General ML · unsupervised and semisupervised learning papers, 2023–2026

Key findings

• General ML · unsupervised and semisupervised learning went from 649 papers in 2023 to 799 in 2026, roughly 1.2x in three years.
• ICML publishes the most General ML · unsupervised and semisupervised learning work: 859 papers from 2023 to 2026, 32% of the four-venue total.
• The fastest-growing overlap is with Deep learning · self supervised learning: papers tagged with both rose from 23 in 2023 to 86 in 2026.
• Among the 6 topics under General Machine Learning, it ranks #1 by 2026 paper count; the fastest grower in the group, General ML · evaluation, is up 4.3x versus 1.2x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A general kernel framework for non-CND distance measures using $|\mathcal{D}|$-dimensional sparse landmark embeddings
• ICML 2026$\mu$pscaling small models: Principled warm starts and hyperparameter transfer
• ICLR 2026A Generalized Geometric Theoretical Framework of Centroid Discriminant Analysis for Linear Classification of Multi-dimensional Data
• CVPR 2026COG Confidence-aware Optimal Geometric Correspondence for Unsupervised Single-reference Novel Object
• NeurIPS 2025$O(\sqrt{T})$ Static Regret and Instance Dependent Constraint Violation for Constrained Online Convex Optimization
• ICML 2025A Square Peg in a Square Hole: Meta-Expert for Long-Tailed Semi-Supervised Learning
• ICLR 2025A General Framework for Producing Interpretable Semantic Text Embeddings
• CVPR 2025A General Adaptive Dual-level Weighting Mechanism for Remote Sensing Pansharpening
• NeurIPS 2024GlotCC: An Open Broad-Coverage CommonCrawl Corpus and Pipeline for Minority Languages
• ICML 2024CKGConv: General Graph Convolution with Continuous Kernels
• ICLR 2024A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
• CVPR 2024Dual Prototype Attention for Unsupervised Video Object Segmentation
• NeurIPS 2023$H$-Consistency Bounds: Characterization and Extensions
• ICML 2023A Mathematical Model for Curriculum Learning for Parities
• ICLR 2023A General Framework For Proving The Equivariant Strong Lottery Ticket Hypothesis
• CVPR 2023A General Regret Bound of Preconditioned Gradient Method for DNN Training
• NeurIPS 2026Attention-based PCA
• ICML 2026A Control-Theoretic View of Mamba on Stability and Robustness
• ICLR 2026A Single Architecture for Representing Invariance Under Any Space Group
• CVPR 2026CROWn A Unified Framework for Anti-Aliased Downsampling and Phase-Calibrated Fusion

