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

731 papers, page 3 of 15

101. NeurIPS 2025Analog Foundation Models
102. NeurIPS 2025Anchor-based Maximum Discrepancy for Relative Similarity Testing
103. NeurIPS 2025Anytime-valid, Bayes-assisted, Prediction-Powered Inference
104. NeurIPS 2025ArchPower: Dataset for Architecture-Level Power Modeling of Modern CPU Design
105. NeurIPS 2025Asymmetric Dual Self-Distillation for 3D Self-Supervised Representation Learning
106. NeurIPS 2025Asymptotically Stable Quaternion-valued Hopfield-structured Neural Network with Periodic Projection-based Supervised Learning Rules
107. NeurIPS 2025AutoHood3D: A Multi‑Modal Benchmark for Automotive Hood Design and Fluid–Structure Interaction
108. NeurIPS 2025Backdoor Mitigation via Invertible Pruning Masks
109. NeurIPS 2025Bayesian Optimization with Preference Exploration using a Monotonic Neural Network Ensemble
110. NeurIPS 2025Boosting Adversarial Transferability with Spatial Adversarial Alignment
111. NeurIPS 2025Breakthrough Sensor-Limited Single View: Towards Implicit Temporal Dynamics for Time Series Domain Adaptation
112. NeurIPS 2025CALM-PDE: Continuous and Adaptive Convolutions for Latent Space Modeling of Time-dependent PDEs
113. NeurIPS 2025CLEAR: Command Level Annotated Dataset for Ransomware Detection
114. NeurIPS 2025CLIMB: Class-imbalanced Learning Benchmark on Tabular Data
115. NeurIPS 2025Coloring Learning for Heterophilic Graph Representation
116. NeurIPS 2025ComRank: Ranking Loss for Multi-Label Complementary Label Learning
117. NeurIPS 2025Confusion-Driven Self-Supervised Progressively Weighted Ensemble Learning for Non-Exemplar Class Incremental Learning
118. NeurIPS 2025Consensus-Robust Transfer Attacks via Parameter and Representation Perturbations
119. NeurIPS 2025Contrastive Self-Supervised Learning As Neural Manifold Packing
120. NeurIPS 2025Correcting misinterpretations of additive models
121. NeurIPS 2025Cost-Sensitive Freeze-thaw Bayesian Optimization for Efficient Hyperparameter Tuning
122. NeurIPS 2025CoUn: Empowering Machine Unlearning via Contrastive Learning
123. NeurIPS 2025Data-Free Model Extraction for Black-box Recommender Systems via Graph Convolutions
124. NeurIPS 2025Decompile-Bench: Million-Scale Binary-Source Function Pairs for Real-World Binary Decompilation
125. NeurIPS 2025DecompNet: Enhancing Time Series Forecasting Models with Implicit Decomposition
126. NeurIPS 2025Decoupled Entropy Minimization
127. NeurIPS 2025DeltaPhi: Physical States Residual Learning for Neural Operators in Data-Limited PDE Solving
128. NeurIPS 2025Demystifying Spectral Feature Learning for Instrumental Variable Regression
129. NeurIPS 2025Diffusion-Guided Graph Data Augmentation
130. NeurIPS 2025Disentanglement Beyond Static vs. Dynamic: A Benchmark and Evaluation Framework for Multi-Factor Sequential Representations
131. NeurIPS 2025Distributionally Robust Performative Optimization
132. NeurIPS 2025Ditch the Denoiser: Emergence of Noise Robustness in Self-Supervised Learning from Data Curriculum
133. NeurIPS 2025Doubly-Robust Estimation of Counterfactual Policy Mean Embeddings
134. NeurIPS 2025DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge
135. NeurIPS 2025DUAL: Learning Diverse Kernels for Aggregated Two-sample and Independence Testing
136. NeurIPS 2025EddyFormer: Accelerated Neural Simulations of Three-Dimensional Turbulence at Scale
137. NeurIPS 2025Efficient Knowledge Transfer in Federated Recommendation for Joint Venture Ecosystem
138. NeurIPS 2025EngiBench: A Framework for Data-Driven Engineering Design Research
139. NeurIPS 2025Enhancing Contrastive Learning with Variable Similarity
140. NeurIPS 2025Enhancing Privacy in Multimodal Federated Learning with Information Theory
141. NeurIPS 2025ENMA: Tokenwise Autoregression for Continuous Neural PDE Operators
142. NeurIPS 2025Evaluating LLM-contaminated Crowdsourcing Data Without Ground Truth
143. NeurIPS 2025Exploring and Exploiting Model Uncertainty in Bayesian Optimization
144. NeurIPS 2025Exploring Structural Degradation in Dense Representations for Self-supervised Learning
145. NeurIPS 2025Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning
146. NeurIPS 2025FedRAM: Federated Reweighting and Aggregation for Multi-Task Learning
147. NeurIPS 2025Flick: Empowering Federated Learning with Commonsense Knowledge
148. NeurIPS 2025Fourier Clouds: Fast Bias Correction for Imbalanced Semi-Supervised Learning
149. NeurIPS 2025From Replication to Redesign: Exploring Pairwise Comparisons for LLM-Based Peer Review
150. NeurIPS 2025From Sequence to Structure: Uncovering Substructure Reasoning in Transformers

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.