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

294 papers, page 2 of 6

51. ICML 2024Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient
52. ICML 2024Dynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization
53. ICML 2024Efficient Algorithms for Sum-Of-Minimum Optimization
54. ICML 2024Efficient Stochastic Approximation of Minimax Excess Risk Optimization
55. ICML 2024FADAS: Towards Federated Adaptive Asynchronous Optimization
56. ICML 2024Federated Optimization with Doubly Regularized Drift Correction
57. ICML 2024From Generalization Analysis to Optimization Designs for State Space Models
58. ICML 2024From Inverse Optimization to Feasibility to ERM
59. ICML 2024Fundamental Benefit of Alternating Updates in Minimax Optimization
60. ICML 2024Gradient Compressed Sensing: A Query-Efficient Gradient Estimator for High-Dimensional Zeroth-Order Optimization
61. ICML 2024High-Probability Bound for Non-Smooth Non-Convex Stochastic Optimization with Heavy Tails
62. ICML 2024How Free is Parameter-Free Stochastic Optimization?
63. ICML 2024How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization
64. ICML 2024Implicit Bias of AdamW: $\ell_\infty$-Norm Constrained Optimization
65. ICML 2024Improving SAM Requires Rethinking its Optimization Formulation
66. ICML 2024In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
67. ICML 2024InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models
68. ICML 2024Joint Composite Latent Space Bayesian Optimization
69. ICML 2024KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions
70. ICML 2024LASER: Linear Compression in Wireless Distributed Optimization
71. ICML 2024Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds
72. ICML 2024Locally Differentially Private Decentralized Stochastic Bilevel Optimization with Guaranteed Convergence Accuracy
73. ICML 2024LPGD: A General Framework for Backpropagation through Embedded Optimization Layers
74. ICML 2024No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths
75. ICML 2024Non-convex Stochastic Composite Optimization with Polyak Momentum
76. ICML 2024Non-stationary Online Convex Optimization with Arbitrary Delays
77. ICML 2024On Discrete Prompt Optimization for Diffusion Models
78. ICML 2024On PI Controllers for Updating Lagrange Multipliers in Constrained Optimization
79. ICML 2024On the Complexity of Finite-Sum Smooth Optimization under the Polyak–Łojasiewicz Condition
80. ICML 2024On The Complexity of First-Order Methods in Stochastic Bilevel Optimization
81. ICML 2024Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimization
82. ICML 2024Optimization without Retraction on the Random Generalized Stiefel Manifold
83. ICML 2024Plug-in Performative Optimization
84. ICML 2024Position: Leverage Foundational Models for Black-Box Optimization
85. ICML 2024Position: Optimization in SciML Should Employ the Function Space Geometry
86. ICML 2024Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds
87. ICML 2024Principled Preferential Bayesian Optimization
88. ICML 2024Probability Distribution of Hypervolume Improvement in Bi-objective Bayesian Optimization
89. ICML 2024Projection-Free Online Convex Optimization with Time-Varying Constraints
90. ICML 2024Quantum Algorithm for Online Exp-concave Optimization
91. ICML 2024Robust Data-driven Prescriptiveness Optimization
92. ICML 2024Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space
93. ICML 2024Sample Average Approximation for Conditional Stochastic Optimization with Dependent Data
94. ICML 2024Skill Set Optimization: Reinforcing Language Model Behavior via Transferable Skills
95. ICML 2024Stochastic Optimization with Arbitrary Recurrent Data Sampling
96. ICML 2024Stochastic Weakly Convex Optimization beyond Lipschitz Continuity
97. ICML 2024Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimization
98. ICML 2024Token-level Direct Preference Optimization
99. ICML 2024Tuning-Free Stochastic Optimization
100. ICML 2024Two-timescale Derivative Free Optimization for Performative Prediction with Markovian Data

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.