All General ML · unsupervised and semisupervised learning papers

203 papers, page 2 of 5

51. NeurIPS 2025DGCBench: A Deep Graph Clustering Benchmark
52. NeurIPS 2025Differentiable Generalized Sliced Wasserstein Plans
53. NeurIPS 2025Differentiable Structure Learning and Causal Discovery for General Binary Data
54. NeurIPS 2025Disentangled Representation Learning via Modular Compositional Bias
55. NeurIPS 2025Diversity Is All You Need for Contrastive Learning: Spectral Bounds on Gradient Magnitudes
56. NeurIPS 2025Dual-Space Semantic Synergy Distillation for Continual Learning of Unlabeled Streams
57. NeurIPS 2025DUET: Dual-Perspective Pseudo Labeling and Uncertainty-aware Exploration & Exploitation Training for Source-Free Domain Adaptation
58. NeurIPS 2025Efficient $k$-Sparse Band–Limited Interpolation with Improved Approximation Ratio
59. NeurIPS 2025Efficient and Near-Optimal Algorithm for Contextual Dueling Bandits with Offline Regression Oracles
60. NeurIPS 2025Efficient Representativeness-Aware Coreset Selection
61. NeurIPS 2025Energy-based generator matching: A neural sampler for general state space
62. NeurIPS 2025Enhancing Contrastive Learning with Variable Similarity
63. NeurIPS 2025Enhancing Deep Batch Active Learning for Regression with Imperfect Data Guided Selection
64. NeurIPS 2025Enhancing Sample Selection Against Label Noise by Cutting Mislabeled Easy Examples
65. NeurIPS 2025Entropy-Calibrated Label Distribution Learning
66. NeurIPS 2025Estimating Model Performance Under Covariate Shift Without Labels
67. NeurIPS 2025EUGens: Efficient, Unified and General Dense Layers
68. NeurIPS 2025Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death
69. NeurIPS 2025FACE: A General Framework for Mapping Collaborative Filtering Embeddings into LLM Tokens
70. NeurIPS 2025FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation
71. NeurIPS 2025Fast MRI for All: Bridging Access Gaps by Training without Raw Data
72. NeurIPS 2025Fast Training of Large Kernel Models with Delayed Projections
73. NeurIPS 2025FedQS: Optimizing Gradient and Model Aggregation for Semi-Asynchronous Federated Learning
74. NeurIPS 2025Flatness is Necessary, Neural Collapse is Not: Rethinking Generalization via Grokking
75. NeurIPS 2025Flux4D: Flow-based Unsupervised 4D Reconstruction
76. NeurIPS 2025Fréchet Geodesic Boosting
77. NeurIPS 2025Generalization Guarantees for Learning Score-Based Branch-and-Cut Policies in Integer Programming
78. NeurIPS 2025Generalizing while preserving monotonicity in comparison-based preference learning models
79. NeurIPS 2025GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers
80. NeurIPS 2025Graph Few-Shot Learning via Adaptive Spectrum Experts and Cross-Set Distribution Calibration
81. NeurIPS 2025Graph–Smoothed Bayesian Black-Box Shift Estimator and Its Information Geometry
82. NeurIPS 2025Hierarchical Retrieval: The Geometry and a Pretrain-Finetune Recipe
83. NeurIPS 2025How many measurements are enough? Bayesian recovery in inverse problems with general distributions
84. NeurIPS 2025Hybrid-Collaborative Augmentation and Contrastive Sample Adaptive-Differential Awareness for Robust Attributed Graph Clustering
85. NeurIPS 2025IA-GGAD: Zero-shot Generalist Graph Anomaly Detection via Invariant and Affinity Learning
86. NeurIPS 2025Imbalances in Neurosymbolic Learning: Characterization and Mitigating Strategies
87. NeurIPS 2025Implicit Bias of Spectral Descent and Muon on Multiclass Separable Data
88. NeurIPS 2025Improved Algorithms for Overlapping and Robust Clustering of Edge-Colored Hypergraphs: An LP-Based Combinatorial Approach
89. NeurIPS 2025Improved Confidence Regions and Optimal Algorithms for Online and Offline Linear MNL Bandits
90. NeurIPS 2025Improving Regret Approximation for Unsupervised Dynamic Environment Generation
91. NeurIPS 2025Improving Task-Specific Multimodal Sentiment Analysis with General MLLMs via Prompting
92. NeurIPS 2025Incentivizing Desirable Effort Profiles in Strategic Classification: The Role of Causality and Uncertainty
93. NeurIPS 2025Incomplete Multi-view Clustering via Hierarchical Semantic Alignment and Cooperative Completion
94. NeurIPS 2025Incomplete Multi-view Deep Clustering with Data Imputation and Alignment
95. NeurIPS 2025Interactive and Hybrid Imitation Learning: Provably Beating Behavior Cloning
96. NeurIPS 2025Joint Hierarchical Representation Learning of Samples and Features via Informed Tree-Wasserstein Distance
97. NeurIPS 2025KAIROS: Scalable Model-Agnostic Data Valuation
98. NeurIPS 2025KSP: Kolmogorov-Smirnov metric-based Post-Hoc Calibration for Survival Analysis
99. NeurIPS 2025Latent Zoning Network: A Unified Principle for Generative Modeling, Representation Learning, and Classification
100. NeurIPS 2025Learning single index models via harmonic decomposition

FAQ

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

2,683 in total: 649 in 2023, 553 in 2024, 682 in 2025, 799 in 2026.

How many General ML · unsupervised and semisupervised learning papers are at NeurIPS 2026?

NeurIPS 2026 has 194 General ML · unsupervised and semisupervised learning papers, versus 203 the year before.

How many General ML · unsupervised and semisupervised learning papers are at ICLR 2026?

ICLR 2026 has 187 General ML · unsupervised and semisupervised learning papers, versus 174 the year before.

How many General ML · unsupervised and semisupervised learning papers are at ICML 2026?

ICML 2026 has 348 General ML · unsupervised and semisupervised learning papers, versus 239 the year before.

How many General ML · unsupervised and semisupervised learning papers are at CVPR 2026?

CVPR 2026 has 70 General ML · unsupervised and semisupervised learning papers, versus 66 the year before.

Which conference publishes the most General ML · unsupervised and semisupervised learning papers?

ICML, with 859 papers from 2023 to 2026.