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

953 papers, page 3 of 20

101. ICML 2026NEMO: Execution-Aware Optimization Modeling via Autonomous Coding Agents
102. ICML 2026Non-Parametric Optimization for Scalable Learning in Stochastic Decision Problems
103. ICML 2026On the Convergence of Decentralized Stochastic Minimax Optimization Algorithm with Compressed Communication
104. ICML 2026On the Optimization Trajectory of DeepWalk Embeddings
105. ICML 2026On the Provable Suboptimality of Momentum SGD in Nonstationary Stochastic Optimization
106. ICML 2026Optimal Transport with Symmetry Groups
107. ICML 2026PASO: Step Parallel Stochastic Optimization
108. ICML 2026PepCompass: Navigating Peptide Embedding Spaces Using Riemannian Geometry
109. ICML 2026Projection-Free Algorithms for Minimax Problems
110. ICML 2026pTNAS: Progressive Neural Architecture Search for Tabular Data
111. ICML 2026Reducing Per-Sample Harm in Stochastic Optimization
112. ICML 2026Reflect-then-Correct: Rebalancing Task Optimization for Generalizable Meta-Reinforcement Learning via Distributional Value Error Reduction
113. ICML 2026Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models
114. ICML 2026Riemannian Dueling Optimization
115. ICML 2026Riemannian Optimization for Fair Spectral Clustering
116. ICML 2026Riemannian stochastic optimization for sufficient dimension reduction
117. ICML 2026RMNP: Row-Momentum Normalized Preconditioning for Scalable Matrix-Based Optimization
118. ICML 2026Robust Contextual Optimization with Missing Covariates
119. ICML 2026Robust Learning via Nested Distributionally Robust Optimization
120. ICML 2026Safeguarded Stochastic Polyak Step Sizes for Non-smooth Optimization: Robust Performance Without Small (Sub)Gradients
121. ICML 2026Second-Order Bilevel Optimization with Accelerated Convergence Rates
122. ICML 2026Sharpness-Aware Minimization Can Hallucinate Minimizers
123. ICML 2026SimpleGPT: Improving GPT via A Simple Normalization Strategy
124. ICML 2026Simultaneous Confidence Bounds for Aggregated Effects via Exact Subset Optimization
125. ICML 2026SoftJAX & SoftTorch: Empowering Automatic Differentiation Libraries with Informative Gradients
126. ICML 2026Solving Stochastic Variational Inequalities without the Bounded Variance Assumption
127. ICML 2026Step-Size Stability in Stochastic Optimization: A Theoretical Perspective
128. ICML 2026Stochastic Gradient Methods under Heavy-Tailed Noises in Weakly Convex Optimization
129. ICML 2026Submodular Optimization for Minimal Augmentation in Robust Language Model Alignment
130. ICML 2026SVRG and Beyond via Posterior Correction
131. ICML 2026TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching
132. ICML 2026TSP with Predictions: Heatmap to Tour with Provable Guarantees
133. ICML 2026Unleashing Implicit Rewards: Prefix-Value Learning for Distribution-Level Optimization
134. ICML 2026Variable Clustering via Distributionally Robust Nodewise Regression
135. ICML 2026Weight Decay Improves Language Model Plasticity
136. ICML 2026Why Do We Need Warm-up? A Theoretical Perspective
137. ICML 2026Zeroth-Order Optimization at the Edge of Stability
138. NeurIPS 2026A Doubly Smoothed Decentralized Stochastic Minimax Optimization Algorithm
139. NeurIPS 2026Achieving Better Local Regret Bound for Online Non-Convex Bilevel Optimization
140. NeurIPS 2026Achieving Directional-Stationarity from a Single Random Direction Step
141. NeurIPS 2026Adaptive Conditional Gradient Sliding: Projection-Free and Line-Search-Free Acceleration
142. NeurIPS 2026Adaptive-Margin Masking and Restoration for Balanced Multimodal Learning
143. NeurIPS 2026Adaptively Incorporating Directional Hints into Zeroth-Order Optimization
144. NeurIPS 2026An Educated Guess: Deriving Statistically Aligned Gradient Estimates for Zeroth-Order Optimization
145. NeurIPS 2026BBOWP-Bench: Evaluating LLMs on Black-Box Optimization Word Problems
146. NeurIPS 2026Beyond Bounded Variance: Variance-Reduced Normalized Methods for Nonconvex Optimization under Blum-Gladyshev Noise
147. NeurIPS 2026Capacity Allocation at the Source: Sparse Target Optimization for LLM Knowledge Editing
148. NeurIPS 2026Certified Single-Level Reformulation for Tri-Level Cyber-Physical Grid Security
149. NeurIPS 2026Closed-Form Last Layer Optimization
150. NeurIPS 2026Continuous p-adic Optimization

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.