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

154 papers, page 3 of 4

101. NeurIPS 2023Oracle Complexity of Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Functional Constrained Optimization
102. NeurIPS 2023Ordering-based Conditions for Global Convergence of Policy Gradient Methods
103. NeurIPS 2023Outlier-Robust Gromov-Wasserstein for Graph Data
104. NeurIPS 2023Outlier-Robust Wasserstein DRO
105. NeurIPS 2023Parallel Submodular Function Minimization
106. NeurIPS 2023Parallel-mentoring for Offline Model-based Optimization
107. NeurIPS 2023Physics-Informed Bayesian Optimization of Variational Quantum Circuits
108. NeurIPS 2023Policy Optimization for Continuous Reinforcement Learning
109. NeurIPS 2023Practical Sharpness-Aware Minimization Cannot Converge All the Way to Optima
110. NeurIPS 2023Preconditioning Matters: Fast Global Convergence of Non-convex Matrix Factorization via Scaled Gradient Descent
111. NeurIPS 2023PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning
112. NeurIPS 2023Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks
113. NeurIPS 2023Projection-Free Methods for Solving Nonconvex-Concave Saddle Point Problems
114. NeurIPS 2023Projection-Free Methods for Stochastic Simple Bilevel Optimization with Convex Lower-level Problem
115. NeurIPS 2023PROTES: Probabilistic Optimization with Tensor Sampling
116. NeurIPS 2023Provable convergence guarantees for black-box variational inference
117. NeurIPS 2023Quantum Bayesian Optimization
118. NeurIPS 2023Quantum speedups for stochastic optimization
119. NeurIPS 2023Rank-1 Matrix Completion with Gradient Descent and Small Random Initialization
120. NeurIPS 2023Recovering Simultaneously Structured Data via Non-Convex Iteratively Reweighted Least Squares
121. NeurIPS 2023Reducing Blackwell and Average Optimality to Discounted MDPs via the Blackwell Discount Factor
122. NeurIPS 2023ReSync: Riemannian Subgradient-based Robust Rotation Synchronization
123. NeurIPS 2023Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial?
124. NeurIPS 2023Riemannian Projection-free Online Learning
125. NeurIPS 2023Riemannian SAM: Sharpness-Aware Minimization on Riemannian Manifolds
126. NeurIPS 2023Riemannian stochastic optimization methods avoid strict saddle points
127. NeurIPS 2023Robust Bayesian Satisficing
128. NeurIPS 2023Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity
129. NeurIPS 2023Robust Learning for Smoothed Online Convex Optimization with Feedback Delay
130. NeurIPS 2023Robust Matrix Sensing in the Semi-Random Model
131. NeurIPS 2023Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing
132. NeurIPS 2023Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms
133. NeurIPS 2023Sampling weights of deep neural networks
134. NeurIPS 2023Searching for Optimal Per-Coordinate Step-sizes with Multidimensional Backtracking
135. NeurIPS 2023Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions
136. NeurIPS 2023SLM: A Smoothed First-Order Lagrangian Method for Structured Constrained Nonconvex Optimization
137. NeurIPS 2023SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data
138. NeurIPS 2023Stability and Generalization of the Decentralized Stochastic Gradient Descent Ascent Algorithm
139. NeurIPS 2023Stable Nonconvex-Nonconcave Training via Linear Interpolation
140. NeurIPS 2023StEik: Stabilizing the Optimization of Neural Signed Distance Functions and Finer Shape Representation
141. NeurIPS 2023Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis
142. NeurIPS 2023TD Convergence: An Optimization Perspective
143. NeurIPS 2023The Behavior and Convergence of Local Bayesian Optimization
144. NeurIPS 2023Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance
145. NeurIPS 2023Training Your Image Restoration Network Better with Random Weight Network as Optimization Function
146. NeurIPS 2023Two Sides of One Coin: the Limits of Untuned SGD and the Power of Adaptive Methods
147. NeurIPS 2023Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?
148. NeurIPS 2023Understanding Multi-phase Optimization Dynamics and Rich Nonlinear Behaviors of ReLU Networks
149. NeurIPS 2023Unexpected Improvements to Expected Improvement for Bayesian Optimization
150. NeurIPS 2023Universal Gradient Descent Ascent Method for Nonconvex-Nonconcave Minimax Optimization

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.