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

144 papers, page 1 of 3

1. NeurIPS 2023“Why Not Looking backward?” A Robust Two-Step Method to Automatically Terminate Bayesian Optimization
2. NeurIPS 2023$\textbf{A}^2\textbf{CiD}^2$: Accelerating Asynchronous Communication in Decentralized Deep Learning
3. NeurIPS 2023A Computationally Efficient Sparsified Online Newton Method
4. NeurIPS 2023A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability
5. NeurIPS 2023A Guide Through the Zoo of Biased SGD
6. NeurIPS 2023A Single-Loop Accelerated Extra-Gradient Difference Algorithm with Improved Complexity Bounds for Constrained Minimax Optimization
7. NeurIPS 2023A Unified Approach for Maximizing Continuous DR-submodular Functions
8. NeurIPS 2023A Unified Discretization Framework for Differential Equation Approach with Lyapunov Arguments for Convex Optimization
9. NeurIPS 2023Accelerated Quasi-Newton Proximal Extragradient: Faster Rate for Smooth Convex Optimization
10. NeurIPS 2023Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance
11. NeurIPS 2023Achieving $\mathcal{O}(\epsilon^{-1.5})$ Complexity in Hessian/Jacobian-free Stochastic Bilevel Optimization
12. NeurIPS 2023Adjustable Robust Reinforcement Learning for Online 3D Bin Packing
13. NeurIPS 2023Advancing Bayesian Optimization via Learning Correlated Latent Space
14. NeurIPS 2023Algorithmic Regularization in Tensor Optimization: Towards a Lifted Approach in Matrix Sensing
15. NeurIPS 2023An Alternating Optimization Method for Bilevel Problems under the Polyak-Łojasiewicz Condition
16. NeurIPS 2023An Exploration-by-Optimization Approach to Best of Both Worlds in Linear Bandits
17. NeurIPS 2023An Optimal Structured Zeroth-order Algorithm for Non-smooth Optimization
18. NeurIPS 2023Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger
19. NeurIPS 2023Bayesian Optimization with Cost-varying Variable Subsets
20. NeurIPS 2023Behavior Alignment via Reward Function Optimization
21. NeurIPS 2023Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence
22. NeurIPS 2023Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time
23. NeurIPS 2023Bi-Level Offline Policy Optimization with Limited Exploration
24. NeurIPS 2023Bicriteria Approximation Algorithms for the Submodular Cover Problem
25. NeurIPS 2023Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start
26. NeurIPS 2023Birder: Communication-Efficient 1-bit Adaptive Optimizer for Practical Distributed DNN Training
27. NeurIPS 2023BiSLS/SPS: Auto-tune Step Sizes for Stable Bi-level Optimization
28. NeurIPS 2023Block Low-Rank Preconditioner with Shared Basis for Stochastic Optimization
29. NeurIPS 2023Boosting with Tempered Exponential Measures
30. NeurIPS 2023BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization
31. NeurIPS 2023Closing the Computational-Statistical Gap in Best Arm Identification for Combinatorial Semi-bandits
32. NeurIPS 2023Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems
33. NeurIPS 2023Connected Superlevel Set in (Deep) Reinforcement Learning and its Application to Minimax Theorems
34. NeurIPS 2023Constrained Policy Optimization with Explicit Behavior Density For Offline Reinforcement Learning
35. NeurIPS 2023Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning
36. NeurIPS 2023Contextual Stochastic Bilevel Optimization
37. NeurIPS 2023Convergence of Adam Under Relaxed Assumptions
38. NeurIPS 2023CoPriv: Network/Protocol Co-Optimization for Communication-Efficient Private Inference
39. NeurIPS 2023Data-driven Optimal Filtering for Linear Systems with Unknown Noise Covariances
40. NeurIPS 2023Debiasing Conditional Stochastic Optimization
41. NeurIPS 2023DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization
42. NeurIPS 2023Delayed Algorithms for Distributed Stochastic Weakly Convex Optimization
43. NeurIPS 2023Distributionally Robust Bayesian Optimization with $\varphi$-divergences
44. NeurIPS 2023Distributionally Robust Linear Quadratic Control
45. NeurIPS 2023Don't be so Monotone: Relaxing Stochastic Line Search in Over-Parameterized Models
46. NeurIPS 2023DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method
47. NeurIPS 2023DP-HyPO: An Adaptive Private Framework for Hyperparameter Optimization
48. NeurIPS 2023DRAUC: An Instance-wise Distributionally Robust AUC Optimization Framework
49. NeurIPS 2023Efficient Activation Function Optimization through Surrogate Modeling
50. NeurIPS 2023Efficient Meta Neural Heuristic for Multi-Objective Combinatorial 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.