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

194 papers, page 3 of 4

101. NeurIPS 2026MemForest: Efficient Agent Memory Management via EventTree Partitioning and Progressive Merging
102. NeurIPS 2026Memorization Is Folding: Topological Signatures of Noisy-Label Learning
103. NeurIPS 2026Mini-batch kernel $k$-means
104. NeurIPS 2026Minimax Rates and Spectral Distillation for Tree Ensembles
105. NeurIPS 2026MinSteer: Minimal-Pair Steering via Two-Stage Cached Continuation
106. NeurIPS 2026Mirror descent actor-critic methods for entropy regularised MDPs in general spaces: stability and convergence
107. NeurIPS 2026MMSkills: Towards Multimodal Skills for General Visual Agents
108. NeurIPS 2026Model-Agnostic FDR Control via Group Gaussian Mirror and Permutation SHAP
109. NeurIPS 2026ModelLens: Finding the Best for Your Task from Myriads of Models
110. NeurIPS 2026MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image
111. NeurIPS 2026Network Learning with Semi-relaxed Gromov-Wasserstein
112. NeurIPS 2026Neural Backward Filtering Forward Guiding
113. NeurIPS 2026Not All Instances Should Contribute Equally: Sparse Support Grounding via Unbalanced Transport for Heterogeneous MIL
114. NeurIPS 2026ODEWorld: A Continuous Predictive Architecture via Physical-Time Flow
115. NeurIPS 2026Offline Constrained Reinforcement Learning under Partial Data Coverage
116. NeurIPS 2026Omni-Interactive Universal Embedder
117. NeurIPS 2026Omni-Safety under Cross-Modality Conflict: Vulnerabilities, Dynamic Mechanisms and Efficient Alignment
118. NeurIPS 2026On the Complexity of Offline Reinforcement Learning with Q*-Approximation and Partial Coverage
119. NeurIPS 2026On the Effect of Token Correlations on Semantic Transfer in Transformers
120. NeurIPS 2026On the Provable Emergence of Hierarchical Concept Structure in CLIP Embeddings
121. NeurIPS 2026One Unified Representation: Resolving the Appearance-Semantics Dilemma via Structural Regularization
122. NeurIPS 2026Online Maximization of Non-Decomposable Test and Population Utilities
123. NeurIPS 2026OpenWhistle: A Large-Scale Longitudinal Dataset and Benchmark of Bottlenose Dolphin Vocalizations
124. NeurIPS 2026Optimal and Efficient Contextual Combinatorial Semi-bandits with General Function Approximation
125. NeurIPS 2026Optimal Convergence Analysis of DDPM for General Distributions
126. NeurIPS 2026Optimal Hidden-Target Learning for Online Inventory Optimization on General Convex Sets
127. NeurIPS 2026PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting
128. NeurIPS 2026Pandora's AI Model Routing Box: Efficient Allocation with Costly Value Estimation
129. NeurIPS 2026Pattern-Based Matrix Analysis Under Permutations
130. NeurIPS 2026PENEX: AdaBoost-Inspired Neural Network Regularization
131. NeurIPS 2026PGSB: Pretrained-Guided Shared Basis for LoRA Model Merging
132. NeurIPS 2026Phase Transitions in Heavy-Tailed Mean Estimation under $\ell_p$ Norms
133. NeurIPS 2026Platonic Representations in the Human Brain: Unsupervised Recovery of Universal Geometry
134. NeurIPS 2026Platonic Task Arithmetic
135. NeurIPS 2026PM-LoRA: Scalable Continual Learning via Progressive Merging of Low Rank Adapters
136. NeurIPS 2026Prediction-Intervention Games and Invariant Sets
137. NeurIPS 2026PRISM: Phenotype-Resolved Inference in Single-Cell Mixed Models via Latent Disease States and Contextualized Differential Expression
138. NeurIPS 2026PRISMIC: Reconstructing User Preference via Intent Decomposition and Consolidation
139. NeurIPS 2026Progressive Pseudo-label Self-balancing Towards Unsupervised Vision-Language Models Adaptation
140. NeurIPS 2026Proximal Difference-in-Differences for Long-Term Causal Learning under Confounding and Outcome Drift
141. NeurIPS 2026RAPTOR: Ridge-Adaptive Logistic Probes
142. NeurIPS 2026Reading Between the Dots: Decoding Hidden Computation across Filler Tokens
143. NeurIPS 2026Reliable Clustering and Quantization via Distortion-Constrained Optimal Transport
144. NeurIPS 2026Rethinking Sequential Locate-Then-Edit: Optimality and Stability
145. NeurIPS 2026Revisiting Subgradient Dominance in Robust MDPs: Counterexamples, Hardness, and Sufficient Conditions
146. NeurIPS 2026Root Cause Analysis of Measurement and Mechanistic Anomalies
147. NeurIPS 2026S2D: Sparse-To-Dense Keymask Distillation for Unsupervised Video Instance Segmentation
148. NeurIPS 2026S²MoE: Shared-Subspace Mixture of Sparse Experts
149. NeurIPS 2026Second-Order Complexity Theory for Risk, Explanation, and Calibration in Machine Learning
150. NeurIPS 2026Self-Recognition Finetuning can Reverse and Prevent Emergent Misalignment

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.