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 9 of 11

401. NeurIPS 2024ProSST: Protein Language Modeling with Quantized Structure and Disentangled Attention
402. NeurIPS 2024Protein-Nucleic Acid Complex Modeling with Frame Averaging Transformer
403. NeurIPS 2024ProtGO: Function-Guided Protein Modeling for Unified Representation Learning
404. NeurIPS 2024Provable and Efficient Dataset Distillation for Kernel Ridge Regression
405. NeurIPS 2024Proving Theorems Recursively
406. NeurIPS 2024PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation
407. NeurIPS 2024QTIP: Quantization with Trellises and Incoherence Processing
408. NeurIPS 2024Quantum algorithm for large-scale market equilibrium computation
409. NeurIPS 2024Random Cycle Coding: Lossless Compression of Cluster Assignments via Bits-Back Coding
410. NeurIPS 2024RankUp: Boosting Semi-Supervised Regression with an Auxiliary Ranking Classifier
411. NeurIPS 2024Real-Time Selection Under General Constraints via Predictive Inference
412. NeurIPS 2024Reasoning Multi-Agent Behavioral Topology for Interactive Autonomous Driving
413. NeurIPS 2024REDUCR: Robust Data Downsampling using Class Priority Reweighting
414. NeurIPS 2024Referencing Where to Focus: Improving Visual Grounding with Referential Query
415. NeurIPS 2024Reinforcement Learning Guided Semi-Supervised Learning
416. NeurIPS 2024ReMAP: Neural Model Reprogramming with Network Inversion and Retrieval-Augmented Mapping for Adaptive Motion Forecasting
417. NeurIPS 2024Reprogramming Pretrained Target-Specific Diffusion Models for Dual-Target Drug Design
418. NeurIPS 2024Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization
419. NeurIPS 2024Resource-Aware Federated Self-Supervised Learning with Global Class Representations
420. NeurIPS 2024Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective
421. NeurIPS 2024Retrieval-Retro: Retrieval-based Inorganic Retrosynthesis with Expert Knowledge
422. NeurIPS 2024Revisiting Ensembling in One-Shot Federated Learning
423. NeurIPS 2024Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning
424. NeurIPS 2024Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective
425. NeurIPS 2024Road Network Representation Learning with the Third Law of Geography
426. NeurIPS 2024Robust Contrastive Multi-view Clustering against Dual Noisy Correspondence
427. NeurIPS 2024Robust Offline Active Learning on Graphs
428. NeurIPS 2024Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel
429. NeurIPS 2024SAM-Guided Masked Token Prediction for 3D Scene Understanding
430. NeurIPS 2024SAMPa: Sharpness-aware Minimization Parallelized
431. NeurIPS 2024Sample Selection via Contrastive Fragmentation for Noisy Label Regression
432. NeurIPS 2024SAND: Smooth imputation of sparse and noisy functional data with Transformer networks
433. NeurIPS 2024SARAD: Spatial Association-Aware Anomaly Detection and Diagnosis for Multivariate Time Series
434. NeurIPS 2024Satformer: Accurate and Robust Traffic Data Estimation for Satellite Networks
435. NeurIPS 2024Scalable and Effective Arithmetic Tree Generation for Adder and Multiplier Designs
436. NeurIPS 2024Scaling Laws in Linear Regression: Compute, Parameters, and Data
437. NeurIPS 2024SDformer: Similarity-driven Discrete Transformer For Time Series Generation
438. NeurIPS 2024SEL-BALD: Deep Bayesian Active Learning with Selective Labels
439. NeurIPS 2024Self-Guiding Exploration for Combinatorial Problems
440. NeurIPS 2024Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments
441. NeurIPS 2024Self-Labeling the Job Shop Scheduling Problem
442. NeurIPS 2024Self-supervised Transformation Learning for Equivariant Representations
443. NeurIPS 2024Semantic Routing via Autoregressive Modeling
444. NeurIPS 2024Semi-supervised Multi-label Learning with Balanced Binary Angular Margin Loss
445. NeurIPS 2024SocraticLM: Exploring Socratic Personalized Teaching with Large Language Models
446. NeurIPS 2024Source Code Foundation Models are Transferable Binary Analysis Knowledge Bases
447. NeurIPS 2024SpaFL: Communication-Efficient Federated Learning With Sparse Models And Low Computational Overhead
448. NeurIPS 2024Spiking Transformer with Experts Mixture
449. NeurIPS 2024SplitNeRF: Split Sum Approximation Neural Field for Joint Geometry, Illumination, and Material Estimation
450. NeurIPS 2024SS1: Accelerating Inference with Fast and Expressive Sketch Structured Transform

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.