Deep learning · optimization papers, 2023–2026

Key findings

• Deep learning · optimization fell from 256 papers in 2023 to 240 in 2026, so interest is cooling.
• NeurIPS publishes the most Deep learning · optimization work: 500 papers from 2023 to 2026, 52% of the four-venue total.
• The fastest-growing overlap is with Alignment and Safety: papers tagged with both rose from 5 in 2023 to 23 in 2026.
• Among the 11 topics under Deep Learning, it ranks #6 by 2026 paper count; the fastest grower in the group, Diffusion Models, is up 4.1x versus 0.9x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026Achieving Directional-Stationarity from a Single Random Direction Step
• ICML 2026Bottleneck Communication Delay Minimization for Communication-Efficient Decentralized Learning
• ICLR 2026From Sorting Algorithms to Scalable Kernels: Bayesian Optimization in High-Dimensional Permutation Spaces
• CVPR 2026Coverage Optimization for Camera View Selection
• NeurIPS 2025High-Performance Arithmetic Circuit Optimization via Differentiable Architecture Search
• ICML 2025Feature-Mapping Topology Optimization with Neural Heaviside Signed Distance Functions
• ICLR 2025Boosting Neural Combinatorial Optimization for Large-Scale Vehicle Routing Problems
• CVPR 2025HotSpot: Signed Distance Function Optimization with an Asymptotically Sufficient Condition
• NeurIPS 2024$\boldsymbol{\mu}\mathbf{P^2}$: Effective Sharpness Aware Minimization Requires Layerwise Perturbation Scaling
• ICML 2024Barrier Algorithms for Constrained Non-Convex Optimization
• ICLR 2024Bayesian Coreset Optimization for Personalized Federated Learning
• CVPR 2024Multi-Session SLAM with Differentiable Wide-Baseline Pose Optimization
• NeurIPS 2023$\textbf{A}^2\textbf{CiD}^2$: Accelerating Asynchronous Communication in Decentralized Deep Learning
• ICML 2023Buying Information for Stochastic Optimization
• ICLR 2023Alternating Differentiation for Optimization Layers
• CVPR 2023Visual-Language Prompt Tuning With Knowledge-Guided Context Optimization
• NeurIPS 2026Adaptive Conditional Gradient Sliding: Projection-Free and Line-Search-Free Acceleration
• ICML 2026Riemannian Optimization for Fair Spectral Clustering
• ICLR 2026Gradient-Based Diversity Optimization with Differentiable Top-$k$ Objective
• CVPR 2026Its Never Too Late Noise Optimization for Collapse Recovery in

