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

44 papers, page 1 of 1

1. ICML 2025A Bregman Proximal Viewpoint on Neural Operators
2. ICML 2025A Memory Efficient Randomized Subspace Optimization Method for Training Large Language Models
3. ICML 2025A Near-Optimal Single-Loop Stochastic Algorithm for Convex Finite-Sum Coupled Compositional Optimization
4. ICML 2025Accelerating PDE-Constrained Optimization by the Derivative of Neural Operators
5. ICML 2025Attention-Only Transformers via Unrolled Subspace Denoising
6. ICML 2025BackSlash: Rate Constrained Optimized Training of Large Language Models
7. ICML 2025Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation
8. ICML 2025BSO: Binary Spiking Online Optimization Algorithm
9. ICML 2025Decoupled SGDA for Games with Intermittent Strategy Communication
10. ICML 2025DTZO: Distributed Trilevel Zeroth Order Learning with Provable Non-Asymptotic Convergence
11. ICML 2025Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method
12. ICML 2025Enabling Optimal Decisions in Rehearsal Learning under CARE Condition
13. ICML 2025Enhancing Parallelism in Decentralized Stochastic Convex Optimization
14. ICML 2025Ensemble Learned Bloom Filters: Two Oracles are Better than One
15. ICML 2025Fast, Accurate Manifold Denoising by Tunneling Riemannian Optimization
16. ICML 2025Feature-Mapping Topology Optimization with Neural Heaviside Signed Distance Functions
17. ICML 2025FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch
18. ICML 2025Generalized additive models via direct optimization of regularized decision stump forests
19. ICML 2025Generalized Smooth Bilevel Optimization with Nonconvex Lower-Level
20. ICML 2025Global curvature for second-order optimization of neural networks
21. ICML 2025Improved Last-Iterate Convergence of Shuffling Gradient Methods for Nonsmooth Convex Optimization
22. ICML 2025Incremental Gradient Descent with Small Epoch Counts is Surprisingly Slow on Ill-Conditioned Problems
23. ICML 2025Inverse Optimization via Learning Feasible Regions
24. ICML 2025Joker: Joint Optimization Framework for Lightweight Kernel Machines
25. ICML 2025Learning Minimum-Size BDDs: Towards Efficient Exact Algorithms
26. ICML 2025Meta-Black-Box-Optimization through Offline Q-function Learning
27. ICML 2025Natural Perturbations for Black-box Training of Neural Networks by Zeroth-Order Optimization
28. ICML 2025Neural Solver Selection for Combinatorial Optimization
29. ICML 2025Nonlinearly Preconditioned Gradient Methods under Generalized Smoothness
30. ICML 2025Optimal Transport Barycenter via Nonconvex-Concave Minimax Optimization
31. ICML 2025Optimization over Sparse Support-Preserving Sets: Two-Step Projection with Global Optimality Guarantees
32. ICML 2025PipeOffload: Improving Scalability of Pipeline Parallelism with Memory Optimization
33. ICML 2025Probabilistic Group Mask Guided Discrete Optimization for Incremental Learning
34. ICML 2025Provable Benefit of Random Permutations over Uniform Sampling in Stochastic Coordinate Descent
35. ICML 2025Radio: Rate–Distortion Optimization for Large Language Model Compression
36. ICML 2025Revisiting Convergence: Shuffling Complexity Beyond Lipschitz Smoothness
37. ICML 2025Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up
38. ICML 2025Scalable Meta-Learning via Mixed-Mode Differentiation
39. ICML 2025SERENA: A Unified Stochastic Recursive Variance Reduced Gradient Framework for Riemannian Non-Convex Optimization
40. ICML 2025Stacey: Promoting Stochastic Steepest Descent via Accelerated $\ell_p$-Smooth Nonconvex Optimization
41. ICML 2025Stochastic Smoothed Primal-Dual Algorithms for Nonconvex Optimization with Linear Inequality Constraints
42. ICML 2025Subspace Optimization for Large Language Models with Convergence Guarantees
43. ICML 2025The Sharpness Disparity Principle in Transformers for Accelerating Language Model Pre-Training
44. ICML 2025TSP: A Two-Sided Smoothed Primal-Dual Method for Nonconvex 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.