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

70 papers, page 1 of 2

1. ICML 2023A Distribution Optimization Framework for Confidence Bounds of Risk Measures
2. ICML 2023A Fully First-Order Method for Stochastic Bilevel Optimization
3. ICML 2023Accelerated Cyclic Coordinate Dual Averaging with Extrapolation for Composite Convex Optimization
4. ICML 2023Accelerated Stochastic Optimization Methods under Quasar-convexity
5. ICML 2023Automatically Auditing Large Language Models via Discrete Optimization
6. ICML 2023Beyond Lipschitz Smoothness: A Tighter Analysis for Nonconvex Optimization
7. ICML 2023Beyond Reward: Offline Preference-guided Policy Optimization
8. ICML 2023Beyond Uniform Lipschitz Condition in Differentially Private Optimization
9. ICML 2023Bilevel Optimization with Coupled Decision-Dependent Distributions
10. ICML 2023Bit Allocation using Optimization
11. ICML 2023Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization
12. ICML 2023Buying Information for Stochastic Optimization
13. ICML 2023Competitive Gradient Optimization
14. ICML 2023Constrained Efficient Global Optimization of Expensive Black-box Functions
15. ICML 2023Continuation Path Learning for Homotopy Optimization
16. ICML 2023Convergence of First-Order Methods for Constrained Nonconvex Optimization with Dependent Data
17. ICML 2023Cyclic Block Coordinate Descent With Variance Reduction for Composite Nonconvex Optimization
18. ICML 2023DADAO: Decoupled Accelerated Decentralized Asynchronous Optimization
19. ICML 2023Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity
20. ICML 2023Differentially Private Optimization on Large Model at Small Cost
21. ICML 2023Differentially Private Stochastic Convex Optimization under a Quantile Loss Function
22. ICML 2023Diffusion Models for Black-Box Optimization
23. ICML 2023Dynamic Constrained Submodular Optimization with Polylogarithmic Update Time
24. ICML 2023Efficient displacement convex optimization with particle gradient descent
25. ICML 2023Escaping saddle points in zeroth-order optimization: the power of two-point estimators
26. ICML 2023Fast as CHITA: Neural Network Pruning with Combinatorial Optimization
27. ICML 2023Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization
28. ICML 2023Faster Rates of Convergence to Stationary Points in Differentially Private Optimization
29. ICML 2023FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization
30. ICML 2023FeDXL: Provable Federated Learning for Deep X-Risk Optimization
31. ICML 2023Generalized Polyak Step Size for First Order Optimization with Momentum
32. ICML 2023Generalized-Smooth Nonconvex Optimization is As Efficient As Smooth Nonconvex Optimization
33. ICML 2023Generative Pretraining for Black-Box Optimization
34. ICML 2023Global Optimization with Parametric Function Approximation
35. ICML 2023Global Selection of Contrastive Batches via Optimization on Sample Permutations
36. ICML 2023Improving Bi-level Optimization Based Methods with Inspiration from Humans' Classroom Study Techniques
37. ICML 2023Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation
38. ICML 2023Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement
39. ICML 2023Linearly Constrained Bilevel Optimization: A Smoothed Implicit Gradient Approach
40. ICML 2023Lottery Tickets in Evolutionary Optimization: On Sparse Backpropagation-Free Trainability
41. ICML 2023Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization
42. ICML 2023Mirror Sinkhorn: Fast Online Optimization on Transport Polytopes
43. ICML 2023Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime
44. ICML 2023Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization
45. ICML 2023Neural Network Accelerated Implicit Filtering: Integrating Neural Network Surrogates With Provably Convergent Derivative Free Optimization Methods
46. ICML 2023Omnipredictors for Constrained Optimization
47. ICML 2023On User-Level Private Convex Optimization
48. ICML 2023Optimal Shrinkage for Distributed Second-Order Optimization
49. ICML 2023Optimal Stochastic Non-smooth Non-convex Optimization through Online-to-Non-convex Conversion
50. ICML 2023Optimization for Amortized Inverse Problems

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.