Nonconvex Optimization papers, 2023–2026

Key findings

• Nonconvex Optimization went from 346 papers in 2023 to 757 in 2026, roughly 2.2x in three years.
• NeurIPS publishes the most Nonconvex Optimization work: 958 papers from 2023 to 2026, 41% of the four-venue total.
• The fastest-growing overlap is with Diffusion Models: papers tagged with both rose from 2 in 2023 to 44 in 2026.
• Among the 5 topics under Optimization and Scaling, it ranks #2 by 2026 paper count; the fastest grower in the group, Scaling Laws, is up 4.1x versus 2.2x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026$\gamma$-weakly $\theta$-up-concavity: A Unified Framework for Non-Convex Optimization Beyond DR-Submodular and OSS Functions
• ICML 2026$\texttt{MetaDistill}$: Unlocking the Performance Ceiling for Pretrained Optimizers
• ICLR 2026Behavioral Embeddings of Programs: A Quasi-Dynamic Approach for Optimization Prediction
• CVPR 2026Coverage Optimization for Camera View Selection
• NeurIPS 2025Beyond Random: Automatic Inner-loop Optimization in Dataset Distillation
• ICML 2025A Bregman Proximal Viewpoint on Neural Operators
• ICLR 2025A Truncated Newton Method for Optimal Transport
• CVPR 2025Balanced Rate-Distortion Optimization in Learned Image Compression
• NeurIPS 2024A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences
• ICML 2024A Doubly Recursive Stochastic Compositional Gradient Descent Method for Federated Multi-Level Compositional Optimization
• ICLR 2024Bayesian Coreset Optimization for Personalized Federated Learning
• CVPR 2024FaceCom: Towards High-fidelity 3D Facial Shape Completion via Optimization and Inpainting Guidance
• NeurIPS 2023A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting
• ICML 2023A Distribution Optimization Framework for Confidence Bounds of Risk Measures
• ICLR 2023A view of mini-batch SGD via generating functions: conditions of convergence, phase transitions, benefit from negative momenta.
• CVPR 2023Attribute-Preserving Face Dataset Anonymization via Latent Code Optimization
• NeurIPS 2026A Doubly Smoothed Decentralized Stochastic Minimax Optimization Algorithm
• ICML 2026A Constrained Optimization Perspective of Unrolled Transformers
• ICLR 2026BEP: A Binary Error Propagation Algorithm for Binary Neural Networks Training
• CVPR 2026Fully Decentralized Certified Unlearning

All Nonconvex Optimization papers

560 papers, page 4 of 12

151. ICML 2024Boosting Offline Optimizers with Surrogate Sensitivity
152. ICML 2024BOtied: Multi-objective Bayesian optimization with tied multivariate ranks
153. ICML 2024Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints
154. ICML 2024Can Gaussian Sketching Converge Faster on a Preconditioned Landscape?
155. ICML 2024Challenges in Training PINNs: A Loss Landscape Perspective
156. ICML 2024Closing the Gap: Achieving Global Convergence (Last Iterate) of Actor-Critic under Markovian Sampling with Neural Network Parametrization
157. ICML 2024Compact Optimality Verification for Optimization Proxies
158. ICML 2024Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation
159. ICML 2024Convergence and Complexity Guarantee for Inexact First-order Riemannian Optimization Algorithms
160. ICML 2024Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point
161. ICML 2024Convergence Guarantees for the DeepWalk Embedding on Block Models
162. ICML 2024Convergence of Some Convex Message Passing Algorithms to a Fixed Point
163. ICML 2024Convex and Bilevel Optimization for Neural-Symbolic Inference and Learning
164. ICML 2024Dealing With Unbounded Gradients in Stochastic Saddle-point Optimization
165. ICML 2024Decentralized Convex Finite-Sum Optimization with Better Dependence on Condition Numbers
166. ICML 2024Deconstructing the Goldilocks Zone of Neural Network Initialization
167. ICML 2024Degeneration-free Policy Optimization: RL Fine-Tuning for Language Models without Degeneration
168. ICML 2024Delving into the Convergence of Generalized Smooth Minimax Optimization
169. ICML 2024Differentiability and Optimization of Multiparameter Persistent Homology
170. ICML 2024Differentiable Distributionally Robust Optimization Layers
171. ICML 2024Differentiable Mapper for Topological Optimization of Data Representation
172. ICML 2024Distributed Bilevel Optimization with Communication Compression
173. ICML 2024Distribution Alignment Optimization through Neural Collapse for Long-tailed Classification
174. ICML 2024Double Momentum Method for Lower-Level Constrained Bilevel Optimization
175. ICML 2024Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient
176. ICML 2024Dynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization
177. ICML 2024Efficient Algorithms for Empirical Group Distributionally Robust Optimization and Beyond
178. ICML 2024Efficient Algorithms for Sum-Of-Minimum Optimization
179. ICML 2024Efficient Stochastic Approximation of Minimax Excess Risk Optimization
180. ICML 2024Equivariant Deep Weight Space Alignment
181. ICML 2024Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring
182. ICML 2024Exploration-Driven Policy Optimization in RLHF: Theoretical Insights on Efficient Data Utilization
183. ICML 2024Exploring the Complexity of Deep Neural Networks through Functional Equivalence
184. ICML 2024FADAS: Towards Federated Adaptive Asynchronous Optimization
185. ICML 2024Faster Adaptive Decentralized Learning Algorithms
186. ICML 2024Federated Optimization with Doubly Regularized Drift Correction
187. ICML 2024Flora: Low-Rank Adapters Are Secretly Gradient Compressors
188. ICML 2024From Generalization Analysis to Optimization Designs for State Space Models
189. ICML 2024From Inverse Optimization to Feasibility to ERM
190. ICML 2024Fundamental Benefit of Alternating Updates in Minimax Optimization
191. ICML 2024Generalized Preference Optimization: A Unified Approach to Offline Alignment
192. ICML 2024Gradient Compressed Sensing: A Query-Efficient Gradient Estimator for High-Dimensional Zeroth-Order Optimization
193. ICML 2024High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling
194. ICML 2024High-Probability Bound for Non-Smooth Non-Convex Stochastic Optimization with Heavy Tails
195. ICML 2024High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise
196. ICML 2024How Free is Parameter-Free Stochastic Optimization?
197. ICML 2024How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization
198. ICML 2024ILILT: Implicit Learning of Inverse Lithography Technologies
199. ICML 2024Implicit Bias of AdamW: $\ell_\infty$-Norm Constrained Optimization
200. ICML 2024Improved Differentially Private and Lazy Online Convex Optimization: Lower Regret without Smoothness Requirements

FAQ

How many Nonconvex Optimization papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

2,330 in total: 346 in 2023, 560 in 2024, 667 in 2025, 757 in 2026.

How many Nonconvex Optimization papers are at NeurIPS 2026?

NeurIPS 2026 has 250 Nonconvex Optimization papers, versus 279 the year before.

How many Nonconvex Optimization papers are at ICLR 2026?

ICLR 2026 has 178 Nonconvex Optimization papers, versus 177 the year before.

How many Nonconvex Optimization papers are at ICML 2026?

ICML 2026 has 286 Nonconvex Optimization papers, versus 192 the year before.

How many Nonconvex Optimization papers are at CVPR 2026?

CVPR 2026 has 43 Nonconvex Optimization papers, versus 19 the year before.

Which conference publishes the most Nonconvex Optimization papers?

NeurIPS, with 958 papers from 2023 to 2026.