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 157 篇，第 2 / 4 页

51. ICML 2023Fair and Optimal Classification via Post-Processing
52. ICML 2023Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning
53. ICML 2023Fair yet Asymptotically Equal Collaborative Learning
54. ICML 2023Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective
55. ICML 2023Flexible Phase Dynamics for Bio-Plausible Contrastive Learning
56. ICML 2023FREDIS: A Fusion Framework of Refinement and Disambiguation for Unreliable Partial Label Learning
57. ICML 2023From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning
58. ICML 2023From Robustness to Privacy and Back
59. ICML 2023Fully Dynamic Submodular Maximization over Matroids
60. ICML 2023Fundamental Tradeoffs in Learning with Prior Information
61. ICML 2023Future-conditioned Unsupervised Pretraining for Decision Transformer
62. ICML 2023General Covariance Data Augmentation for Neural PDE Solvers
63. ICML 2023General Sequential Episodic Memory Model
64. ICML 2023Generalization Bounds using Data-Dependent Fractal Dimensions
65. ICML 2023Generalized Disparate Impact for Configurable Fairness Solutions in ML
66. ICML 2023Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Cost
67. ICML 2023GNOT: A General Neural Operator Transformer for Operator Learning
68. ICML 2023Gradient Descent in Neural Networks as Sequential Learning in Reproducing Kernel Banach Space
69. ICML 2023GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets
70. ICML 2023Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games
71. ICML 2023Hyperbolic Representation Learning: Revisiting and Advancing
72. ICML 2023Hyperparameters in Reinforcement Learning and How To Tune Them
73. ICML 2023Hypothesis Transfer Learning with Surrogate Classification Losses: Generalization Bounds through Algorithmic Stability
74. ICML 2023Identifiability and Generalizability in Constrained Inverse Reinforcement Learning
75. ICML 2023Inflow, Outflow, and Reciprocity in Machine Learning
76. ICML 2023JAWS-X: Addressing Efficiency Bottlenecks of Conformal Prediction Under Standard and Feedback Covariate Shift
77. ICML 2023Learning Compiler Pass Orders using Coreset and Normalized Value Prediction
78. ICML 2023Learning Control-Oriented Dynamical Structure from Data
79. ICML 2023Learning Distributions over Quantum Measurement Outcomes
80. ICML 2023Learning Rate Schedules in the Presence of Distribution Shift
81. ICML 2023Learning Representations without Compositional Assumptions
82. ICML 2023Learning to Boost Training by Periodic Nowcasting Near Future Weights
83. ICML 2023Learning to Initiate and Reason in Event-Driven Cascading Processes
84. ICML 2023Learning to Learn from APIs: Black-Box Data-Free Meta-Learning
85. ICML 2023Learning useful representations for shifting tasks and distributions
86. ICML 2023Learning-Rate-Free Learning by D-Adaptation
87. ICML 2023Likelihood Adjusted Semidefinite Programs for Clustering Heterogeneous Data
88. ICML 2023Lower Bounds for Learning in Revealing POMDPs
89. ICML 2023Machine Learning Force Fields with Data Cost Aware Training
90. ICML 2023Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels
91. ICML 2023Maximum Optimality Margin: A Unified Approach for Contextual Linear Programming and Inverse Linear Programming
92. ICML 2023Mixture Proportion Estimation Beyond Irreducibility
93. ICML 2023Model-Aware Contrastive Learning: Towards Escaping the Dilemmas
94. ICML 2023Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes
95. ICML 2023Multiply Robust Off-policy Evaluation and Learning under Truncation by Death
96. ICML 2023Near-Optimal $\Phi$-Regret Learning in Extensive-Form Games
97. ICML 2023No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation
98. ICML 2023Non-stationary Reinforcement Learning under General Function Approximation
99. ICML 2023Nonparametric Iterative Machine Teaching
100. ICML 2023NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data

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。