General ML · supervised learning papers, 2023–2026

Key findings

• General ML · supervised learning went from 263 papers in 2023 to 279 in 2026, roughly 1.1x in three years.
• NeurIPS publishes the most General ML · supervised learning work: 731 papers from 2023 to 2026, 51% of the four-venue total.
• The fastest-growing overlap is with Diffusion Models: papers tagged with both rose from 2 in 2023 to 9 in 2026.
• Among the 6 topics under General Machine Learning, it ranks #3 by 2026 paper count; the fastest grower in the group, General ML · evaluation, is up 4.3x versus 1.1x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026AOT-POT: Adaptive Operator Transformation for Large-Scale PDE Pre-training
• ICML 2026$\mathbb{R}^{2k}$ is Theoretically Large Enough for Embedding-based Top-$k$ Retrieval
• ICLR 2026Adaptive Concept Discovery for Interpretable Few-Shot Text Classification
• CVPR 2026Enabling Supervised Learning of Generative Signatures for Generalized AI-Generated Images
• NeurIPS 2025$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning
• ICML 2025Ab Initio Nonparametric Variable Selection for Scalable Symbolic Regression with Large $p$
• ICLR 2025Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximations
• CVPR 2025A Unified Framework for Heterogeneous Semi-supervised Learning
• NeurIPS 2024(FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning
• ICML 2024A New Branch-and-Bound Pruning Framework for $\ell_0$-Regularized Problems
• ICLR 2024A Study of Bayesian Neural Network Surrogates for Bayesian Optimization
• CVPR 2024Efficient Hyperparameter Optimization with Adaptive Fidelity Identification
• NeurIPS 2023A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning
• ICML 2023A Model-free Closeness-of-influence Test for Features in Supervised Learning
• ICLR 2023Deep Ranking Ensembles for Hyperparameter Optimization
• CVPR 2023Boosting Semi-Supervised Learning by Exploiting All Unlabeled Data
• NeurIPS 2026Approximation of Maximally Monotone Operators : A Graph Convergence Perspective
• ICML 2026Active Budget Allocation for Efficient Scaling Law Estimation via Surrogate-Guided Pruning
• ICLR 2026Buckingham $\pi$-Invariant Test‑Time Projection for Robust PDE Surrogate Modeling
• CVPR 2026HyperNAS Enhancing Architecture Representation for NAS Predictor via Hypernetwork

