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 4 of 4

151. NeurIPS 2026SemGeo-Gen: Unsupervised Generation of Approximate Cross-Instance Semantic-Geometric Correspondences
152. NeurIPS 2026Settling Pure Differentially Private Covariance Estimation
153. NeurIPS 2026ShiftRAG: Bypassing the Textual Bottleneck via Decoupled Learning and Continuous Soft Tokens
154. NeurIPS 2026Simulation-free Unbalanced Dynamic Optimal Transport with General Growth Penalty
155. NeurIPS 2026Sparse Koopman Autoencoders Identify Local Dynamical Regimes in Multibasin Systems
156. NeurIPS 2026Sparsifying Correlation Clustering: Edge Coresets, Triangle Witnesses, and Observation Lower Bounds
157. NeurIPS 2026SPATIALEPIBENCH: Benchmarking Spatial Information and Epidemic Priors in Forecasting
158. NeurIPS 2026State-Action Selection in General Stochastic Games under Regularized Policy Gradient Methods
159. NeurIPS 2026State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
160. NeurIPS 2026Subdata Selection: A Unified Framework for Optimal Selection and Statistical Efficiency Assessment
161. NeurIPS 2026Submodular Benchmark Selection
162. NeurIPS 2026Subspace-Guided Continual Learning: Hessian Based Stable–Plastic Decomposition for Exemplar-Free Class-Incremental Learning
163. NeurIPS 2026Symmetry Guarantees Statistic Recovery in Variational Inference
164. NeurIPS 2026Synthetic Pre-Pre-Training Improves Language Model Robustness to Noisy Pre-Training Data
165. NeurIPS 2026TabClustPFN: A Prior-Fitted Network for Tabular Data Clustering
166. NeurIPS 2026TabK: Amortized Bayesian Estimation of the Number of Clusters in Tabular Data
167. NeurIPS 2026TENET: Time-point Encoding Network for Multivariate Time Series Anomaly Detection
168. NeurIPS 2026Testable Learning of General Halfspaces under Massart Noise
169. NeurIPS 2026The BV4 Benchmark for Unsupervised Anomaly Detection in High-Dimensional Spectral Data Streams
170. NeurIPS 2026The parameters in weight-sparse transformers are interpretable
171. NeurIPS 2026The Query Complexity of Local Search in Rounds on General Graphs
172. NeurIPS 2026The Sample Complexity of Multiple Change Point Identification under Bandit Feedback
173. NeurIPS 2026The Sampling Complexity of Condorcet Winner Identification in Dueling Bandits
174. NeurIPS 2026TILT: Target-induced loss tilting under covariate shift
175. NeurIPS 2026ToMAToMP: Robust and Multi-Parameter Topological Clustering
176. NeurIPS 2026TopoPrune: Robust Data Pruning via Unified Latent Space Topology
177. NeurIPS 2026Towards Identifiable Latent Additive Noise Models
178. NeurIPS 2026Towards more general control of diffusion models using Jeffrey Guidance
179. NeurIPS 2026Towards Understanding the Power and Limits of the Muon Optimizer: A River-Valley Perspective
180. NeurIPS 2026Tracing Actual Causes with Counterfactual Witness Maps
181. NeurIPS 2026Tree Search With Predictions
182. NeurIPS 2026Trust Region Continual Learning as an Implicit Meta-Learner
183. NeurIPS 2026UGGRH: Unsupervised Generative Completion and Graph-attention Refinement for Incomplete Cross-modal Hashing
184. NeurIPS 2026UniRAP: Towards Unified Part-level Physical Affordance Reasoning and Actionable Perception
185. NeurIPS 2026Unpaired Canonical Correlation Analysis
186. NeurIPS 2026Unrolled gradients in disguise: bridging interpolation-based and Jacobian regularization for stable neural dynamics
187. NeurIPS 2026Unsupervised Concept Discovery with Dirichlet Concept Diffusion Models
188. NeurIPS 2026Virtual-Flow: Virtual Microphone-based Speech Enhancement via Unsupervised Flow Matching
189. NeurIPS 2026What the Geometry of Good Models Tells Us
190. NeurIPS 2026What Transformer FFNs Never See: Theory, Diagnosis, and Lightweight Remediation
191. NeurIPS 2026When and Why is Optimistic Multiplicative Weights Slow? The Geometry of Energy Dissipation
192. NeurIPS 2026Words That Make Language Models Perceive
193. NeurIPS 2026You Don’t Need Aligned Representations: Knowledge Distillation via Random Prototype Spaces
194. NeurIPS 2026Your Embedding Model Is SMARTer Than You Think

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.