All Deep learning · optimization papers

272 papers, page 2 of 6

51. ICML 2026Non-Parametric Optimization for Scalable Learning in Stochastic Decision Problems
52. ICML 2026On the Convergence of Decentralized Stochastic Minimax Optimization Algorithm with Compressed Communication
53. ICML 2026On the Optimization Trajectory of DeepWalk Embeddings
54. ICML 2026On the Provable Suboptimality of Momentum SGD in Nonstationary Stochastic Optimization
55. ICML 2026Optimal Transport with Symmetry Groups
56. ICML 2026PASO: Step Parallel Stochastic Optimization
57. ICML 2026PepCompass: Navigating Peptide Embedding Spaces Using Riemannian Geometry
58. ICML 2026Projection-Free Algorithms for Minimax Problems
59. ICML 2026pTNAS: Progressive Neural Architecture Search for Tabular Data
60. ICML 2026Reducing Per-Sample Harm in Stochastic Optimization
61. ICML 2026Reflect-then-Correct: Rebalancing Task Optimization for Generalizable Meta-Reinforcement Learning via Distributional Value Error Reduction
62. ICML 2026Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models
63. ICML 2026Riemannian Dueling Optimization
64. ICML 2026Riemannian Optimization for Fair Spectral Clustering
65. ICML 2026Riemannian stochastic optimization for sufficient dimension reduction
66. ICML 2026RMNP: Row-Momentum Normalized Preconditioning for Scalable Matrix-Based Optimization
67. ICML 2026Robust Contextual Optimization with Missing Covariates
68. ICML 2026Robust Learning via Nested Distributionally Robust Optimization
69. ICML 2026Safeguarded Stochastic Polyak Step Sizes for Non-smooth Optimization: Robust Performance Without Small (Sub)Gradients
70. ICML 2026Second-Order Bilevel Optimization with Accelerated Convergence Rates
71. ICML 2026Sharpness-Aware Minimization Can Hallucinate Minimizers
72. ICML 2026SimpleGPT: Improving GPT via A Simple Normalization Strategy
73. ICML 2026Simultaneous Confidence Bounds for Aggregated Effects via Exact Subset Optimization
74. ICML 2026SoftJAX & SoftTorch: Empowering Automatic Differentiation Libraries with Informative Gradients
75. ICML 2026Solving Stochastic Variational Inequalities without the Bounded Variance Assumption
76. ICML 2026Step-Size Stability in Stochastic Optimization: A Theoretical Perspective
77. ICML 2026Stochastic Gradient Methods under Heavy-Tailed Noises in Weakly Convex Optimization
78. ICML 2026Submodular Optimization for Minimal Augmentation in Robust Language Model Alignment
79. ICML 2026SVRG and Beyond via Posterior Correction
80. ICML 2026TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching
81. ICML 2026TSP with Predictions: Heatmap to Tour with Provable Guarantees
82. ICML 2026Unleashing Implicit Rewards: Prefix-Value Learning for Distribution-Level Optimization
83. ICML 2026Variable Clustering via Distributionally Robust Nodewise Regression
84. ICML 2026Weight Decay Improves Language Model Plasticity
85. ICML 2026Why Do We Need Warm-up? A Theoretical Perspective
86. ICML 2026Zeroth-Order Optimization at the Edge of Stability
87. ICML 2025A Bregman Proximal Viewpoint on Neural Operators
88. ICML 2025A Memory Efficient Randomized Subspace Optimization Method for Training Large Language Models
89. ICML 2025A Near-Optimal Single-Loop Stochastic Algorithm for Convex Finite-Sum Coupled Compositional Optimization
90. ICML 2025Accelerating PDE-Constrained Optimization by the Derivative of Neural Operators
91. ICML 2025Attention-Only Transformers via Unrolled Subspace Denoising
92. ICML 2025BackSlash: Rate Constrained Optimized Training of Large Language Models
93. ICML 2025Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation
94. ICML 2025BSO: Binary Spiking Online Optimization Algorithm
95. ICML 2025Decoupled SGDA for Games with Intermittent Strategy Communication
96. ICML 2025DTZO: Distributed Trilevel Zeroth Order Learning with Provable Non-Asymptotic Convergence
97. ICML 2025Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method
98. ICML 2025Enabling Optimal Decisions in Rehearsal Learning under CARE Condition
99. ICML 2025Enhancing Parallelism in Decentralized Stochastic Convex Optimization
100. ICML 2025Ensemble Learned Bloom Filters: Two Oracles are Better than One

FAQ

How many Deep learning · optimization papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

953 in total: 256 in 2023, 294 in 2024, 163 in 2025, 240 in 2026.

How many Deep learning · optimization papers are at NeurIPS 2026?

NeurIPS 2026 has 103 Deep learning · optimization papers, versus 62 the year before.

How many Deep learning · optimization papers are at ICLR 2026?

ICLR 2026 has 44 Deep learning · optimization papers, versus 51 the year before.

How many Deep learning · optimization papers are at ICML 2026?

ICML 2026 has 86 Deep learning · optimization papers, versus 44 the year before.

How many Deep learning · optimization papers are at CVPR 2026?

CVPR 2026 has 7 Deep learning · optimization papers, versus 6 the year before.

Which conference publishes the most Deep learning · optimization papers?

NeurIPS, with 500 papers from 2023 to 2026.