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 4 of 10

151. NeurIPS 2025Semi-infinite Nonconvex Constrained Min-Max Optimization
152. NeurIPS 2025Sequential Monte Carlo for Policy Optimization in Continuous POMDPs
153. NeurIPS 2025Set Smoothness Unlocks Clarke Hyper-stationarity in Bilevel Optimization
154. NeurIPS 2025Smart Surrogate Losses for Contextual Stochastic Linear Optimization with Robust Constraints
155. NeurIPS 2025Solver-Free Decision-Focused Learning for Linear Optimization Problems
156. NeurIPS 2025Stab-SGD: Noise-Adaptivity in Smooth Optimization with Stability Ratios
157. NeurIPS 2025Stochastic Momentum Methods for Non-smooth Non-Convex Finite-Sum Coupled Compositional Optimization
158. NeurIPS 2025System Prompt Optimization with Meta-Learning
159. NeurIPS 2025TANDEM: Bi-Level Data Mixture Optimization with Twin Networks
160. NeurIPS 2025Targeted Maximum Likelihood Learning: An Optimization Perspective
161. NeurIPS 2025The Bias-Variance Tradeoff in Data-Driven Optimization: A Local Misspecification Perspective
162. NeurIPS 2025Theoretical Investigation of Adafactor for Non-Convex Smooth Optimization
163. NeurIPS 2025TokMan:Tokenize Manhattan Mask Optimization for Inverse Lithography
164. NeurIPS 2025Uniform Wrappers: Bridging Concave to Quadratizable Functions in Online Optimization
165. NeurIPS 2025What Data Enables Optimal Decisions? An Exact Characterization for Linear Optimization
166. NeurIPS 2024$\boldsymbol{\mu}\mathbf{P^2}$: Effective Sharpness Aware Minimization Requires Layerwise Perturbation Scaling
167. NeurIPS 2024$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise
168. NeurIPS 20242DQuant: Low-bit Post-Training Quantization for Image Super-Resolution
169. NeurIPS 20244-bit Shampoo for Memory-Efficient Network Training
170. NeurIPS 2024A Bayesian Approach to Data Point Selection
171. NeurIPS 2024A Framework for Bilevel Optimization on Riemannian Manifolds
172. NeurIPS 2024A Layer-Wise Natural Gradient Optimizer for Training Deep Neural Networks
173. NeurIPS 2024A PID Controller Approach for Adaptive Probability-dependent Gradient Decay in Model Calibration
174. NeurIPS 2024A Primal-Dual-Assisted Penalty Approach to Bilevel Optimization with Coupled Constraints
175. NeurIPS 2024A Single-Step, Sharpness-Aware Minimization is All You Need to Achieve Efficient and Accurate Sparse Training
176. NeurIPS 2024Accelerating Matroid Optimization through Fast Imprecise Oracles
177. NeurIPS 2024Achieving Linear Convergence with Parameter-Free Algorithms in Decentralized Optimization
178. NeurIPS 2024Activation Map Compression through Tensor Decomposition for Deep Learning
179. NeurIPS 2024Adam with model exponential moving average is effective for nonconvex optimization
180. NeurIPS 2024ADOPT: Modified Adam Can Converge with Any $\beta_2$ with the Optimal Rate
181. NeurIPS 2024Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimization
182. NeurIPS 2024AlterMOMA: Fusion Redundancy Pruning for Camera-LiDAR Fusion Models with Alternative Modality Masking
183. NeurIPS 2024An Accelerated Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness
184. NeurIPS 2024An Accelerated Gradient Method for Convex Smooth Simple Bilevel Optimization
185. NeurIPS 2024Analyzing & Reducing the Need for Learning Rate Warmup in GPT Training
186. NeurIPS 2024BitDelta: Your Fine-Tune May Only Be Worth One Bit
187. NeurIPS 2024BLAST: Block-Level Adaptive Structured Matrices for Efficient Deep Neural Network Inference
188. NeurIPS 2024BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning
189. NeurIPS 2024Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment
190. NeurIPS 2024Can Learned Optimization Make Reinforcement Learning Less Difficult?
191. NeurIPS 2024Changing the Training Data Distribution to Reduce Simplicity Bias Improves In-distribution Generalization
192. NeurIPS 2024Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift
193. NeurIPS 2024Classifier-guided Gradient Modulation for Enhanced Multimodal Learning
194. NeurIPS 2024CoBo: Collaborative Learning via Bilevel Optimization
195. NeurIPS 2024CoMERA: Computing- and Memory-Efficient Training via Rank-Adaptive Tensor Optimization
196. NeurIPS 2024Communication-Efficient Federated Group Distributionally Robust Optimization
197. NeurIPS 2024Conformal Inverse Optimization
198. NeurIPS 2024Constrained Diffusion Models via Dual Training
199. NeurIPS 2024Contextual Linear Optimization with Bandit Feedback
200. NeurIPS 2024Continuous Temporal Domain Generalization

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.