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

500 papers, page 6 of 10

251. NeurIPS 2024Infinite Limits of Multi-head Transformer Dynamics
252. NeurIPS 2024Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?
253. NeurIPS 2024Jointly Modeling Inter- & Intra-Modality Dependencies for Multi-modal Learning
254. NeurIPS 2024Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization
255. NeurIPS 2024Learn more, but bother less: parameter efficient continual learning
256. NeurIPS 2024Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate
257. NeurIPS 2024Learning Generalized Linear Programming Value Functions
258. NeurIPS 2024Learning to Solve Quadratic Unconstrained Binary Optimization in a Classification Way
259. NeurIPS 2024Learning with Fitzpatrick Losses
260. NeurIPS 2024Learning-Augmented Dynamic Submodular Maximization
261. NeurIPS 2024LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch
262. NeurIPS 2024Linguistic Collapse: Neural Collapse in (Large) Language Models
263. NeurIPS 2024LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning
264. NeurIPS 2024Localized Zeroth-Order Prompt Optimization
265. NeurIPS 2024LoQT: Low-Rank Adapters for Quantized Pretraining
266. NeurIPS 2024LoRA-GA: Low-Rank Adaptation with Gradient Approximation
267. NeurIPS 2024Loss Landscape Characterization of Neural Networks without Over-Parametrization
268. NeurIPS 2024Low Precision Local Training is Enough for Federated Learning
269. NeurIPS 2024Make Continual Learning Stronger via C-Flat
270. NeurIPS 2024Matching the Statistical Query Lower Bound for $k$-Sparse Parity Problems with Sign Stochastic Gradient Descent
271. NeurIPS 2024Memory-Efficient Gradient Unrolling for Large-Scale Bi-level Optimization
272. NeurIPS 2024Memory-Efficient LLM Training with Online Subspace Descent
273. NeurIPS 2024MG-Net: Learn to Customize QAOA with Circuit Depth Awareness
274. NeurIPS 2024MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence
275. NeurIPS 2024Model Fusion through Bayesian Optimization in Language Model Fine-Tuning
276. NeurIPS 2024MOTE-NAS: Multi-Objective Training-based Estimate for Efficient Neural Architecture Search
277. NeurIPS 2024Navigating Extremes: Dynamic Sparsity in Large Output Spaces
278. NeurIPS 2024Nearly Minimax Optimal Submodular Maximization with Bandit Feedback
279. NeurIPS 2024Neur2BiLO: Neural Bilevel Optimization
280. NeurIPS 2024Neural Characteristic Activation Analysis and Geometric Parameterization for ReLU Networks
281. NeurIPS 2024Neural collapse vs. low-rank bias: Is deep neural collapse really optimal?
282. NeurIPS 2024Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms
283. NeurIPS 2024Non-geodesically-convex optimization in the Wasserstein space
284. NeurIPS 2024Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset
285. NeurIPS 2024Normalization Layer Per-Example Gradients are Sufficient to Predict Gradient Noise Scale in Transformers
286. NeurIPS 2024Nuclear Norm Regularization for Deep Learning
287. NeurIPS 2024Offline Behavior Distillation
288. NeurIPS 2024On Convergence of Adam for Stochastic Optimization under Relaxed Assumptions
289. NeurIPS 2024On the Inductive Bias of Stacking Towards Improving Reasoning
290. NeurIPS 2024Optimization-based Causal Estimation from Heterogeneous Environments
291. NeurIPS 2024Outlier-Robust Distributionally Robust Optimization via Unbalanced Optimal Transport
292. NeurIPS 2024PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularization
293. NeurIPS 2024Penalty-based Methods for Simple Bilevel Optimization under Hölderian Error Bounds
294. NeurIPS 2024Personalized Federated Learning via Feature Distribution Adaptation
295. NeurIPS 2024PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models
296. NeurIPS 2024Pre-trained Gaussian Processes for Bayesian Optimization
297. NeurIPS 2024Quantum Algorithms for Non-smooth Non-convex Optimization
298. NeurIPS 2024Recurrent neural networks: vanishing and exploding gradients are not the end of the story
299. NeurIPS 2024ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution
300. NeurIPS 2024Regularized Adaptive Momentum Dual Averaging with an Efficient Inexact Subproblem Solver for Training Structured Neural Network

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.