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

250 papers, page 2 of 5

51. NeurIPS 2026Decoupled Optimization for Teacher-Student Semi-Supervised Learning via a Pioneer Student
52. NeurIPS 2026Deriving Hyperparameter Scaling Laws via Modern Optimization Theory
53. NeurIPS 2026Diff3R: Feed-forward 3D Gaussian Splatting with Uncertainty Aware Differentiable Optimization
54. NeurIPS 2026Diffusion Transformers with Residual Adaptive Layer Normalization
55. NeurIPS 2026Diffusion-State Policy Optimization for Masked Diffusion Language Models
56. NeurIPS 2026DisCoMBO: Steering Expert-in-the-Loop Black Box Optimization via Distributional Conformance
57. NeurIPS 2026Discrete Flow Matching: Convergence Guarantees Under Minimal Assumptions
58. NeurIPS 2026Disentangling Optimization Geometry via Hierarchical Polar Adapters for Class-Incremental Learning
59. NeurIPS 2026Distribution-Adaptive Policy Optimization
60. NeurIPS 2026Distributionally Robust Black Box Optimization-based Bidding Strategy in Auction-based Federated Learning
61. NeurIPS 2026Distributionally Robust Listwise Preference Optimization
62. NeurIPS 2026Distributionally Robust Token Optimization in RLHF
63. NeurIPS 2026Do We Need Asynchronous SGD? On the Near-Optimality of Synchronous Methods
64. NeurIPS 2026Don't Waste Population: Post-Anneal Refinement for Combinatorial Optimization
65. NeurIPS 2026DP-EGGROLL: Centered Fitness-Vector Privatization for Backprop-Free Differentially Private Optimization
66. NeurIPS 2026DrPO: Drifting Preference Optimization for One-Step Generative Models
67. NeurIPS 2026DT-PBO: an Interpretable Tree-based Surrogate Model for Preferential Bayesian Optimization
68. NeurIPS 2026E-GEO: A Testbed for Generative Engine Optimization in E-Commerce
69. NeurIPS 2026EDISCO: Equivariant Discrete Diffusion for Euclidean Combinatorial Optimization
70. NeurIPS 2026EMA-Nesterov: Stabilizing Nesterov's Lookahead for Accelerated Deep Learning Optimization
71. NeurIPS 2026ENGINE: Endogenous Variational MultiScale Optimization for Zeroth-Order LLM Fine-Tuning
72. NeurIPS 2026EpiPivot: Learning to Control the Simplex Method under Epistemic Uncertainty
73. NeurIPS 2026Evolution Fine-Tuning: Learning to Discover Across 371 Optimization Tasks
74. NeurIPS 2026Exploration as Constrained Policy-Space Optimization
75. NeurIPS 2026Federated Distributionally Robust Neural Combinatorial Optimization with Convergence Guarantees
76. NeurIPS 2026Finding Simple Proofs for First-Order Optimization
77. NeurIPS 2026Finite-Resolution Decision Sufficiency for Linear Optimization
78. NeurIPS 2026Flow Matching Policy Optimization with Mirror Descent and Entropy Constraints
79. NeurIPS 2026Flow-Corrected Shape Optimization: Taming Manifold Drift in High-Dimensional 3D Models
80. NeurIPS 2026Follow the Regularized Leader Does Not Converge in Constrained Optimization
81. NeurIPS 2026From Cursed to Competitive: Closing the ZO–FO Gap via Input-to-State Stability
82. NeurIPS 2026From Inexact Gradients to Byzantine Robustness: Acceleration and Optimization under Similarity
83. NeurIPS 2026Fully First-Order Algorithms for Online Non-Convex Bilevel Optimization
84. NeurIPS 2026Functional Gradient Descent with Adaptive Representations
85. NeurIPS 2026Geometry-Aware Zeroth-Order Optimization for Fine-Tuning Quantized LLMs
86. NeurIPS 2026Global Convergence in Deep Networks via the Second Law of Thermodynamics
87. NeurIPS 2026Global Convergence of Four-Layer Matrix Factorization under Random Initialization
88. NeurIPS 2026Gradient Descent’s Last Iterate is Often (slightly) Suboptimal
89. NeurIPS 2026Gradient Regularized Newton Boosting Trees with Global Convergence
90. NeurIPS 2026Group Distributionally Robust Optimization with Flexible Sample Queries
91. NeurIPS 2026Group-Graph Policy Optimization for Long-Horizon Agentic Reinforcement Learning
92. NeurIPS 2026Guaranteed Noisy CP Tensor Recovery via Riemannian Optimization on the Segre Manifold
93. NeurIPS 2026HALO: Homotopy-Augmented Layer Optimization for Stable LLM Supervised Post-training
94. NeurIPS 2026Hawkeye: Hardware-Aware GPU Kernel Optimization with Minimal Supervision
95. NeurIPS 2026How Neural Reward Models Learn Features for Policy Optimization: A Single-Index Analysis
96. NeurIPS 2026Improved Regret Analysis For Parallel Gaussian Process Bandit Optimization
97. NeurIPS 2026Improving Quantized Zeroth-Order Optimization through Reconstructed Low-Rank Structures
98. NeurIPS 2026Improving the Optimization Landscape of Matrix Completion with $\epsilon$-close Surrogates
99. NeurIPS 2026In-Context Black-Box Optimization with Unreliable Feedback
100. NeurIPS 2026In-Expectation Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise

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.