-Aware Optimization
71. ICML 2026Decision-focused Sparse Tangent Portfolio Optimization
72. ICML 2026Depth over Fidelity in Fixed-Budget Noisy Evolution Strategies
73. ICML 2026Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement
74. ICML 2026DisjunctiveNet: Neural Symbolic Learning via Differentiable Convexified Optimization Layers
75. ICML 2026DREAM: Dual-Standard Semantic Homogeneity with Dynamic Optimization for Graph Learning with Label Noise
76. ICML 2026Dual Quaternion SE(3) Synchronization with Recovery Guarantees
77. ICML 2026Fast Spectrally Sparse Signal Reconstruction via Jacobi-Preconditioned Gradient Descent
78. ICML 2026Finding Stationary Points by Comparisons
79. ICML 2026Flatland: The Adventures of Gradient Descent with Large Step Sizes
80. ICML 2026From Optimization to Generalization under Heavy-Tailed Data: The Role of Gradient Clipping
81. ICML 2026Goal-Oriented Lower-Tail Calibration of Gaussian Processes for Bayesian Optimization
82. ICML 2026Gradient Flow Sampler-based Distributionally Robust Optimization
83. ICML 2026Gradient Smoothing: Coupling Layer-wise Updates for Improved Optimization
84. ICML 2026GradPower: Powering Gradients for Faster Language Model Pre-Training
85. ICML 2026GXPO: Group Cross-Lingual Relative Policy Optimization for Code Generation
86. ICML 2026HeraSys: Collaborative Serving of Multiple LLM Workflows via Fine-Grained End-to-End Optimization
87. ICML 2026Hom-PGD$^+$: Fast Reparameterized Optimization over Non-convex Ball-Homeomorphic Set
88. ICML 2026Improved Convergence Analysis of Topology Dependence in Decentralized SGD
89. ICML 2026Improved Stochastic Optimization of LogSumExp
90. ICML 2026In-Context Learning as Rate–Distortion Optimization
91. ICML 2026Incremental Learning of Sparse Attention Patterns in Transformers
92. ICML 2026Iterative Robust Satisficing: Minimizing Performance Degradation Under Distribution Shift
93. ICML 2026Learning Credal Ensembles via Distributionally Robust Optimization
94. ICML 2026LK Losses: Direct Acceptance Rate Optimization for Speculative Decoding
95. ICML 2026Local Minima in Quadratic-Penalty Relaxations of Binary Linear Programs
96. ICML 2026Local-Minima-Preserving Polynomial Relaxation of Ising Problems
97. ICML 2026Loss-Aware Distributionally Robust Optimization via Trainable Optimal Transport Ambiguity Sets
98. ICML 2026Lower Complexity Bounds for Nonconvex-Strongly-Convex Bilevel Optimization with First-Order Oracles
99. ICML 2026LS$^{2}$MC-GDA: A Smoothed Algorithm for Federated Stochastic Multi-Level Compositional Minimax Optimization
100. ICML 2026MoCL: Metabolic Optimization for Curvature-Aware Continual Learning

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.