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 9 of 20

401. NeurIPS 2025TokMan:Tokenize Manhattan Mask Optimization for Inverse Lithography
402. NeurIPS 2025Uniform Wrappers: Bridging Concave to Quadratizable Functions in Online Optimization
403. NeurIPS 2025What Data Enables Optimal Decisions? An Exact Characterization for Linear Optimization
404. CVPR 2024Multi-Session SLAM with Differentiable Wide-Baseline Pose Optimization
405. ICLR 2024A Discretization Framework for Robust Contextual Stochastic Optimization
406. ICLR 2024A Precise Characterization of SGD Stability Using Loss Surface Geometry
407. ICLR 2024Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise
408. ICLR 2024Accelerating Sinkhorn algorithm with sparse Newton iterations
409. ICLR 2024Approximating Nash Equilibria in Normal-Form Games via Stochastic Optimization
410. ICLR 2024Bayesian Coreset Optimization for Personalized Federated Learning
411. ICLR 2024Communication-Efficient Federated Non-Linear Bandit Optimization
412. ICLR 2024Constraint-Free Structure Learning with Smooth Acyclic Orientations
413. ICLR 2024Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold
414. ICLR 2024Det-CGD: Compressed Gradient Descent with Matrix Stepsizes for Non-Convex Optimization
415. ICLR 2024Dropout Enhanced Bilevel Training
416. ICLR 2024Fast Equilibrium of SGD in Generic Situations
417. ICLR 2024FedDA: Faster Adaptive Gradient Methods for Federated Constrained Optimization
418. ICLR 2024Implicit Maximum a Posteriori Filtering via Adaptive Optimization
419. ICLR 2024Large Language Models to Enhance Bayesian Optimization
420. ICLR 2024Learning model uncertainty as variance-minimizing instance weights
421. ICLR 2024Linear attention is (maybe) all you need (to understand Transformer optimization)
422. ICLR 2024Lion Secretly Solves a Constrained Optimization: As Lyapunov Predicts
423. ICLR 2024Local Composite Saddle Point Optimization
424. ICLR 2024Magnitude Invariant Parametrizations Improve Hypernetwork Learning
425. ICLR 2024Most discriminative stimuli for functional cell type clustering
426. ICLR 2024Neur2RO: Neural Two-Stage Robust Optimization
427. ICLR 2024On the Hardness of Online Nonconvex Optimization with Single Oracle Feedback
428. ICLR 2024On the Parameterization of Second-Order Optimization Effective towards the Infinite Width
429. ICLR 2024Optimistic Bayesian Optimization with Unknown Constraints
430. ICLR 2024Relaxing the Additivity Constraints in Decentralized No-Regret High-Dimensional Bayesian Optimization
431. ICLR 2024Revisiting the Last-Iterate Convergence of Stochastic Gradient Methods
432. ICLR 2024Robust Model-Based Optimization for Challenging Fitness Landscapes
433. ICLR 2024Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion
434. ICLR 2024Unified Projection-Free Algorithms for Adversarial DR-Submodular Optimization
435. ICML 2024A Doubly Recursive Stochastic Compositional Gradient Descent Method for Federated Multi-Level Compositional Optimization
436. ICML 2024A Nearly Optimal Single Loop Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness
437. ICML 2024A New Theoretical Perspective on Data Heterogeneity in Federated Optimization
438. ICML 2024A Universal Transfer Theorem for Convex Optimization Algorithms Using Inexact First-order Oracles
439. ICML 2024Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need
440. ICML 2024Activation-Descent Regularization for Input Optimization of ReLU Networks
441. ICML 2024Advancing Dynamic Sparse Training by Exploring Optimization Opportunities
442. ICML 2024Autonomous Sparse Mean-CVaR Portfolio Optimization
443. ICML 2024Barrier Algorithms for Constrained Non-Convex Optimization
444. ICML 2024Bayesian Optimization of Function Networks with Partial Evaluations
445. ICML 2024Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints
446. ICML 2024Compact Optimality Verification for Optimization Proxies
447. ICML 2024Dealing With Unbounded Gradients in Stochastic Saddle-point Optimization
448. ICML 2024Decentralized Convex Finite-Sum Optimization with Better Dependence on Condition Numbers
449. ICML 2024Delving into the Convergence of Generalized Smooth Minimax Optimization
450. ICML 2024Differentiable Distributionally Robust Optimization Layers

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.