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

731 papers, page 2 of 15

51. NeurIPS 2026Newton-PINet: A fast physics-informed neural network with Newton linearization for meta-learning nonlinear PDEs
52. NeurIPS 2026Phaedra: Learning High-Fidelity Discrete Tokenization for the Physical Sciences
53. NeurIPS 2026Post-ADC Inference: Valid Inference After Active Data Collection
54. NeurIPS 2026Pref-DetectGPT: Unveiling Machine-Generated Text via Preference-Aware Curvature Measurement
55. NeurIPS 2026Querying Counterfactuals on Tissue Graphs with Supervised Disentanglement
56. NeurIPS 2026Regret-Based $(\epsilon,\delta)$-optimal Stopping Criteria for Bayesian Optimization
57. NeurIPS 2026Resilient Semi-Supervised Inference with Heterogeneous Unlabeled Data
58. NeurIPS 2026Rethinking Attention in Depth for Operator Learning
59. NeurIPS 2026Robust Surrogate Modeling for Explainable Graph Neural Networks
60. NeurIPS 2026Saddle-to-Saddle Dynamics in Self-Supervised Shortcut Learning
61. NeurIPS 2026Safe Linear Bandits with Unknown Safety Gaps
62. NeurIPS 2026Sample Efficient Generative Molecular Optimization with Joint Self-Improvement
63. NeurIPS 2026Seeing but Not Detecting: Privacy-Preserving Scene Text Attack via Hybrid Adversarial Policy Learning
64. NeurIPS 2026Self-Distillation of Hidden Layers for Masked Self-Supervised Representation Learning
65. NeurIPS 2026Self-Supervised Doppler-Guided RF Odometry
66. NeurIPS 2026SetAD: Semi-Supervised Anomaly Learning in Contextual Sets
67. NeurIPS 2026SIEVE: Overcoming Topological Obstruction in Equivariant Self-Supervised Learning
68. NeurIPS 2026Sparsely Supervised Diffusion
69. NeurIPS 2026SPDEBench: An Extensive Benchmark for Learning Stochastic PDEs
70. NeurIPS 2026SS3D: End2End Self-Supervised 3D from Web Videos
71. NeurIPS 2026StarWM: Self-Supervised Trained Attention Routing for Robust World Models
72. NeurIPS 2026Sup-Norm Error under Proportional Asymptotics: Phase Transitions under Linear Regression
73. NeurIPS 2026Surrogate Calibration for Transferable Adversarial Attacks against Black-Box MLLMs
74. NeurIPS 2026Testable Learning of General Halfspaces under Massart Noise
75. NeurIPS 2026The Graph Concept Bottleneck: Decoding Combinatorial Reasoning in GNNs for Interpretability
76. NeurIPS 2026The Platonic Defense: Backdoor Defense for Self-Supervised Encoders in the Era of Large Scale Pre-training
77. NeurIPS 2026Theory Guided and Interpretable Neural Operator Design for Partial Differential Equation Learning
78. NeurIPS 2026Topology-Aware Representation Alignment for Semi-Supervised Vision-Language Learning
79. NeurIPS 2026Train on the Sphere, Deploy on the Hill: Closed-Form-Anchored Surrogates for Real-Terrain Boundary-Integral Equations
80. NeurIPS 2026Trajectory-Matching Meta Pseudo-Labeling for Semi-Supervised Learning
81. NeurIPS 2026Transferable, Time-Parallel Graph Dynamics via Space-Time Factorization
82. NeurIPS 2026Tree-Structured Synergy of Large Language Models and Bayesian Optimization for Efficient CASH
83. NeurIPS 2026Tune-Up Open-Weight CLIP: Optimization Framework for Self-Supervised Fine-tuning of CLIP
84. NeurIPS 2026TurtLES: A Large-Scale Benchmark for Turbulent 3D Neural PDE Surrogates
85. NeurIPS 2026What to Perturb, How to Propagate: A Graph-Guided Transferable Attack on VLP Models
86. NeurIPS 2026Winning Lottery Tickets in Neural Networks via a Quantum-Inspired Classical Algorithm
87. NeurIPS 2026ZNO: Stable Rational Neural Operators in the Z-Domain for Discrete-Time Dynamics
88. NeurIPS 2025$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning
89. NeurIPS 2025$\epsilon$-Seg: Sparsely Supervised Semantic Segmentation of Microscopy Data
90. NeurIPS 20251000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities
91. NeurIPS 20253DID: Direct 3D Inverse Design for Aerodynamics with Physics-Aware Optimization
92. NeurIPS 202570% Size, 100% Accuracy: Lossless LLM Compression for Efficient GPU Inference via Dynamic-Length Float (DFloat11)
93. NeurIPS 2025A General-Purpose Theorem for High-Probability Bounds of Stochastic Approximation with Polyak Averaging
94. NeurIPS 2025Accelerating Model-Free Optimization via Averaging of Cost Samples
95. NeurIPS 2025AdaTS: Learning Adaptive Time Series Representations via Dynamic Soft Contrasts
96. NeurIPS 2025Advancing Interpretability of CLIP Representations with Concept Surrogate Model
97. NeurIPS 2025Adversarial Graph Fusion for Incomplete Multi-view Semi-supervised Learning with Tensorial Imputation
98. NeurIPS 2025AiDE-Q: Synthetic Labeled Datasets Can Enhance Learning Models for Quantum Property Estimation
99. NeurIPS 2025An Efficient Orlicz-Sobolev Approach for Transporting Unbalanced Measures on a Graph
100. NeurIPS 2025An Information-theoretical Framework for Understanding Out-of-distribution Detection with Pretrained Vision-Language Models

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.