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

263 papers, page 3 of 6

101. ICLR 2023One Transformer Can Understand Both 2D & 3D Molecular Data
102. ICLR 2023Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised training
103. ICLR 2023Progressively Compressed Auto-Encoder for Self-supervised Representation Learning
104. ICLR 2023RoPAWS: Robust Semi-supervised Representation Learning from Uncurated Data
105. ICLR 2023Sampling-based inference for large linear models, with application to linearised Laplace
106. ICLR 2023Sampling-free Inference for Ab-Initio Potential Energy Surface Networks
107. ICLR 2023Self-Supervised Category-Level Articulated Object Pose Estimation with Part-Level SE(3) Equivariance
108. ICLR 2023Self-supervised learning with rotation-invariant kernels
109. ICLR 2023Semi-supervised learning with a principled likelihood from a generative model of data curation
110. ICLR 2023SimPer: Simple Self-Supervised Learning of Periodic Targets
111. ICLR 2023SoftMatch: Addressing the Quantity-Quality Tradeoff in Semi-supervised Learning
112. ICLR 2023Spectral Augmentation for Self-Supervised Learning on Graphs
113. ICLR 2023Symmetric Pruning in Quantum Neural Networks
114. ICLR 2023Temperature Schedules for self-supervised contrastive methods on long-tail data
115. ICLR 2023The hidden uniform cluster prior in self-supervised learning
116. ICLR 2023Towards the Generalization of Contrastive Self-Supervised Learning
117. ICLR 2023Transformer Meets Boundary Value Inverse Problems
118. ICLR 2023Unbiased Supervised Contrastive Learning
119. ICLR 2023Understanding The Robustness of Self-supervised Learning Through Topic Modeling
120. ICLR 2023Weighted Ensemble Self-Supervised Learning
121. ICLR 2023What shapes the loss landscape of self supervised learning?
122. ICML 2023A Model-free Closeness-of-influence Test for Features in Supervised Learning
123. ICML 2023A Neural PDE Solver with Temporal Stencil Modeling
124. ICML 2023Active Ranking of Experts Based on their Performances in Many Tasks
125. ICML 2023Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano
126. ICML 2023AutoCoreset: An Automatic Practical Coreset Construction Framework
127. ICML 2023Bidirectional Adaptation for Robust Semi-Supervised Learning with Inconsistent Data Distributions
128. ICML 2023Continual Learners are Incremental Model Generalizers
129. ICML 2023CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations
130. ICML 2023Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the Least
131. ICML 2023Dimensionality Reduction for General KDE Mode Finding
132. ICML 2023Disentangled Multi-Fidelity Deep Bayesian Active Learning
133. ICML 2023Distance Weighted Supervised Learning for Offline Interaction Data
134. ICML 2023Evaluating Self-Supervised Learning via Risk Decomposition
135. ICML 2023Fast Sampling of Diffusion Models via Operator Learning
136. ICML 2023Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods
137. ICML 2023GNOT: A General Neural Operator Transformer for Operator Learning
138. ICML 2023Image Shortcut Squeezing: Countering Perturbative Availability Poisons with Compression
139. ICML 2023Learning Neural PDE Solvers with Parameter-Guided Channel Attention
140. ICML 2023Learning the Right Layers a Data-Driven Layer-Aggregation Strategy for Semi-Supervised Learning on Multilayer Graphs
141. ICML 2023Loss Balancing for Fair Supervised Learning
142. ICML 2023Machine Learning Force Fields with Data Cost Aware Training
143. ICML 2023Minimizing Trajectory Curvature of ODE-based Generative Models
144. ICML 2023Neural Inverse Operators for Solving PDE Inverse Problems
145. ICML 2023Neural Status Registers
146. ICML 2023Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization
147. ICML 2023NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data
148. ICML 2023On the Stepwise Nature of Self-Supervised Learning
149. ICML 2023Optimizing Hyperparameters with Conformal Quantile Regression
150. ICML 2023Random Teachers are Good Teachers

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.