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

511 papers, page 5 of 11

201. NeurIPS 2024Customizing Language Models with Instance-wise LoRA for Sequential Recommendation
202. NeurIPS 2024CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns
203. NeurIPS 2024DALD: Improving Logits-based Detector without Logits from Black-box LLMs
204. NeurIPS 2024Data Acquisition via Experimental Design for Data Markets
205. NeurIPS 2024Data-Efficient Learning with Neural Programs
206. NeurIPS 2024Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning
207. NeurIPS 2024DDN: Dual-domain Dynamic Normalization for Non-stationary Time Series Forecasting
208. NeurIPS 2024Decoupling Semantic Similarity from Spatial Alignment for Neural Networks.
209. NeurIPS 2024DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach
210. NeurIPS 2024Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond
211. NeurIPS 2024Deep Submodular Peripteral Networks
212. NeurIPS 2024DeformableTST: Transformer for Time Series Forecasting without Over-reliance on Patching
213. NeurIPS 2024DeiSAM: Segment Anything with Deictic Prompting
214. NeurIPS 2024DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking
215. NeurIPS 2024Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval
216. NeurIPS 2024DePLM: Denoising Protein Language Models for Property Optimization
217. NeurIPS 2024Derivative-enhanced Deep Operator Network
218. NeurIPS 2024Designing Cell-Type-Specific Promoter Sequences Using Conservative Model-Based Optimization
219. NeurIPS 2024Detecting Bugs with Substantial Monetary Consequences by LLM and Rule-based Reasoning
220. NeurIPS 2024Differentially Private Graph Diffusion with Applications in Personalized PageRanks
221. NeurIPS 2024DiffLight: A Partial Rewards Conditioned Diffusion Model for Traffic Signal Control with Missing Data
222. NeurIPS 2024DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis
223. NeurIPS 2024Disentangling Interpretable Factors with Supervised Independent Subspace Principal Component Analysis
224. NeurIPS 2024Dissect Black Box: Interpreting for Rule-Based Explanations in Unsupervised Anomaly Detection
225. NeurIPS 2024Dissecting the Interplay of Attention Paths in a Statistical Mechanics Theory of Transformers
226. NeurIPS 2024Diversify, Contextualize, and Adapt: Efficient Entropy Modeling for Neural Image Codec
227. NeurIPS 2024Double-Ended Synthesis Planning with Goal-Constrained Bidirectional Search
228. NeurIPS 2024DPIC: Decoupling Prompt and Intrinsic Characteristics for LLM Generated Text Detection
229. NeurIPS 2024DRACO: A Denoising-Reconstruction Autoencoder for Cryo-EM
230. NeurIPS 2024Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data
231. NeurIPS 2024Dual-Personalizing Adapter for Federated Foundation Models
232. NeurIPS 2024Efficiency for Free: Ideal Data Are Transportable Representations
233. NeurIPS 2024Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes
234. NeurIPS 2024Efficient Streaming Algorithms for Graphlet Sampling
235. NeurIPS 2024Embedding Dimension of Contrastive Learning and $k$-Nearest Neighbors
236. NeurIPS 2024Empowering Active Learning for 3D Molecular Graphs with Geometric Graph Isomorphism
237. NeurIPS 2024End-to-End Ontology Learning with Large Language Models
238. NeurIPS 2024Enhancing Domain Adaptation through Prompt Gradient Alignment
239. NeurIPS 2024Enhancing LLM Reasoning via Vision-Augmented Prompting
240. NeurIPS 2024Enhancing Semi-Supervised Learning via Representative and Diverse Sample Selection
241. NeurIPS 2024Evaluate calibration of language models with folktexts
242. NeurIPS 2024Evidential Mixture Machines: Deciphering Multi-Label Correlations for Active Learning Sensitivity
243. NeurIPS 2024Evidential Stochastic Differential Equations for Time-Aware Sequential Recommendation
244. NeurIPS 2024Ex Uno Pluria: Insights on Ensembling in Low Precision Number Systems
245. NeurIPS 2024Expert-level protocol translation for self-driving labs
246. NeurIPS 2024Exploiting Activation Sparsity with Dense to Dynamic-k Mixture-of-Experts Conversion
247. NeurIPS 2024Exploiting Representation Curvature for Boundary Detection in Time Series
248. NeurIPS 2024Exploring Structured Semantic Priors Underlying Diffusion Score for Test-time Adaptation
249. NeurIPS 2024Extending Multi-modal Contrastive Representations
250. NeurIPS 2024Fast Iterative Hard Thresholding Methods with Pruning Gradient Computations

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.