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

275 papers, page 3 of 6

101. NeurIPS 2024Gradient-Free Methods for Nonconvex Nonsmooth Stochastic Compositional Optimization
102. NeurIPS 2024Great Minds Think Alike: The Universal Convergence Trend of Input Salience
103. NeurIPS 2024Guiding Neural Collapse: Optimising Towards the Nearest Simplex Equiangular Tight Frame
104. NeurIPS 2024Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models
105. NeurIPS 2024High-probability complexity bounds for stochastic non-convex minimax optimization
106. NeurIPS 2024How Does Black-Box Impact the Learning Guarantee of Stochastic Compositional Optimization?
107. NeurIPS 2024How does Gradient Descent Learn Features --- A Local Analysis for Regularized Two-Layer Neural Networks
108. NeurIPS 2024How does PDE order affect the convergence of PINNs?
109. NeurIPS 2024Implicit Optimization Bias of Next-token Prediction in Linear Models
110. NeurIPS 2024Implicit Regularization of Decentralized Gradient Descent for Sparse Regression
111. NeurIPS 2024Improved Particle Approximation Error for Mean Field Neural Networks
112. NeurIPS 2024Improved Regret for Bandit Convex Optimization with Delayed Feedback
113. NeurIPS 2024Improving Deep Learning Optimization through Constrained Parameter Regularization
114. NeurIPS 2024Improving Equivariant Model Training via Constraint Relaxation
115. NeurIPS 2024Improving Generalization and Convergence by Enhancing Implicit Regularization
116. NeurIPS 2024Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity
117. NeurIPS 2024In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization
118. NeurIPS 2024In-Context Learning State Vector with Inner and Momentum Optimization
119. NeurIPS 2024Incorporating Test-Time Optimization into Training with Dual Networks for Human Mesh Recovery
120. NeurIPS 2024Inexact Augmented Lagrangian Methods for Conic Optimization: Quadratic Growth and Linear Convergence
121. NeurIPS 2024Inversion-based Latent Bayesian Optimization
122. NeurIPS 2024IPM-LSTM: A Learning-Based Interior Point Method for Solving Nonlinear Programs
123. NeurIPS 2024Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?
124. NeurIPS 2024John Ellipsoids via Lazy Updates
125. NeurIPS 2024Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization
126. NeurIPS 2024Last-Iterate Convergence for Generalized Frank-Wolfe in Monotone Variational Inequalities
127. NeurIPS 2024Learnability of high-dimensional targets by two-parameter models and gradient flow
128. NeurIPS 2024Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise
129. NeurIPS 2024Learning and Transferring Sparse Contextual Bigrams with Linear Transformers
130. NeurIPS 2024Learning Cut Generating Functions for Integer Programming
131. NeurIPS 2024Learning Elastic Costs to Shape Monge Displacements
132. NeurIPS 2024Learning from Noisy Labels via Conditional Distributionally Robust Optimization
133. NeurIPS 2024Learning Generalized Linear Programming Value Functions
134. NeurIPS 2024Learning to Solve Quadratic Unconstrained Binary Optimization in a Classification Way
135. NeurIPS 2024Learning with Fitzpatrick Losses
136. NeurIPS 2024Learning-Augmented Dynamic Submodular Maximization
137. NeurIPS 2024Length Optimization in Conformal Prediction
138. NeurIPS 2024LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch
139. NeurIPS 2024Linear Transformers are Versatile In-Context Learners
140. NeurIPS 2024Localized Zeroth-Order Prompt Optimization
141. NeurIPS 2024Loss Landscape Characterization of Neural Networks without Over-Parametrization
142. NeurIPS 2024Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks
143. NeurIPS 2024Lower Bounds of Uniform Stability in Gradient-Based Bilevel Algorithms for Hyperparameter Optimization
144. NeurIPS 2024Make Continual Learning Stronger via C-Flat
145. NeurIPS 2024Markov Equivalence and Consistency in Differentiable Structure Learning
146. NeurIPS 2024Mean-Field Langevin Dynamics for Signed Measures via a Bilevel Approach
147. NeurIPS 2024Memory-Efficient Gradient Unrolling for Large-Scale Bi-level Optimization
148. NeurIPS 2024Metrizing Weak Convergence with Maximum Mean Discrepancies
149. NeurIPS 2024MG-Net: Learn to Customize QAOA with Circuit Depth Awareness
150. NeurIPS 2024MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence

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.