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

159 papers, page 3 of 4

101. ICLR 2024Bayesian Coreset Optimization for Personalized Federated Learning
102. ICLR 2024Communication-Efficient Federated Non-Linear Bandit Optimization
103. ICLR 2024Constraint-Free Structure Learning with Smooth Acyclic Orientations
104. ICLR 2024Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold
105. ICLR 2024Det-CGD: Compressed Gradient Descent with Matrix Stepsizes for Non-Convex Optimization
106. ICLR 2024Dropout Enhanced Bilevel Training
107. ICLR 2024Fast Equilibrium of SGD in Generic Situations
108. ICLR 2024FedDA: Faster Adaptive Gradient Methods for Federated Constrained Optimization
109. ICLR 2024Implicit Maximum a Posteriori Filtering via Adaptive Optimization
110. ICLR 2024Large Language Models to Enhance Bayesian Optimization
111. ICLR 2024Learning model uncertainty as variance-minimizing instance weights
112. ICLR 2024Linear attention is (maybe) all you need (to understand Transformer optimization)
113. ICLR 2024Lion Secretly Solves a Constrained Optimization: As Lyapunov Predicts
114. ICLR 2024Local Composite Saddle Point Optimization
115. ICLR 2024Magnitude Invariant Parametrizations Improve Hypernetwork Learning
116. ICLR 2024Most discriminative stimuli for functional cell type clustering
117. ICLR 2024Neur2RO: Neural Two-Stage Robust Optimization
118. ICLR 2024On the Hardness of Online Nonconvex Optimization with Single Oracle Feedback
119. ICLR 2024On the Parameterization of Second-Order Optimization Effective towards the Infinite Width
120. ICLR 2024Optimistic Bayesian Optimization with Unknown Constraints
121. ICLR 2024Relaxing the Additivity Constraints in Decentralized No-Regret High-Dimensional Bayesian Optimization
122. ICLR 2024Revisiting the Last-Iterate Convergence of Stochastic Gradient Methods
123. ICLR 2024Robust Model-Based Optimization for Challenging Fitness Landscapes
124. ICLR 2024Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion
125. ICLR 2024Unified Projection-Free Algorithms for Adversarial DR-Submodular Optimization
126. ICLR 2023A view of mini-batch SGD via generating functions: conditions of convergence, phase transitions, benefit from negative momenta.
127. ICLR 2023Accelerated Single-Call Methods for Constrained Min-Max Optimization
128. ICLR 2023Alternating Differentiation for Optimization Layers
129. ICLR 2023Approximate Vanishing Ideal Computations at Scale
130. ICLR 2023Asynchronous Distributed Bilevel Optimization
131. ICLR 2023BALTO: fast tensor program optimization with diversity-based active learning
132. ICLR 2023Behavior Proximal Policy Optimization
133. ICLR 2023Behind the Scenes of Gradient Descent: A Trajectory Analysis via Basis Function Decomposition
134. ICLR 2023Betty: An Automatic Differentiation Library for Multilevel Optimization
135. ICLR 2023Breaking Correlation Shift via Conditional Invariant Regularizer
136. ICLR 2023Depth Separation with Multilayer Mean-Field Networks
137. ICLR 2023Discovering Evolution Strategies via Meta-Black-Box Optimization
138. ICLR 2023Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality
139. ICLR 2023Distributionally Robust Recourse Action
140. ICLR 2023Equivariance-aware Architectural Optimization of Neural Networks
141. ICLR 2023Eva: Practical Second-order Optimization with Kronecker-vectorized Approximation
142. ICLR 2023Faster federated optimization under second-order similarity
143. ICLR 2023Fisher-Legendre (FishLeg) optimization of deep neural networks
144. ICLR 2023Globally Optimal Training of Neural Networks with Threshold Activation Functions
145. ICLR 2023Hyperparameter Optimization through Neural Network Partitioning
146. ICLR 2023Improving Object-centric Learning with Query Optimization
147. ICLR 2023Over-parameterized Model Optimization with Polyak-{\L}ojasiewicz Condition
148. ICLR 2023Parallel Deep Neural Networks Have Zero Duality Gap
149. ICLR 2023Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization
150. ICLR 2023Predictor-corrector algorithms for stochastic optimization under gradual distribution shift

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.