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

62 papers, page 1 of 2

1. NeurIPS 2025A Beyond-Worst-Case Analysis of Greedy k-means++
2. NeurIPS 2025A Near-Optimal Algorithm for Decentralized Convex-Concave Finite-Sum Minimax Optimization
3. NeurIPS 2025A Tale of Two Symmetries: Exploring the Loss Landscape of Equivariant Models
4. NeurIPS 2025An Ellipsoid Algorithm for Online Convex Optimization
5. NeurIPS 2025Auto-Connect: Connectivity-Preserving RigFormer with Direct Preference Optimization
6. NeurIPS 2025Beyond Random: Automatic Inner-loop Optimization in Dataset Distillation
7. NeurIPS 2025Beyond Value Functions: Single-Loop Bilevel Optimization under Flatness Conditions
8. NeurIPS 2025Boosting Resilience of Large Language Models through Causality-Driven Robust Optimization
9. NeurIPS 2025Breaking the Frozen Subspace: Importance Sampling for Low-Rank Optimization in LLM Pretraining
10. NeurIPS 2025BundleFlow: Deep Menus for Combinatorial Auctions by Diffusion-Based Optimization
11. NeurIPS 2025Conditional Gradient Methods with Standard LMO for Stochastic Simple Bilevel Optimization
12. NeurIPS 2025Continuous Subspace Optimization for Continual Learning
13. NeurIPS 2025Convergence of Clipped SGD on Convex $(L_0,L_1)$-Smooth Functions
14. NeurIPS 2025Convergence Rates of Constrained Expected Improvement
15. NeurIPS 2025Differentiable extensions with rounding guarantees for combinatorial optimization over permutations
16. NeurIPS 2025Enhancing Training Data Attribution with Representational Optimization
17. NeurIPS 2025Escaping saddle points without Lipschitz smoothness: the power of nonlinear preconditioning
18. NeurIPS 2025Factor Decorrelation Enhanced Data Removal from Deep Predictive Models
19. NeurIPS 2025Fast Projection-Free Approach (without Optimization Oracle) for Optimization over Compact Convex Set
20. NeurIPS 2025Flat Channels to Infinity in Neural Loss Landscapes
21. NeurIPS 2025FSNet: Feasibility-Seeking Neural Network for Constrained Optimization with Guarantees
22. NeurIPS 2025Generalized Gradient Norm Clipping & Non-Euclidean $(L_0,L_1)$-Smoothness
23. NeurIPS 2025HALO: Hadamard-Assisted Lower-Precision Optimization for LLMs
24. NeurIPS 2025High-Performance Arithmetic Circuit Optimization via Differentiable Architecture Search
25. NeurIPS 2025KOALA++: Efficient Kalman-Based Optimization with Gradient-Covariance Products
26. NeurIPS 2025Learn2Mix: Training Neural Networks Using Adaptive Data Integration
27. NeurIPS 2025Learning (Approximately) Equivariant Networks via Constrained Optimization
28. NeurIPS 2025Learning Theory for Kernel Bilevel Optimization
29. NeurIPS 2025Leveraging robust optimization for llm alignment under distribution shifts
30. NeurIPS 2025MetaBox-v2: A Unified Benchmark Platform for Meta-Black-Box Optimization
31. NeurIPS 2025Momentum-SAM: Sharpness Aware Minimization without Computational Overhead
32. NeurIPS 2025Omnipresent Yet Overlooked: Heat Kernels in Combinatorial Bayesian Optimization
33. NeurIPS 2025On the $O(\frac{\sqrt{d}}{K^{1/4}})$ Convergence Rate of AdamW Measured by $\ell_1$ Norm
34. NeurIPS 2025On the Complexity of Finding Stationary Points in Nonconvex Simple Bilevel Optimization
35. NeurIPS 2025On the Optimal Construction of Unbiased Gradient Estimators for Zeroth-Order Optimization
36. NeurIPS 2025Online Optimization for Offline Safe Reinforcement Learning
37. NeurIPS 2025Partial Correlation Network Estimation by Semismooth Newton Methods
38. NeurIPS 2025PDPO: Parametric Density Path Optimization
39. NeurIPS 2025Preference Optimization by Estimating the Ratio of the Data Distribution
40. NeurIPS 2025Probing Neural Combinatorial Optimization Models
41. NeurIPS 2025Problem-Parameter-Free Decentralized Bilevel Optimization
42. NeurIPS 2025Proximalized Preference Optimization for Diverse Feedback Types: A Decomposed Perspective on DPO
43. NeurIPS 2025Purifying Shampoo: Investigating Shampoo's Heuristics by Decomposing its Preconditioner
44. NeurIPS 2025Rethinking Neural Combinatorial Optimization for Vehicle Routing Problems with Different Constraint Tightness Degrees
45. NeurIPS 2025Revisiting Frank-Wolfe for Structured Nonconvex Optimization
46. NeurIPS 2025Robust Hyperbolic Learning with Curvature-Aware Optimization
47. NeurIPS 2025Scalable inference of functional neural connectivity at submillisecond timescales
48. NeurIPS 2025Semi-infinite Nonconvex Constrained Min-Max Optimization
49. NeurIPS 2025Sequential Monte Carlo for Policy Optimization in Continuous POMDPs
50. NeurIPS 2025Set Smoothness Unlocks Clarke Hyper-stationarity in 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.