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

144 papers, page 3 of 3

101. NeurIPS 2023Parallel Submodular Function Minimization
102. NeurIPS 2023Parallel-mentoring for Offline Model-based Optimization
103. NeurIPS 2023Path following algorithms for $\ell_2$-regularized $M$-estimation with approximation guarantee
104. NeurIPS 2023Path Regularization: A Convexity and Sparsity Inducing Regularization for Parallel ReLU Networks
105. NeurIPS 2023Payoff-based Learning with Matrix Multiplicative Weights in Quantum Games
106. NeurIPS 2023Percentile Criterion Optimization in Offline Reinforcement Learning
107. NeurIPS 2023Phase diagram of early training dynamics in deep neural networks: effect of the learning rate, depth, and width
108. NeurIPS 2023Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control
109. NeurIPS 2023Practical Sharpness-Aware Minimization Cannot Converge All the Way to Optima
110. NeurIPS 2023Preconditioning Matters: Fast Global Convergence of Non-convex Matrix Factorization via Scaled Gradient Descent
111. NeurIPS 2023Predict-then-Calibrate: A New Perspective of Robust Contextual LP
112. NeurIPS 2023PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning
113. NeurIPS 2023Projection-Free Online Convex Optimization via Efficient Newton Iterations
114. NeurIPS 2023QuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator Learning
115. NeurIPS 2023Quantum Bayesian Optimization
116. NeurIPS 2023Quantum speedups for stochastic optimization
117. NeurIPS 2023Reduced Policy Optimization for Continuous Control with Hard Constraints
118. NeurIPS 2023Regret Minimization via Saddle Point Optimization
119. NeurIPS 2023Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressions
120. NeurIPS 2023Resetting the Optimizer in Deep RL: An Empirical Study
121. NeurIPS 2023ReSync: Riemannian Subgradient-based Robust Rotation Synchronization
122. NeurIPS 2023Revisiting Implicit Differentiation for Learning Problems in Optimal Control
123. NeurIPS 2023Reward Scale Robustness for Proximal Policy Optimization via DreamerV3 Tricks
124. NeurIPS 2023Riemannian SAM: Sharpness-Aware Minimization on Riemannian Manifolds
125. NeurIPS 2023Riemannian stochastic optimization methods avoid strict saddle points
126. NeurIPS 2023Robust Learning for Smoothed Online Convex Optimization with Feedback Delay
127. NeurIPS 2023Sampling weights of deep neural networks
128. NeurIPS 2023Self-Correcting Bayesian Optimization through Bayesian Active Learning
129. NeurIPS 2023SHOT: Suppressing the Hessian along the Optimization Trajectory for Gradient-Based Meta-Learning
130. NeurIPS 2023Sketchy: Memory-efficient Adaptive Regularization with Frequent Directions
131. NeurIPS 2023SLM: A Smoothed First-Order Lagrangian Method for Structured Constrained Nonconvex Optimization
132. NeurIPS 2023Solving a Class of Non-Convex Minimax Optimization in Federated Learning
133. NeurIPS 2023SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data
134. NeurIPS 2023State Regularized Policy Optimization on Data with Dynamics Shift
135. NeurIPS 2023Stochastic Approximation Approaches to Group Distributionally Robust Optimization
136. NeurIPS 2023TD Convergence: An Optimization Perspective
137. NeurIPS 2023Towards Characterizing the First-order Query Complexity of Learning (Approximate) Nash Equilibria in Zero-sum Matrix Games
138. NeurIPS 2023Two-Stage Predict+Optimize for MILPs with Unknown Parameters in Constraints
139. NeurIPS 2023Understanding Contrastive Learning via Distributionally Robust Optimization
140. NeurIPS 2023Universal Gradient Descent Ascent Method for Nonconvex-Nonconcave Minimax Optimization
141. NeurIPS 2023Unlocking Feature Visualization for Deep Network with MAgnitude Constrained Optimization
142. NeurIPS 2023Weitzman's Rule for Pandora's Box with Correlations
143. NeurIPS 2023When Does Optimizing a Proper Loss Yield Calibration?
144. NeurIPS 2023Winner Takes It All: Training Performant RL Populations for Combinatorial 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.