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 18 of 20

851. NeurIPS 2023Delayed Algorithms for Distributed Stochastic Weakly Convex Optimization
852. NeurIPS 2023Distributionally Robust Bayesian Optimization with $\varphi$-divergences
853. NeurIPS 2023Distributionally Robust Linear Quadratic Control
854. NeurIPS 2023Don't be so Monotone: Relaxing Stochastic Line Search in Over-Parameterized Models
855. NeurIPS 2023DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method
856. NeurIPS 2023DP-HyPO: An Adaptive Private Framework for Hyperparameter Optimization
857. NeurIPS 2023DRAUC: An Instance-wise Distributionally Robust AUC Optimization Framework
858. NeurIPS 2023Efficient Activation Function Optimization through Surrogate Modeling
859. NeurIPS 2023Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization
860. NeurIPS 2023Encoding Human Behavior in Information Design through Deep Learning
861. NeurIPS 2023Enhancing Sharpness-Aware Optimization Through Variance Suppression
862. NeurIPS 2023ExPT: Synthetic Pretraining for Few-Shot Experimental Design
863. NeurIPS 2023Faster Differentially Private Convex Optimization via Second-Order Methods
864. NeurIPS 2023Faster Discrete Convex Function Minimization with Predictions: The M-Convex Case
865. NeurIPS 2023Faster Margin Maximization Rates for Generic Optimization Methods
866. NeurIPS 2023Federated Compositional Deep AUC Maximization
867. NeurIPS 2023Federated Conditional Stochastic Optimization
868. NeurIPS 2023FedL2P: Federated Learning to Personalize
869. NeurIPS 2023FedNAR: Federated Optimization with Normalized Annealing Regularization
870. NeurIPS 2023Finding Local Minima Efficiently in Decentralized Optimization
871. NeurIPS 2023Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization
872. NeurIPS 2023First Order Stochastic Optimization with Oblivious Noise
873. NeurIPS 2023Fixing the NTK: From Neural Network Linearizations to Exact Convex Programs
874. NeurIPS 2023Fractal Landscapes in Policy Optimization
875. NeurIPS 2023Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization
876. NeurIPS 2023Global Identifiability of $\ell_1$-based Dictionary Learning via Matrix Volume Optimization
877. NeurIPS 2023GloptiNets: Scalable Non-Convex Optimization with Certificates
878. NeurIPS 2023Graph of Circuits with GNN for Exploring the Optimal Design Space
879. NeurIPS 2023Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses
880. NeurIPS 2023Hybrid Policy Optimization from Imperfect Demonstrations
881. NeurIPS 2023Hypervolume Maximization: A Geometric View of Pareto Set Learning
882. NeurIPS 2023Importance-aware Co-teaching for Offline Model-based Optimization
883. NeurIPS 2023Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures
884. NeurIPS 2023Incentivizing Honesty among Competitors in Collaborative Learning and Optimization
885. NeurIPS 2023Joint Prompt Optimization of Stacked LLMs using Variational Inference
886. NeurIPS 2023Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures
887. NeurIPS 2023L2T-DLN: Learning to Teach with Dynamic Loss Network
888. NeurIPS 2023Learning Cuts via Enumeration Oracles
889. NeurIPS 2023Learning the Efficient Frontier
890. NeurIPS 2023Learning to Configure Separators in Branch-and-Cut
891. NeurIPS 2023LEPARD: Learning Explicit Part Discovery for 3D Articulated Shape Reconstruction
892. NeurIPS 2023LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees
893. NeurIPS 2023Macro Placement by Wire-Mask-Guided Black-Box Optimization
894. NeurIPS 2023Making Scalable Meta Learning Practical
895. NeurIPS 2023Mechanic: A Learning Rate Tuner
896. NeurIPS 2023Memory-Constrained Algorithms for Convex Optimization
897. NeurIPS 2023MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning
898. NeurIPS 2023Momentum Provably Improves Error Feedback!
899. NeurIPS 2023Neural Fields with Hard Constraints of Arbitrary Differential Order
900. NeurIPS 2023Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian 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.