通用机器学习 · unsupervised and semisupervised learning论文趋势 2023–2026

主要结论

• 通用机器学习 · unsupervised and semisupervised learning论文从 2023 年的 649 篇增长到 2026 年的 799 篇，三年间扩大到约 1.2 倍。
• ICML 是发通用机器学习 · unsupervised and semisupervised learning论文最多的会议，2023–2026 年共 859 篇，占四个会议总量的 32%。
• 交叉方向里涨得最快的是深度学习 · self supervised learning：同时属于两个主题的论文从 2023 年的 23 篇增至 2026 年的 86 篇。
• 在「通用机器学习」的 6 个主题里，它的 2026 年论文数排第 1；同组增长最快的通用机器学习 · evaluation是 4.3 倍，通用机器学习 · unsupervised and semisupervised learning是 1.2 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• 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

全部通用机器学习 · unsupervised and semisupervised learning论文

共 859 篇，第 4 / 18 页

151. ICML 2026GFD-EMVC: Evolutionary Multi-View Classification with Label Noise via Gradient and Feature Dual-Perception
152. ICML 2026gp2Scale: A Class of Compactly Supported Non-Stationary Kernels and Distributed Computing for Exact Gaussian Processes on 10 Million Data Points
153. ICML 2026Gradient Flow Through Diagram Expansions: Learning Regimes and Explicit Solutions
154. ICML 2026Gradient Transformer: Learning to Generate Updates for LLMs
155. ICML 2026GraphFLEx: Unsupervised Structure Learning $\underline{\text{F}}$ramework for $\underline{\text{L}}$arge $\underline{\text{Ex}}$panding $\underline{\text{Graph}}$s
156. ICML 2026How can embedding models bind concepts?
157. ICML 2026How High is ‘High’? Rethinking the Roles of Dimensionality in Topological Data Analysis and Manifold Learning
158. ICML 2026Identifiable Markov Switching Models with Instantaneous Effects and Exponential Families
159. ICML 2026Identifiable Nonlinear Differentiable Causal Discovery via Independence and Adaptive Group Sparsity
160. ICML 2026Identifying Latent Concepts and Structures for Generalized Category Discovery
161. ICML 2026Incomplete Multi-View Clustering via Neighborhood-Conditioned Diffusion
162. ICML 2026Independent Component Discovery in Temporal Count Data
163. ICML 2026Inference from Quantized Data via Normal Variance-Mean Mixtures
164. ICML 2026Is Graph Mixup Beneficial? Investigating Interpolation And Empirical Performance of Graph Mixup Methods
165. ICML 2026KANFIS: A Neuro-Symbolic Framework for Interpretable and Uncertainty-Aware Learning
166. ICML 2026Kernel-based Maximum-of-difference Test for Two-sample Comparison
167. ICML 2026KODA: Contrastive Representation Comparison and Alignment for Vision-Language Foundation Models
168. ICML 2026Language Model Augmented Semi-Supervised Statistical Inference
169. ICML 2026LaRA-Fusion: Latent-Robust Adaptation via Dual-Loop Constraints for Infrared and Visible Image Fusion
170. ICML 2026Large Scale Manifold Balanced Clustering
171. ICML 2026Learning Flexible Generalization in Video Quality Assessment by Bringing Device and Viewing Condition Distributions
172. ICML 2026Learning General Causal Structures with Hidden Dynamic Process for Climate Analysis
173. ICML 2026Learning Generalizable Skill Policy with Data-Efficient Unsupervised RL
174. ICML 2026Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach
175. ICML 2026Learning to Label: A Reinforced Self-Evolving Framework for Semi-supervised Referring Expression Segmentation
176. ICML 2026LEGO-FL: Learning Heterogeneous Federated Models as a LEGO Assembly Games
177. ICML 2026LEMUR: Learned Multi-Vector Retrieval
178. ICML 2026Less Is More in Federated Continual Learning: RieSelect for Conflict-Aware Layer Selection in LLMs
179. ICML 2026Lightweight Federated Incremental Learning via Decoupled Replay
180. ICML 2026Likelihood over Estimation: Robust Quadratic Discriminant Analysis for Heavy-Tailed Distributions with Theory and Evidence
181. ICML 2026Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement
182. ICML 2026Lions and Muons: Optimization via Stochastic Frank-Wolfe under Heavy-Tailed Noises
183. ICML 2026Local Hessian Spectral Filtering for Robust Intrinsic Dimension Estimation
184. ICML 2026Local Intrinsic Dimension of Representations Predicts Alignment and Generalization in AI Models and Human Brain
185. ICML 2026LOTTERY: Learning from Reference-Only Samples in Two-Sample Testing under Size Asymmetry
186. ICML 2026Many Experiments, Few Repetitions, Unpaired Data, and Sparse Effects: Is Causal Inference Possible?
187. ICML 2026Masked Multi-path Contrast with Confidence-Gated Semantic Imputation for Incomplete Multi-view Clustering
188. ICML 2026Measuring Intent Comprehension in LLMs
189. ICML 2026MEC: Machine-Learning-Assisted Generalized Entropy Calibration for Semi-Supervised Mean Estimation
190. ICML 2026MePo: Meta Post-Refinement for Rehearsal-Free General Continual Learning
191. ICML 2026Merge to Remember: Sharpness-Aware Isotropic Merging for Continual Learning
192. ICML 2026Message Passing on the Edge: Towards Scalable and Expressive GNNs
193. ICML 2026mHC: Manifold-Constrained Hyper-Connections
194. ICML 2026MixFP4: Enhancing NVFP4 with Adaptive FP4/INT4 Block Representations
195. ICML 2026MoCL: Metabolic Optimization for Curvature-Aware Continual Learning
196. ICML 2026Modelling Attention with Aitchison Geometry: Token Distinguishability and Temperature Scaling
197. ICML 2026Multi-View Causal Discovery without Non-Gaussianity: Identifiability and Algorithms
198. ICML 2026Multimodal Nested Learning for Decoupled and Coordinated Optimization
199. ICML 2026Multivariate Distributional Reinforcement Learning Using Sliced Divergences
200. ICML 2026Multiview Self-Representation Learning across Heterogeneous Views

常见问题

2023–2026 年四大会议一共有多少篇通用机器学习 · unsupervised and semisupervised learning论文？

共 2,683 篇，按年份分别是 2023 年 649 篇、2024 年 553 篇、2025 年 682 篇、2026 年 799 篇。

NeurIPS 2026 有多少篇通用机器学习 · unsupervised and semisupervised learning论文？

NeurIPS 2026 有 194 篇通用机器学习 · unsupervised and semisupervised learning论文，上一届是 203 篇。

ICLR 2026 有多少篇通用机器学习 · unsupervised and semisupervised learning论文？

ICLR 2026 有 187 篇通用机器学习 · unsupervised and semisupervised learning论文，上一届是 174 篇。

ICML 2026 有多少篇通用机器学习 · unsupervised and semisupervised learning论文？

ICML 2026 有 348 篇通用机器学习 · unsupervised and semisupervised learning论文，上一届是 239 篇。

CVPR 2026 有多少篇通用机器学习 · unsupervised and semisupervised learning论文？

CVPR 2026 有 70 篇通用机器学习 · unsupervised and semisupervised learning论文，上一届是 66 篇。

哪个会议的通用机器学习 · unsupervised and semisupervised learning论文最多？

ICML，2023–2026 年共 859 篇。