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 3 of 6

101. ICML 2025Fast, Accurate Manifold Denoising by Tunneling Riemannian Optimization
102. ICML 2025Feature-Mapping Topology Optimization with Neural Heaviside Signed Distance Functions
103. ICML 2025FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch
104. ICML 2025Generalized additive models via direct optimization of regularized decision stump forests
105. ICML 2025Generalized Smooth Bilevel Optimization with Nonconvex Lower-Level
106. ICML 2025Global curvature for second-order optimization of neural networks
107. ICML 2025Improved Last-Iterate Convergence of Shuffling Gradient Methods for Nonsmooth Convex Optimization
108. ICML 2025Incremental Gradient Descent with Small Epoch Counts is Surprisingly Slow on Ill-Conditioned Problems
109. ICML 2025Inverse Optimization via Learning Feasible Regions
110. ICML 2025Joker: Joint Optimization Framework for Lightweight Kernel Machines
111. ICML 2025Learning Minimum-Size BDDs: Towards Efficient Exact Algorithms
112. ICML 2025Meta-Black-Box-Optimization through Offline Q-function Learning
113. ICML 2025Natural Perturbations for Black-box Training of Neural Networks by Zeroth-Order Optimization
114. ICML 2025Neural Solver Selection for Combinatorial Optimization
115. ICML 2025Nonlinearly Preconditioned Gradient Methods under Generalized Smoothness
116. ICML 2025Optimal Transport Barycenter via Nonconvex-Concave Minimax Optimization
117. ICML 2025Optimization over Sparse Support-Preserving Sets: Two-Step Projection with Global Optimality Guarantees
118. ICML 2025PipeOffload: Improving Scalability of Pipeline Parallelism with Memory Optimization
119. ICML 2025Probabilistic Group Mask Guided Discrete Optimization for Incremental Learning
120. ICML 2025Provable Benefit of Random Permutations over Uniform Sampling in Stochastic Coordinate Descent
121. ICML 2025Radio: Rate–Distortion Optimization for Large Language Model Compression
122. ICML 2025Revisiting Convergence: Shuffling Complexity Beyond Lipschitz Smoothness
123. ICML 2025Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up
124. ICML 2025Scalable Meta-Learning via Mixed-Mode Differentiation
125. ICML 2025SERENA: A Unified Stochastic Recursive Variance Reduced Gradient Framework for Riemannian Non-Convex Optimization
126. ICML 2025Stacey: Promoting Stochastic Steepest Descent via Accelerated $\ell_p$-Smooth Nonconvex Optimization
127. ICML 2025Stochastic Smoothed Primal-Dual Algorithms for Nonconvex Optimization with Linear Inequality Constraints
128. ICML 2025Subspace Optimization for Large Language Models with Convergence Guarantees
129. ICML 2025The Sharpness Disparity Principle in Transformers for Accelerating Language Model Pre-Training
130. ICML 2025TSP: A Two-Sided Smoothed Primal-Dual Method for Nonconvex Bilevel Optimization
131. ICML 2024A Doubly Recursive Stochastic Compositional Gradient Descent Method for Federated Multi-Level Compositional Optimization
132. ICML 2024A Nearly Optimal Single Loop Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness
133. ICML 2024A New Theoretical Perspective on Data Heterogeneity in Federated Optimization
134. ICML 2024A Universal Transfer Theorem for Convex Optimization Algorithms Using Inexact First-order Oracles
135. ICML 2024Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need
136. ICML 2024Activation-Descent Regularization for Input Optimization of ReLU Networks
137. ICML 2024Advancing Dynamic Sparse Training by Exploring Optimization Opportunities
138. ICML 2024Autonomous Sparse Mean-CVaR Portfolio Optimization
139. ICML 2024Barrier Algorithms for Constrained Non-Convex Optimization
140. ICML 2024Bayesian Optimization of Function Networks with Partial Evaluations
141. ICML 2024Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints
142. ICML 2024Compact Optimality Verification for Optimization Proxies
143. ICML 2024Dealing With Unbounded Gradients in Stochastic Saddle-point Optimization
144. ICML 2024Decentralized Convex Finite-Sum Optimization with Better Dependence on Condition Numbers
145. ICML 2024Delving into the Convergence of Generalized Smooth Minimax Optimization
146. ICML 2024Differentiable Distributionally Robust Optimization Layers
147. ICML 2024Differentiable Mapper for Topological Optimization of Data Representation
148. ICML 2024Distributed Bilevel Optimization with Communication Compression
149. ICML 2024Distribution Alignment Optimization through Neural Collapse for Long-tailed Classification
150. ICML 2024Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient

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.