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

500 papers, page 5 of 10

201. NeurIPS 2024Controlling Continuous Relaxation for Combinatorial Optimization
202. NeurIPS 2024CRAYM: Neural Field Optimization via Camera RAY Matching
203. NeurIPS 2024DASH: Warm-Starting Neural Network Training in Stationary Settings without Loss of Plasticity
204. NeurIPS 2024Decision-Focused Learning with Directional Gradients
205. NeurIPS 2024Deep linear networks for regression are implicitly regularized towards flat minima
206. NeurIPS 2024Derivatives of Stochastic Gradient Descent in parametric optimization
207. NeurIPS 2024Diffeomorphic interpolation for efficient persistence-based topological optimization
208. NeurIPS 2024DISP-LLM: Dimension-Independent Structural Pruning for Large Language Models
209. NeurIPS 2024Dual Risk Minimization: Towards Next-Level Robustness in Fine-tuning Zero-Shot Models
210. NeurIPS 2024Dual-Perspective Activation: Efficient Channel Denoising via Joint Forward-Backward Criterion for Artificial Neural Networks
211. NeurIPS 2024EDT: An Efficient Diffusion Transformer Framework Inspired by Human-like Sketching
212. NeurIPS 2024Efficient Adaptation of Pre-trained Vision Transformer via Householder Transformation
213. NeurIPS 2024Efficient Combinatorial Optimization via Heat Diffusion
214. NeurIPS 2024Efficient Prompt Optimization Through the Lens of Best Arm Identification
215. NeurIPS 2024Efficient Sign-Based Optimization: Accelerating Convergence via Variance Reduction
216. NeurIPS 2024EMR-Merging: Tuning-Free High-Performance Model Merging
217. NeurIPS 2024ESPACE: Dimensionality Reduction of Activations for Model Compression
218. NeurIPS 2024Exact, Tractable Gauss-Newton Optimization in Deep Reversible Architectures Reveal Poor Generalization
219. NeurIPS 2024Explicit Eigenvalue Regularization Improves Sharpness-Aware Minimization
220. NeurIPS 2024Exploring and Exploiting the Asymmetric Valley of Deep Neural Networks
221. NeurIPS 2024Fast Rates in Stochastic Online Convex Optimization by Exploiting the Curvature of Feasible Sets
222. NeurIPS 2024Faster Accelerated First-order Methods for Convex Optimization with Strongly Convex Function Constraints
223. NeurIPS 2024Fetch and Forge: Efficient Dataset Condensation for Object Detection
224. NeurIPS 2024Fine-grained Analysis of In-context Linear Estimation: Data, Architecture, and Beyond
225. NeurIPS 2024First-Order Methods for Linearly Constrained Bilevel Optimization
226. NeurIPS 2024First-Order Minimax Bilevel Optimization
227. NeurIPS 2024Fully Unconstrained Online Learning
228. NeurIPS 2024Functionally Constrained Algorithm Solves Convex Simple Bilevel Problem
229. NeurIPS 2024GACL: Exemplar-Free Generalized Analytic Continual Learning
230. NeurIPS 2024Generalized Fast Exact Conformalization
231. NeurIPS 2024Generic Unsupervised Optimization for a Latent Variable Model With Exponential Family Observables
232. NeurIPS 2024Geometry-aware training of factorized layers in tensor Tucker format
233. NeurIPS 2024Global Convergence in Training Large-Scale Transformers
234. NeurIPS 2024Globally Q-linear Gauss-Newton Method for Overparameterized Non-convex Matrix Sensing
235. NeurIPS 2024GO4Align: Group Optimization for Multi-Task Alignment
236. NeurIPS 2024Gradient Rewiring for Editable Graph Neural Network Training
237. NeurIPS 2024Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution
238. NeurIPS 2024Guiding Neural Collapse: Optimising Towards the Nearest Simplex Equiangular Tight Frame
239. NeurIPS 2024Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models
240. NeurIPS 2024How does Gradient Descent Learn Features --- A Local Analysis for Regularized Two-Layer Neural Networks
241. NeurIPS 2024Implicit Optimization Bias of Next-token Prediction in Linear Models
242. NeurIPS 2024Implicit Regularization of Sharpness-Aware Minimization for Scale-Invariant Problems
243. NeurIPS 2024Improved Regret for Bandit Convex Optimization with Delayed Feedback
244. NeurIPS 2024Improving Deep Learning Optimization through Constrained Parameter Regularization
245. NeurIPS 2024Improving Equivariant Model Training via Constraint Relaxation
246. NeurIPS 2024Improving Generalization and Convergence by Enhancing Implicit Regularization
247. NeurIPS 2024Improving robustness to corruptions with multiplicative weight perturbations
248. NeurIPS 2024Improving Visual Prompt Tuning by Gaussian Neighborhood Minimization for Long-Tailed Visual Recognition
249. NeurIPS 2024In-Context Learning with Representations: Contextual Generalization of Trained Transformers
250. NeurIPS 2024Incorporating Surrogate Gradient Norm to Improve Offline Optimization Techniques

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.