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

736 papers, page 3 of 15

101. ICML 2026FOAM: Frequency and Operator-Error Based Adaptive Damping Method for Reducing Staleness-Oriented Error for Shampoo
102. ICML 2026From Lyapunov Analysis to Algorithm Design in two-sided PL Minimax Optimization
103. ICML 2026From Muon to Gluon: Bridging Theory and Practice of LMO-based Optimizers for LLMs
104. ICML 2026From Optimization to Generalization under Heavy-Tailed Data: The Role of Gradient Clipping
105. ICML 2026Generative Large Neighborhood Search: Scalable Set Cover Optimization via Discrete Diffusion
106. ICML 2026Geometric Convergence of Gauss–Newton for Neural Networks: Riemannian Geometry and Adaptive Damping
107. ICML 2026Geometry-Misalignment in Distributional Learning
108. ICML 2026GIPO: Gaussian Importance Sampling Policy Optimization
109. ICML 2026GIST: Targeted Data Selection for Instruction Tuning via Coupled Optimization Geometry
110. ICML 2026Global Convergence of Adaptive Sensing for Principal Eigenvector Estimation
111. ICML 2026Goal-Oriented Lower-Tail Calibration of Gaussian Processes for Bayesian Optimization
112. ICML 2026Gradient Flow Sampler-based Distributionally Robust Optimization
113. ICML 2026Gradient Smoothing: Coupling Layer-wise Updates for Improved Optimization
114. ICML 2026Gradient Testing and Estimation by Comparisons
115. ICML 2026Gradient-Free Approaches is a Key to an Efficient Interaction with Markovian Stochasticity
116. ICML 2026GradPower: Powering Gradients for Faster Language Model Pre-Training
117. ICML 2026GRPO is Secretly a Process Reward Model
118. ICML 2026Hierarchical Decision Making with Structured Policies: A Principled Design via Inverse Optimization
119. ICML 2026High-Probability Convergence Guarantees of Decentralized SGD
120. ICML 2026HO-SFL: Hybrid-Order Split Federated Learning with Backprop-Free Clients and Dimension-Free Aggregation
121. ICML 2026Hom-PGD$^+$: Fast Reparameterized Optimization over Non-convex Ball-Homeomorphic Set
122. ICML 2026HONet: Data-Efficient Learning for Exact Cover Tasks via Hypergraph Optimization
123. ICML 2026How to Train Your Advisor: Steering Black-Box LLMs with Advisor Models
124. ICML 2026Improved Convergence Analysis of Topology Dependence in Decentralized SGD
125. ICML 2026Improved Dimension Dependence for Bandit Convex Optimization with Gradient Variation
126. ICML 2026Improved Stochastic Optimization of LogSumExp
127. ICML 2026Improving Explicit Dynamic Gaussian Splatting Optimization via Update Mixture
128. ICML 2026In-Context Learning as Rate–Distortion Optimization
129. ICML 2026Incremental Learning of Sparse Attention Patterns in Transformers
130. ICML 2026Iterated Population Based Training with Task-Agnostic Restarts
131. ICML 2026Iterative Robust Satisficing: Minimizing Performance Degradation Under Distribution Shift
132. ICML 2026Joint Learning in the Gaussian Single Index Model
133. ICML 2026KernelFoundry: Hardware-Aware Evolutionary GPU Kernel Optimization
134. ICML 2026L-SR1: Learned Symmetric-Rank-One Preconditioning
135. ICML 2026Langevin Rollout Optimization for Modelic Reinforcement Learning
136. ICML 2026Last-iterate Convergence of ADMM on Multi-affine Quadratic Equality Constrained Problem
137. ICML 2026Latent Space Robust Optimization of Neural Processes with Aligned Stratified Order-Statistic Loss Reduction
138. ICML 2026Layer-wise Gradient Disentanglement: Decoupling Semantics and Preferences in Direct Preference Optimization
139. ICML 2026Learning Credal Ensembles via Distributionally Robust Optimization
140. ICML 2026Learning Dynamics of Zeroth-Order Optimization: A Kernel Perspective
141. ICML 2026Learning from Comparison: Constrained Projection Policy Optimization for Pareto-Front Improvement
142. ICML 2026Learning High-Dimensional Parity Functions with Product Networks using Gradient Descent
143. ICML 2026Learning Permutation from Structure Without Supervision
144. ICML 2026Learning Rate Annealing Improves Tuning Robustness in Stochastic Optimization
145. ICML 2026Learning-Augmented Scalable Linear Assignment Problem Optimization via Neural Dual Warm-Starts
146. ICML 2026Learning-to-Optimize via Deep Unfolded Flows
147. ICML 2026LILO: Bayesian Optimization with Natural Language Feedback
148. ICML 2026LiMuon: Light and Fast Muon Optimizer for Large Models
149. ICML 2026Linear-Core Surrogates: Smooth Loss Functions with Linear Rates for Classification and Structured Prediction
150. ICML 2026Lions and Muons: Optimization via Stochastic Frank-Wolfe under Heavy-Tailed Noises

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.