General ML · supervised learning papers, 2023–2026

Key findings

• General ML · supervised learning went from 263 papers in 2023 to 279 in 2026, roughly 1.1x in three years.
• NeurIPS publishes the most General ML · supervised learning work: 731 papers from 2023 to 2026, 51% of the four-venue total.
• The fastest-growing overlap is with Diffusion Models: papers tagged with both rose from 2 in 2023 to 9 in 2026.
• Among the 6 topics under General Machine Learning, it ranks #3 by 2026 paper count; the fastest grower in the group, General ML · evaluation, is up 4.3x versus 1.1x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026AOT-POT: Adaptive Operator Transformation for Large-Scale PDE Pre-training
• ICML 2026$\mathbb{R}^{2k}$ is Theoretically Large Enough for Embedding-based Top-$k$ Retrieval
• ICLR 2026Adaptive Concept Discovery for Interpretable Few-Shot Text Classification
• CVPR 2026Enabling Supervised Learning of Generative Signatures for Generalized AI-Generated Images
• NeurIPS 2025$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning
• ICML 2025Ab Initio Nonparametric Variable Selection for Scalable Symbolic Regression with Large $p$
• ICLR 2025Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximations
• CVPR 2025A Unified Framework for Heterogeneous Semi-supervised Learning
• NeurIPS 2024(FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning
• ICML 2024A New Branch-and-Bound Pruning Framework for $\ell_0$-Regularized Problems
• ICLR 2024A Study of Bayesian Neural Network Surrogates for Bayesian Optimization
• CVPR 2024Efficient Hyperparameter Optimization with Adaptive Fidelity Identification
• NeurIPS 2023A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning
• ICML 2023A Model-free Closeness-of-influence Test for Features in Supervised Learning
• ICLR 2023Deep Ranking Ensembles for Hyperparameter Optimization
• CVPR 2023Boosting Semi-Supervised Learning by Exploiting All Unlabeled Data
• NeurIPS 2026Approximation of Maximally Monotone Operators : A Graph Convergence Perspective
• ICML 2026Active Budget Allocation for Efficient Scaling Law Estimation via Surrogate-Guided Pruning
• ICLR 2026Buckingham $\pi$-Invariant Test‑Time Projection for Robust PDE Surrogate Modeling
• CVPR 2026HyperNAS Enhancing Architecture Representation for NAS Predictor via Hypernetwork

All General ML · supervised learning papers

313 papers, page 3 of 7

101. ICML 2026Towards Theoretical Understanding of Transformer Test-Time Computing: Investigation on In-Context Linear Regression
102. ICML 2026Trajectory-Aware Certified Decentralized Unlearning via SGD Stability
103. ICML 2026Transformer Circuits Can Realize Clustering Algorithms
104. ICML 2026Transolver-3: Scaling Up Transformer Solvers to Industrial-Scale Geometries
105. ICML 2026Trust Functions: Near Lossless Weak-to-Strong Generalization by Learning to Trust the Weak Teacher
106. ICML 2026Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity
107. ICML 2026Tuning-Free One-Class Discriminant Learning for Tabular Anomaly Detection
108. ICML 2026Two-Layer Linear Auto-Regressive Models Estimate Latent States
109. ICML 2026Uncovering the Gradient Geometry of Long CoT: A Spectral-guided Approach to Reasoning Distillation
110. ICML 2026Understanding Self-Supervised Learning via Latent Distribution Matching
111. ICML 2026When More Experts Hurt: Underfitting in Multi-Expert Learning to Defer
112. ICML 2025A Square Peg in a Square Hole: Meta-Expert for Long-Tailed Semi-Supervised Learning
113. ICML 2025Ab Initio Nonparametric Variable Selection for Scalable Symbolic Regression with Large $p$
114. ICML 2025Accelerating PDE-Constrained Optimization by the Derivative of Neural Operators
115. ICML 2025Active Learning with Selective Time-Step Acquisition for PDEs
116. ICML 2025Adaptive Partitioning Schemes for Optimistic Optimization
117. ICML 2025Almost Optimal Fully Dynamic $k$-Center Clustering with Recourse
118. ICML 2025An Analysis of Quantile Temporal-Difference Learning
119. ICML 2025Approximately Correct Label Distribution Learning
120. ICML 2025Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation
121. ICML 2025Balancing the Scales: A Theoretical and Algorithmic Framework for Learning from Imbalanced Data
122. ICML 2025Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation
123. ICML 2025BSemiFL: Semi-supervised Federated Learning via a Bayesian Approach
124. ICML 2025Calibrated Physics-Informed Uncertainty Quantification
125. ICML 2025Clustering Properties of Self-Supervised Learning
126. ICML 2025Clustering via Self-Supervised Diffusion
127. ICML 2025CoastalBench: A Decade-Long High-Resolution Dataset to Emulate Complex Coastal Processes
128. ICML 2025Collapse-Proof Non-Contrastive Self-Supervised Learning
129. ICML 2025Compressing tree ensembles through Level-wise Optimization and Pruning
130. ICML 2025Concentration Distribution Learning from Label Distributions
131. ICML 2025Confidence Difference Reflects Various Supervised Signals in Confidence-Difference Classification
132. ICML 2025Correlation Clustering Beyond the Pivot Algorithm
133. ICML 2025Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
134. ICML 2025Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models
135. ICML 2025Deep Electromagnetic Structure Design Under Limited Evaluation Budgets
136. ICML 2025DeepLayout: Learning Neural Representations of Circuit Placement Layout
137. ICML 2025Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning
138. ICML 2025Depth Degeneracy in Neural Networks: Vanishing Angles in Fully Connected ReLU Networks on Initialization
139. ICML 2025Discovering Global False Negatives On the Fly for Self-supervised Contrastive Learning
140. ICML 2025Disentangling Invariant Subgraph via Variance Contrastive Estimation under Distribution Shifts
141. ICML 2025Divide and Conquer: Learning Label Distribution with Subtasks
142. ICML 2025Does One-shot Give the Best Shot? Mitigating Model Inconsistency in One-shot Federated Learning
143. ICML 2025Enhancing Logits Distillation with Plug&Play Kendall's $\tau$ Ranking Loss
144. ICML 2025Enhancing Target-unspecific Tasks through a Features Matrix
145. ICML 2025EnsLoss: Stochastic Calibrated Loss Ensembles for Preventing Overfitting in Classification
146. ICML 2025ENSUR: Equitable and Statistically Unbiased Recommendation
147. ICML 2025Equivalence is All: A Unified View for Self-supervised Graph Learning
148. ICML 2025FedClean: A General Robust Label Noise Correction for Federated Learning
149. ICML 2025FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain
150. ICML 2025Focal-SAM: Focal Sharpness-Aware Minimization for Long-Tailed Classification

FAQ

How many General ML · supervised learning papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

1,425 in total: 263 in 2023, 511 in 2024, 372 in 2025, 279 in 2026.

How many General ML · supervised learning papers are at NeurIPS 2026?

NeurIPS 2026 has 87 General ML · supervised learning papers, versus 171 the year before.

How many General ML · supervised learning papers are at ICLR 2026?

ICLR 2026 has 48 General ML · supervised learning papers, versus 40 the year before.

How many General ML · supervised learning papers are at ICML 2026?

ICML 2026 has 111 General ML · supervised learning papers, versus 114 the year before.

How many General ML · supervised learning papers are at CVPR 2026?

CVPR 2026 has 33 General ML · supervised learning papers, versus 47 the year before.

Which conference publishes the most General ML · supervised learning papers?

NeurIPS, with 731 papers from 2023 to 2026.