All General ML · supervised learning papers

171 papers, page 2 of 4

51. NeurIPS 2025EngiBench: A Framework for Data-Driven Engineering Design Research
52. NeurIPS 2025Enhancing Contrastive Learning with Variable Similarity
53. NeurIPS 2025Enhancing Privacy in Multimodal Federated Learning with Information Theory
54. NeurIPS 2025ENMA: Tokenwise Autoregression for Continuous Neural PDE Operators
55. NeurIPS 2025Evaluating LLM-contaminated Crowdsourcing Data Without Ground Truth
56. NeurIPS 2025Exploring and Exploiting Model Uncertainty in Bayesian Optimization
57. NeurIPS 2025Exploring Structural Degradation in Dense Representations for Self-supervised Learning
58. NeurIPS 2025Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning
59. NeurIPS 2025FedRAM: Federated Reweighting and Aggregation for Multi-Task Learning
60. NeurIPS 2025Flick: Empowering Federated Learning with Commonsense Knowledge
61. NeurIPS 2025Fourier Clouds: Fast Bias Correction for Imbalanced Semi-Supervised Learning
62. NeurIPS 2025From Replication to Redesign: Exploring Pairwise Comparisons for LLM-Based Peer Review
63. NeurIPS 2025From Sequence to Structure: Uncovering Substructure Reasoning in Transformers
64. NeurIPS 2025From Synapses to Dynamics: Obtaining Function from Structure in a Connectome Constrained Model of the Head Direction Circuit
65. NeurIPS 2025Functional Scaling Laws in Kernel Regression: Loss Dynamics and Learning Rate Schedules
66. NeurIPS 2025Functional Virtual Adversarial Training for Semi-Supervised Time Series Classification
67. NeurIPS 2025Gaussian Approximation and Concentration of Constant Learning-Rate Stochastic Gradient Descent
68. NeurIPS 2025Generating and Checking DNN Verification Proofs
69. NeurIPS 2025Geometric Algorithms for Neural Combinatorial Optimization with Constraints
70. NeurIPS 2025Geometry Aware Operator Transformer as an efficient and accurate neural surrogate for PDEs on arbitrary domains
71. NeurIPS 2025GUI-G1: Understanding R1-Zero-Like Training for Visual Grounding in GUI Agents
72. NeurIPS 2025Hamiltonian Neural PDE Solvers through Functional Approximation
73. NeurIPS 2025Hybrid Autoencoders for Tabular Data: Leveraging Model-Based Augmentation in Low-Label Settings
74. NeurIPS 2025Improved Scaling Laws in Linear Regression via Data Reuse
75. NeurIPS 2025IneqSearch: Hybrid Reasoning for Olympiad Inequality Proofs
76. NeurIPS 2025Infinite Neural Operators: Gaussian processes on functions
77. NeurIPS 2025Influence Guided Context Selection for Effective Retrieval-Augmented Generation
78. NeurIPS 2025Informed Initialization for Bayesian Optimization and Active Learning
79. NeurIPS 2025ItDPDM: Information-Theoretic Discrete Poisson Diffusion Model
80. NeurIPS 2025Jamais Vu: Exposing the Generalization Gap in Supervised Semantic Correspondence
81. NeurIPS 2025KaRF: Weakly-Supervised Kolmogorov-Arnold Networks-based Radiance Fields for Local Color Editing
82. NeurIPS 2025Keep It on a Leash: Controllable Pseudo-label Generation Towards Realistic Long-Tailed Semi-Supervised Learning
83. NeurIPS 2025Learning Individual Behavior in Agent-Based Models with Graph Diffusion Networks
84. NeurIPS 2025Learning Shared Representations from Unpaired Data
85. NeurIPS 2025Learning Stochastic Multiscale Models
86. NeurIPS 2025Learning to Flow from Generative Pretext Tasks for Neural Architecture Encoding
87. NeurIPS 2025Light-Weight Diffusion Multiplier and Uncertainty Quantification for Fourier Neural Operators
88. NeurIPS 2025Meta Guidance: Incorporating Inductive Biases into Deep Time Series Imputers
89. NeurIPS 2025MIND: Material Interface Generation from UDFs for Non-Manifold Surface Reconstruction
90. NeurIPS 2025Mitigating Spurious Features in Contrastive Learning with Spectral Regularization
91. NeurIPS 2025Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning
92. NeurIPS 2025Model Reconciliation via Cost-Optimal Explanations in Probabilistic Logic Programming
93. NeurIPS 2025Monotone and Separable Set Functions: Characterizations and Neural Models
94. NeurIPS 2025More Than Just Functional: LLM-as-a-Critique for Efficient Code Generation
95. NeurIPS 2025Negative Feedback Really Matters: Signed Dual-Channel Graph Contrastive Learning Framework for Recommendation
96. NeurIPS 2025Neurons as Detectors of Coherent Sets in Sensory Dynamics
97. NeurIPS 2025NoBOOM: Chemical Process Datasets for Industrial Anomaly Detection
98. NeurIPS 2025Non-Adaptive Adversarial Face Generation
99. NeurIPS 2025OctoNet: A Large-Scale Multi-Modal Dataset for Human Activity Understanding Grounded in Motion-Captured 3D Pose Labels
100. NeurIPS 2025On Agnostic PAC Learning in the Small Error Regime

FAQ

How many General ML · supervised learning papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

1,425 in total: 263 in 2023, 511 in 2024, 372 in 2025, 279 in 2026.

How many General ML · supervised learning papers are at NeurIPS 2026?

NeurIPS 2026 has 87 General ML · supervised learning papers, versus 171 the year before.

How many General ML · supervised learning papers are at ICLR 2026?

ICLR 2026 has 48 General ML · supervised learning papers, versus 40 the year before.

How many General ML · supervised learning papers are at ICML 2026?

ICML 2026 has 111 General ML · supervised learning papers, versus 114 the year before.

How many General ML · supervised learning papers are at CVPR 2026?

CVPR 2026 has 33 General ML · supervised learning papers, versus 47 the year before.

Which conference publishes the most General ML · supervised learning papers?

NeurIPS, with 731 papers from 2023 to 2026.