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

103 papers, page 1 of 3

1. NeurIPS 2026A Doubly Smoothed Decentralized Stochastic Minimax Optimization Algorithm
2. NeurIPS 2026Achieving Better Local Regret Bound for Online Non-Convex Bilevel Optimization
3. NeurIPS 2026Achieving Directional-Stationarity from a Single Random Direction Step
4. NeurIPS 2026Adaptive Conditional Gradient Sliding: Projection-Free and Line-Search-Free Acceleration
5. NeurIPS 2026Adaptive-Margin Masking and Restoration for Balanced Multimodal Learning
6. NeurIPS 2026Adaptively Incorporating Directional Hints into Zeroth-Order Optimization
7. NeurIPS 2026An Educated Guess: Deriving Statistically Aligned Gradient Estimates for Zeroth-Order Optimization
8. NeurIPS 2026BBOWP-Bench: Evaluating LLMs on Black-Box Optimization Word Problems
9. NeurIPS 2026Beyond Bounded Variance: Variance-Reduced Normalized Methods for Nonconvex Optimization under Blum-Gladyshev Noise
10. NeurIPS 2026Capacity Allocation at the Source: Sparse Target Optimization for LLM Knowledge Editing
11. NeurIPS 2026Certified Single-Level Reformulation for Tri-Level Cyber-Physical Grid Security
12. NeurIPS 2026Closed-Form Last Layer Optimization
13. NeurIPS 2026Continuous p-adic Optimization
14. NeurIPS 2026Decoupled Optimization for Teacher-Student Semi-Supervised Learning via a Pioneer Student
15. NeurIPS 2026Differentiable Nonlinear Model Predictive Control
16. NeurIPS 2026DisCoMBO: Steering Expert-in-the-Loop Black Box Optimization via Distributional Conformance
17. NeurIPS 2026Disentangling Optimization Geometry via Hierarchical Polar Adapters for Class-Incremental Learning
18. NeurIPS 2026Distributionally Robust Black Box Optimization-based Bidding Strategy in Auction-based Federated Learning
19. NeurIPS 2026Distributionally Robust Listwise Preference Optimization
20. NeurIPS 2026Don't Waste Population: Post-Anneal Refinement for Combinatorial Optimization
21. NeurIPS 2026EDISCO: Equivariant Discrete Diffusion for Euclidean Combinatorial Optimization
22. NeurIPS 2026EMA-Nesterov: Stabilizing Nesterov's Lookahead for Accelerated Deep Learning Optimization
23. NeurIPS 2026Evolution Fine-Tuning: Learning to Discover Across 371 Optimization Tasks
24. NeurIPS 2026Expected Batch Optimal Transport Plans and Consequences for Flow Matching
25. NeurIPS 2026Federated Distributionally Robust Neural Combinatorial Optimization with Convergence Guarantees
26. NeurIPS 2026Finding Simple Proofs for First-Order Optimization
27. NeurIPS 2026Finite-Resolution Decision Sufficiency for Linear Optimization
28. NeurIPS 2026Flow-Corrected Shape Optimization: Taming Manifold Drift in High-Dimensional 3D Models
29. NeurIPS 2026Follow the Regularized Leader Does Not Converge in Constrained Optimization
30. NeurIPS 2026From Cursed to Competitive: Closing the ZO–FO Gap via Input-to-State Stability
31. NeurIPS 2026Fully First-Order Algorithms for Online Non-Convex Bilevel Optimization
32. NeurIPS 2026GNES: Neural-Guided Evolutionary Program Search for Interpretable Multi-Agent Control
33. NeurIPS 2026Gradient Descent’s Last Iterate is Often (slightly) Suboptimal
34. NeurIPS 2026Gradient Regularized Newton Boosting Trees with Global Convergence
35. NeurIPS 2026Group Distributionally Robust Optimization with Flexible Sample Queries
36. NeurIPS 2026Guaranteed Noisy CP Tensor Recovery via Riemannian Optimization on the Segre Manifold
37. NeurIPS 2026Improving Quantized Zeroth-Order Optimization through Reconstructed Low-Rank Structures
38. NeurIPS 2026Improving the Optimization Landscape of Matrix Completion with $\epsilon$-close Surrogates
39. NeurIPS 2026In-Context Black-Box Optimization with Unreliable Feedback
40. NeurIPS 2026In-Expectation Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise
41. NeurIPS 2026Instance-Dependent Bandit Convex Optimization in One Dimension
42. NeurIPS 2026Interpreting Neural Combinatorial Optimization via Evolving Programmatic Bottlenecks
43. NeurIPS 2026Intrinsic Muon: Spectral Optimization on Riemannian Matrix Manifolds
44. NeurIPS 2026KDASO: Knowledge-Data Dual-Driven Automated Skill Optimization for Liability Adjudication Task
45. NeurIPS 2026KiteNorm: Variance Regularisation for Stable and Scalable Post-LN Transformers
46. NeurIPS 2026LDPCache: Locally Differentially Private Multi-Query Processing with Cache Optimization for Large Language Models
47. NeurIPS 2026Learning Polyhedral Conformal Sets for Robust Optimization
48. NeurIPS 2026Learning Rate Decay Can Exponentially Accelerate SGD for Global Optimization of Nonconvex Functions
49. NeurIPS 2026Learning-Augmented Online Portfolio Selection: Optimal Robustness-Consistency Tradeoffs
50. NeurIPS 2026Literati: Towards Anytime Optimal Shape Generalized Trees via AO*

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.