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

72 papers, page 1 of 2

1. ICML 2024A Doubly Recursive Stochastic Compositional Gradient Descent Method for Federated Multi-Level Compositional Optimization
2. ICML 2024A Nearly Optimal Single Loop Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness
3. ICML 2024A New Theoretical Perspective on Data Heterogeneity in Federated Optimization
4. ICML 2024A Universal Transfer Theorem for Convex Optimization Algorithms Using Inexact First-order Oracles
5. ICML 2024Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need
6. ICML 2024Activation-Descent Regularization for Input Optimization of ReLU Networks
7. ICML 2024Advancing Dynamic Sparse Training by Exploring Optimization Opportunities
8. ICML 2024Autonomous Sparse Mean-CVaR Portfolio Optimization
9. ICML 2024Barrier Algorithms for Constrained Non-Convex Optimization
10. ICML 2024Bayesian Optimization of Function Networks with Partial Evaluations
11. ICML 2024Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints
12. ICML 2024Compact Optimality Verification for Optimization Proxies
13. ICML 2024Dealing With Unbounded Gradients in Stochastic Saddle-point Optimization
14. ICML 2024Decentralized Convex Finite-Sum Optimization with Better Dependence on Condition Numbers
15. ICML 2024Delving into the Convergence of Generalized Smooth Minimax Optimization
16. ICML 2024Differentiable Distributionally Robust Optimization Layers
17. ICML 2024Differentiable Mapper for Topological Optimization of Data Representation
18. ICML 2024Distributed Bilevel Optimization with Communication Compression
19. ICML 2024Distribution Alignment Optimization through Neural Collapse for Long-tailed Classification
20. ICML 2024Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient
21. ICML 2024Dynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization
22. ICML 2024Efficient Algorithms for Sum-Of-Minimum Optimization
23. ICML 2024Efficient Stochastic Approximation of Minimax Excess Risk Optimization
24. ICML 2024FADAS: Towards Federated Adaptive Asynchronous Optimization
25. ICML 2024Federated Optimization with Doubly Regularized Drift Correction
26. ICML 2024From Generalization Analysis to Optimization Designs for State Space Models
27. ICML 2024From Inverse Optimization to Feasibility to ERM
28. ICML 2024Fundamental Benefit of Alternating Updates in Minimax Optimization
29. ICML 2024Gradient Compressed Sensing: A Query-Efficient Gradient Estimator for High-Dimensional Zeroth-Order Optimization
30. ICML 2024High-Probability Bound for Non-Smooth Non-Convex Stochastic Optimization with Heavy Tails
31. ICML 2024How Free is Parameter-Free Stochastic Optimization?
32. ICML 2024How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization
33. ICML 2024Implicit Bias of AdamW: $\ell_\infty$-Norm Constrained Optimization
34. ICML 2024Improving SAM Requires Rethinking its Optimization Formulation
35. ICML 2024In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
36. ICML 2024InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models
37. ICML 2024Joint Composite Latent Space Bayesian Optimization
38. ICML 2024KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions
39. ICML 2024LASER: Linear Compression in Wireless Distributed Optimization
40. ICML 2024Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds
41. ICML 2024Locally Differentially Private Decentralized Stochastic Bilevel Optimization with Guaranteed Convergence Accuracy
42. ICML 2024LPGD: A General Framework for Backpropagation through Embedded Optimization Layers
43. ICML 2024No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths
44. ICML 2024Non-convex Stochastic Composite Optimization with Polyak Momentum
45. ICML 2024Non-stationary Online Convex Optimization with Arbitrary Delays
46. ICML 2024On Discrete Prompt Optimization for Diffusion Models
47. ICML 2024On PI Controllers for Updating Lagrange Multipliers in Constrained Optimization
48. ICML 2024On the Complexity of Finite-Sum Smooth Optimization under the Polyak–Łojasiewicz Condition
49. ICML 2024On The Complexity of First-Order Methods in Stochastic Bilevel Optimization
50. ICML 2024Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimization

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.