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

240 papers, page 5 of 5

201. NeurIPS 2026Optimization Dynamics Imprint Semantic Specificity in Contrastive Embedding Norms
202. NeurIPS 2026Orthogonal Updates for the Win: Towards Accelerated Adaptive Minimax Optimization
203. NeurIPS 2026PACE: Partial-state Amortized Constraint Editing for Neural Combinatorial Optimization
204. NeurIPS 2026Pairwise AUC Optimization Needs Corrective Power: A Unified View
205. NeurIPS 2026ParetoM$^3$: Learning on the Pareto Set under Preference Guidance via Min-Max-Min Optimization
206. NeurIPS 2026Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs
207. NeurIPS 2026POME: Post Optimization Model Edit via Muon-Style Projection
208. NeurIPS 2026Quantum Speedups for Stochastic Optimization with Heavy-Tailed Noise
209. NeurIPS 2026Randomized Kriging Believer For Parallel Bayesian Optimization With Regret Bounds
210. NeurIPS 2026Regret-Based $(\epsilon,\delta)$-optimal Stopping Criteria for Bayesian Optimization
211. NeurIPS 2026Regularization Paths for Continuous DAG Learning
212. NeurIPS 2026Regularized Large Neighborhood Search
213. NeurIPS 2026Rennala-NSGD: Asynchronous Stochastic Optimization Beyond Euclidean Geometry
214. NeurIPS 2026Rethinking Language Model Scaling under Transferable Hypersphere Optimization
215. NeurIPS 2026Revisiting Decentralized Online Convex Optimization with Compressed Communication
216. NeurIPS 2026Riemannian Bilevel Optimization under the Polyak–Łojasiewicz Condition
217. NeurIPS 2026Riemannian Lyapunov Framework: Optimization as Closed-Loop Control on Riemannian Manifolds
218. NeurIPS 2026Robust Latent Space Bayesian Optimization with Marginalized Kernel
219. NeurIPS 2026Route-Consistent Adaptation for Stable Quantization of Mixture-of-Experts Models with Theoretical Guarantees
220. NeurIPS 2026Sample complexity of stochastic optimization with integer variables
221. NeurIPS 2026SAS: Simple Attention Sparsification via End-to-End Optimization of Context Ranking
222. NeurIPS 2026Select-then-differentiate: Solving Bilevel Optimization with Manifold Lower-level Solution Sets
223. NeurIPS 2026Sharper Guarantees for Misspecified Kernelized Bandit Optimization
224. NeurIPS 2026Sign-Based Optimizers Are Effective Under Heavy-Tailed Noise
225. NeurIPS 2026Skip the Hessian, Keep the Rates: Globalized Semismooth Newton with Lazy Hessian Updates
226. NeurIPS 2026Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks
227. NeurIPS 2026Spectral Annealing: Normalization-Path Exploration for Large-Scale Ising Optimization
228. NeurIPS 2026Stochastic Approximation Approach for Decentralized Optimization on Time Varying Random Networks
229. NeurIPS 2026Stochastic Dynamic Barrier Perturbed Gradient Methods for Nonconvex Simple Bilevel Optimization
230. NeurIPS 2026Stochastic Reconfiguration as Statistical Filtering for Overparameterized Neural Quantum States
231. NeurIPS 2026Stochastic Zeroth-Order Optimization Under Heavy-Tailed Noise
232. NeurIPS 2026Structure vs. Chaos: Asymmetric Entropic Optimization for Enforcing Instruction Hierarchy
233. NeurIPS 2026Taming the Tails: Why Distributionally Robust Optimization Needs New Theory for Imbalanced Regression
234. NeurIPS 2026The Cost of Mismatch: Noise Amplification in Zeroth-Order Reinforcement Learning
235. NeurIPS 2026The Optimization Prior: Instilling depth for shallow networks, detail for coarse networks
236. NeurIPS 2026Towards Hyperparameter Transfer for Differentially Private Optimization
237. NeurIPS 2026Towards Settling the Complexity of Non-Euclidean Parallel Convex Optimization
238. NeurIPS 2026Truncated Riemannian (1+1)-ES for Black-Box Optimization with Intrinsic Dimension Guarantees
239. NeurIPS 2026Why Pass@k Optimization Can Degrade Pass@1: Prompt Interference in LLM Post-Training
240. NeurIPS 2026Your Hypergradient is Skewed: Antithetic Neumann Estimation for 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.