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

2,330 papers, page 33 of 47

1601. ICML 2024Efficient Algorithms for Empirical Group Distributionally Robust Optimization and Beyond
1602. ICML 2024Efficient Algorithms for Sum-Of-Minimum Optimization
1603. ICML 2024Efficient Stochastic Approximation of Minimax Excess Risk Optimization
1604. ICML 2024Equivariant Deep Weight Space Alignment
1605. ICML 2024Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring
1606. ICML 2024Exploration-Driven Policy Optimization in RLHF: Theoretical Insights on Efficient Data Utilization
1607. ICML 2024Exploring the Complexity of Deep Neural Networks through Functional Equivalence
1608. ICML 2024FADAS: Towards Federated Adaptive Asynchronous Optimization
1609. ICML 2024Faster Adaptive Decentralized Learning Algorithms
1610. ICML 2024Federated Optimization with Doubly Regularized Drift Correction
1611. ICML 2024Flora: Low-Rank Adapters Are Secretly Gradient Compressors
1612. ICML 2024From Generalization Analysis to Optimization Designs for State Space Models
1613. ICML 2024From Inverse Optimization to Feasibility to ERM
1614. ICML 2024Fundamental Benefit of Alternating Updates in Minimax Optimization
1615. ICML 2024Generalized Preference Optimization: A Unified Approach to Offline Alignment
1616. ICML 2024Gradient Compressed Sensing: A Query-Efficient Gradient Estimator for High-Dimensional Zeroth-Order Optimization
1617. ICML 2024High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling
1618. ICML 2024High-Probability Bound for Non-Smooth Non-Convex Stochastic Optimization with Heavy Tails
1619. ICML 2024High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise
1620. ICML 2024How Free is Parameter-Free Stochastic Optimization?
1621. ICML 2024How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization
1622. ICML 2024ILILT: Implicit Learning of Inverse Lithography Technologies
1623. ICML 2024Implicit Bias of AdamW: $\ell_\infty$-Norm Constrained Optimization
1624. ICML 2024Improved Differentially Private and Lazy Online Convex Optimization: Lower Regret without Smoothness Requirements
1625. ICML 2024Improving Robustness to Multiple Spurious Correlations by Multi-Objective Optimization
1626. ICML 2024Improving SAM Requires Rethinking its Optimization Formulation
1627. ICML 2024In-context Convergence of Transformers
1628. ICML 2024In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
1629. ICML 2024InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models
1630. ICML 2024Interpreting and Improving Diffusion Models from an Optimization Perspective
1631. ICML 2024Iterative Regularized Policy Optimization with Imperfect Demonstrations
1632. ICML 2024Joint Composite Latent Space Bayesian Optimization
1633. ICML 2024KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions
1634. ICML 2024LASER: Linear Compression in Wireless Distributed Optimization
1635. ICML 2024Learning to Remove Cuts in Integer Linear Programming
1636. ICML 2024Learning with 3D rotations, a hitchhiker's guide to SO(3)
1637. ICML 2024Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds
1638. ICML 2024Locally Differentially Private Decentralized Stochastic Bilevel Optimization with Guaranteed Convergence Accuracy
1639. ICML 2024LPGD: A General Framework for Backpropagation through Embedded Optimization Layers
1640. ICML 2024MALIBO: Meta-learning for Likelihood-free Bayesian Optimization
1641. ICML 2024Mitigating Catastrophic Forgetting in Online Continual Learning by Modeling Previous Task Interrelations via Pareto Optimization
1642. ICML 2024MLI Formula: A Nearly Scale-Invariant Solution with Noise Perturbation
1643. ICML 2024Model Alignment as Prospect Theoretic Optimization
1644. ICML 2024Moreau Envelope for Nonconvex Bi-Level Optimization: A Single-Loop and Hessian-Free Solution Strategy
1645. ICML 2024No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths
1646. ICML 2024Non-convex Stochastic Composite Optimization with Polyak Momentum
1647. ICML 2024Non-stationary Online Convex Optimization with Arbitrary Delays
1648. ICML 2024Offline Multi-Objective Optimization
1649. ICML 2024On Convergence of Incremental Gradient for Non-convex Smooth Functions
1650. ICML 2024On Discrete Prompt Optimization for Diffusion Models

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.