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

191 papers, page 1 of 4

1. NeurIPS 2024$\boldsymbol{\mu}\mathbf{P^2}$: Effective Sharpness Aware Minimization Requires Layerwise Perturbation Scaling
2. NeurIPS 2024$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise
3. NeurIPS 20242DQuant: Low-bit Post-Training Quantization for Image Super-Resolution
4. NeurIPS 20244-bit Shampoo for Memory-Efficient Network Training
5. NeurIPS 2024A Bayesian Approach to Data Point Selection
6. NeurIPS 2024A Framework for Bilevel Optimization on Riemannian Manifolds
7. NeurIPS 2024A Layer-Wise Natural Gradient Optimizer for Training Deep Neural Networks
8. NeurIPS 2024A PID Controller Approach for Adaptive Probability-dependent Gradient Decay in Model Calibration
9. NeurIPS 2024A Primal-Dual-Assisted Penalty Approach to Bilevel Optimization with Coupled Constraints
10. NeurIPS 2024A Single-Step, Sharpness-Aware Minimization is All You Need to Achieve Efficient and Accurate Sparse Training
11. NeurIPS 2024Accelerating Matroid Optimization through Fast Imprecise Oracles
12. NeurIPS 2024Achieving Linear Convergence with Parameter-Free Algorithms in Decentralized Optimization
13. NeurIPS 2024Activation Map Compression through Tensor Decomposition for Deep Learning
14. NeurIPS 2024Adam with model exponential moving average is effective for nonconvex optimization
15. NeurIPS 2024ADOPT: Modified Adam Can Converge with Any $\beta_2$ with the Optimal Rate
16. NeurIPS 2024Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimization
17. NeurIPS 2024AlterMOMA: Fusion Redundancy Pruning for Camera-LiDAR Fusion Models with Alternative Modality Masking
18. NeurIPS 2024An Accelerated Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness
19. NeurIPS 2024An Accelerated Gradient Method for Convex Smooth Simple Bilevel Optimization
20. NeurIPS 2024Analyzing & Reducing the Need for Learning Rate Warmup in GPT Training
21. NeurIPS 2024BitDelta: Your Fine-Tune May Only Be Worth One Bit
22. NeurIPS 2024BLAST: Block-Level Adaptive Structured Matrices for Efficient Deep Neural Network Inference
23. NeurIPS 2024BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning
24. NeurIPS 2024Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment
25. NeurIPS 2024Can Learned Optimization Make Reinforcement Learning Less Difficult?
26. NeurIPS 2024Changing the Training Data Distribution to Reduce Simplicity Bias Improves In-distribution Generalization
27. NeurIPS 2024Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift
28. NeurIPS 2024Classifier-guided Gradient Modulation for Enhanced Multimodal Learning
29. NeurIPS 2024CoBo: Collaborative Learning via Bilevel Optimization
30. NeurIPS 2024CoMERA: Computing- and Memory-Efficient Training via Rank-Adaptive Tensor Optimization
31. NeurIPS 2024Communication-Efficient Federated Group Distributionally Robust Optimization
32. NeurIPS 2024Conformal Inverse Optimization
33. NeurIPS 2024Constrained Diffusion Models via Dual Training
34. NeurIPS 2024Contextual Linear Optimization with Bandit Feedback
35. NeurIPS 2024Continuous Temporal Domain Generalization
36. NeurIPS 2024Controlling Continuous Relaxation for Combinatorial Optimization
37. NeurIPS 2024CRAYM: Neural Field Optimization via Camera RAY Matching
38. NeurIPS 2024DASH: Warm-Starting Neural Network Training in Stationary Settings without Loss of Plasticity
39. NeurIPS 2024Decision-Focused Learning with Directional Gradients
40. NeurIPS 2024Deep linear networks for regression are implicitly regularized towards flat minima
41. NeurIPS 2024Derivatives of Stochastic Gradient Descent in parametric optimization
42. NeurIPS 2024Diffeomorphic interpolation for efficient persistence-based topological optimization
43. NeurIPS 2024DISP-LLM: Dimension-Independent Structural Pruning for Large Language Models
44. NeurIPS 2024Dual Risk Minimization: Towards Next-Level Robustness in Fine-tuning Zero-Shot Models
45. NeurIPS 2024Dual-Perspective Activation: Efficient Channel Denoising via Joint Forward-Backward Criterion for Artificial Neural Networks
46. NeurIPS 2024EDT: An Efficient Diffusion Transformer Framework Inspired by Human-like Sketching
47. NeurIPS 2024Efficient Adaptation of Pre-trained Vision Transformer via Householder Transformation
48. NeurIPS 2024Efficient Combinatorial Optimization via Heat Diffusion
49. NeurIPS 2024Efficient Prompt Optimization Through the Lens of Best Arm Identification
50. NeurIPS 2024Efficient Sign-Based Optimization: Accelerating Convergence via Variance Reduction

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.