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

256 papers, page 1 of 6

1. CVPR 2023Attribute-Preserving Face Dataset Anonymization via Latent Code Optimization
2. CVPR 2023Deep Dive Into Gradients: Better Optimization for 3D Object Detection With Gradient-Corrected IoU Supervision
3. CVPR 2023Elastic Aggregation for Federated Optimization
4. CVPR 2023LiDAR-in-the-Loop Hyperparameter Optimization
5. CVPR 2023Optimization-Inspired Cross-Attention Transformer for Compressive Sensing
6. CVPR 2023Towards Better Decision Forests: Forest Alternating Optimization
7. CVPR 2023Transformer-Based Learned Optimization
8. CVPR 2023Visual-Language Prompt Tuning With Knowledge-Guided Context Optimization
9. ICLR 2023A view of mini-batch SGD via generating functions: conditions of convergence, phase transitions, benefit from negative momenta.
10. ICLR 2023Accelerated Single-Call Methods for Constrained Min-Max Optimization
11. ICLR 2023Alternating Differentiation for Optimization Layers
12. ICLR 2023Approximate Vanishing Ideal Computations at Scale
13. ICLR 2023Asynchronous Distributed Bilevel Optimization
14. ICLR 2023BALTO: fast tensor program optimization with diversity-based active learning
15. ICLR 2023Behavior Proximal Policy Optimization
16. ICLR 2023Behind the Scenes of Gradient Descent: A Trajectory Analysis via Basis Function Decomposition
17. ICLR 2023Betty: An Automatic Differentiation Library for Multilevel Optimization
18. ICLR 2023Breaking Correlation Shift via Conditional Invariant Regularizer
19. ICLR 2023Depth Separation with Multilayer Mean-Field Networks
20. ICLR 2023Discovering Evolution Strategies via Meta-Black-Box Optimization
21. ICLR 2023Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality
22. ICLR 2023Distributionally Robust Recourse Action
23. ICLR 2023Equivariance-aware Architectural Optimization of Neural Networks
24. ICLR 2023Eva: Practical Second-order Optimization with Kronecker-vectorized Approximation
25. ICLR 2023Faster federated optimization under second-order similarity
26. ICLR 2023Fisher-Legendre (FishLeg) optimization of deep neural networks
27. ICLR 2023Globally Optimal Training of Neural Networks with Threshold Activation Functions
28. ICLR 2023Hyperparameter Optimization through Neural Network Partitioning
29. ICLR 2023Improving Object-centric Learning with Query Optimization
30. ICLR 2023Over-parameterized Model Optimization with Polyak-{\L}ojasiewicz Condition
31. ICLR 2023Parallel Deep Neural Networks Have Zero Duality Gap
32. ICLR 2023Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization
33. ICLR 2023Predictor-corrector algorithms for stochastic optimization under gradual distribution shift
34. ICLR 2023Projective Proximal Gradient Descent for Nonconvex Nonsmooth Optimization: Fast Convergence Without Kurdyka-Lojasiewicz (KL) Property
35. ICLR 2023RandProx: Primal-Dual Optimization Algorithms with Randomized Proximal Updates
36. ICLR 2023Robust Scheduling with GFlowNets
37. ICLR 2023SGDA with shuffling: faster convergence for nonconvex-PŁ minimax optimization
38. ICLR 2023Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives
39. ICLR 2023The Symmetric Generalized Eigenvalue Problem as a Nash Equilibrium
40. ICLR 2023Topology-aware Robust Optimization for Out-of-Distribution Generalization
41. ICLR 2023Trainable Weight Averaging: Efficient Training by Optimizing Historical Solutions
42. ICLR 2023Unsupervised Learning for Combinatorial Optimization Needs Meta Learning
43. ICML 2023A Distribution Optimization Framework for Confidence Bounds of Risk Measures
44. ICML 2023A Fully First-Order Method for Stochastic Bilevel Optimization
45. ICML 2023Accelerated Cyclic Coordinate Dual Averaging with Extrapolation for Composite Convex Optimization
46. ICML 2023Accelerated Stochastic Optimization Methods under Quasar-convexity
47. ICML 2023Automatically Auditing Large Language Models via Discrete Optimization
48. ICML 2023Beyond Lipschitz Smoothness: A Tighter Analysis for Nonconvex Optimization
49. ICML 2023Beyond Reward: Offline Preference-guided Policy Optimization
50. ICML 2023Beyond Uniform Lipschitz Condition in Differentially Private 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.