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

318 papers, page 3 of 7

101. NeurIPS 2023Equivariant Single View Pose Prediction Via Induced and Restriction Representations
102. NeurIPS 2023Estimating and Controlling for Equalized Odds via Sensitive Attribute Predictors
103. NeurIPS 2023Estimating Causal Effects Identifiable from a Combination of Observations and Experiments
104. NeurIPS 2023Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts
105. NeurIPS 2023Explain Any Concept: Segment Anything Meets Concept-Based Explanation
106. NeurIPS 2023Explaining Predictive Uncertainty with Information Theoretic Shapley Values
107. NeurIPS 2023f-Policy Gradients: A General Framework for Goal-Conditioned RL using f-Divergences
108. NeurIPS 2023FABind: Fast and Accurate Protein-Ligand Binding
109. NeurIPS 2023Face Reconstruction from Facial Templates by Learning Latent Space of a Generator Network
110. NeurIPS 2023Fair Allocation of Indivisible Chores: Beyond Additive Costs
111. NeurIPS 2023Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint
112. NeurIPS 2023Fair, Polylog-Approximate Low-Cost Hierarchical Clustering
113. NeurIPS 2023Fast and Regret Optimal Best Arm Identification: Fundamental Limits and Low-Complexity Algorithms
114. NeurIPS 2023Fed-CO$_{2}$: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning
115. NeurIPS 2023Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds
116. NeurIPS 2023Federated Learning with Manifold Regularization and Normalized Update Reaggregation
117. NeurIPS 2023Federated Spectral Clustering via Secure Similarity Reconstruction
118. NeurIPS 2023FiGURe: Simple and Efficient Unsupervised Node Representations with Filter Augmentations
119. NeurIPS 2023First- and Second-Order Bounds for Adversarial Linear Contextual Bandits
120. NeurIPS 2023Fundamental Limits and Tradeoffs in Invariant Representation Learning
121. NeurIPS 2023General Munchausen Reinforcement Learning with Tsallis Kullback-Leibler Divergence
122. NeurIPS 2023Generalized equivalences between subsampling and ridge regularization
123. NeurIPS 2023GLIME: General, Stable and Local LIME Explanation
124. NeurIPS 2023Globally injective and bijective neural operators
125. NeurIPS 2023Granger Components Analysis: Unsupervised learning of latent temporal dependencies
126. NeurIPS 2023Graph Contrastive Learning with Stable and Scalable Spectral Encoding
127. NeurIPS 2023Graph Convolutional Kernel Machine versus Graph Convolutional Networks
128. NeurIPS 2023GUST: Combinatorial Generalization by Unsupervised Grouping with Neuronal Coherence
129. NeurIPS 2023H-nobs: Achieving Certified Fairness and Robustness in Distributed Learning on Heterogeneous Datasets
130. NeurIPS 2023H3T: Efficient Integration of Memory Optimization and Parallelism for Large-scale Transformer Training
131. NeurIPS 2023Harnessing the power of choices in decision tree learning
132. NeurIPS 2023Have it your way: Individualized Privacy Assignment for DP-SGD
133. NeurIPS 2023Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection
134. NeurIPS 2023How a Student becomes a Teacher: learning and forgetting through Spectral methods
135. NeurIPS 2023How many samples are needed to leverage smoothness?
136. NeurIPS 2023How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget
137. NeurIPS 2023HubRouter: Learning Global Routing via Hub Generation and Pin-hub Connection
138. NeurIPS 2023Human-Aligned Calibration for AI-Assisted Decision Making
139. NeurIPS 2023ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets
140. NeurIPS 2023Implicit Regularization in Over-Parameterized Support Vector Machine
141. NeurIPS 2023Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning
142. NeurIPS 2023Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL with General Regularizers and Multiple Optimal Arms
143. NeurIPS 2023Improved Frequency Estimation Algorithms with and without Predictions
144. NeurIPS 2023In Defense of Softmax Parametrization for Calibrated and Consistent Learning to Defer
145. NeurIPS 2023Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization
146. NeurIPS 2023Incentives in Private Collaborative Machine Learning
147. NeurIPS 2023Incentivizing Honesty among Competitors in Collaborative Learning and Optimization
148. NeurIPS 2023Information Maximizing Curriculum: A Curriculum-Based Approach for Learning Versatile Skills
149. NeurIPS 2023InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
150. NeurIPS 2023InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback

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.