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 13 of 15

601. NeurIPS 2024Towards Next-Generation Logic Synthesis: A Scalable Neural Circuit Generation Framework
602. NeurIPS 2024Towards Stable Representations for Protein Interface Prediction
603. NeurIPS 2024Towards training digitally-tied analog blocks via hybrid gradient computation
604. NeurIPS 2024Towards Understanding Evolving Patterns in Sequential Data
605. NeurIPS 2024Training Compute-Optimal Protein Language Models
606. NeurIPS 2024Transductive Active Learning: Theory and Applications
607. NeurIPS 2024Transferable Adversarial Attacks on SAM and Its Downstream Models
608. NeurIPS 2024TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks
609. NeurIPS 2024UDC: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization Problems
610. NeurIPS 2024Ultrafast classical phylogenetic method beats large protein language models on variant effect prediction
611. NeurIPS 2024Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective
612. NeurIPS 2024UniIF: Unified Molecule Inverse Folding
613. NeurIPS 2024Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators
614. NeurIPS 2024Universal Sample Coding
615. NeurIPS 2024Unlocking the Potential of Global Human Expertise
616. NeurIPS 2024UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation
617. NeurIPS 2024Unsupervised Anomaly Detection in The Presence of Missing Values
618. NeurIPS 2024Unsupervised Discovery of Formulas for Mathematical Constants
619. NeurIPS 2024Untrained Neural Nets for Snapshot Compressive Imaging: Theory and Algorithms
620. NeurIPS 2024Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?
621. NeurIPS 2024Unveiling The Matthew Effect Across Channels: Assessing Layer Width Sufficiency via Weight Norm Variance
622. NeurIPS 2024UQE: A Query Engine for Unstructured Databases
623. NeurIPS 2024Verified Code Transpilation with LLMs
624. NeurIPS 2024VISA: Variational Inference with Sequential Sample-Average Approximations
625. NeurIPS 2024Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning
626. NeurIPS 2024Weight Diffusion for Future: Learn to Generalize in Non-Stationary Environments
627. NeurIPS 2024What Makes Partial-Label Learning Algorithms Effective?
628. NeurIPS 2024What makes unlearning hard and what to do about it
629. NeurIPS 2024When Is Inductive Inference Possible?
630. NeurIPS 2024Why Go Full? Elevating Federated Learning Through Partial Network Updates
631. NeurIPS 2024WindsorML: High-Fidelity Computational Fluid Dynamics Dataset For Automotive Aerodynamics
632. NeurIPS 2024You Don’t Need Domain-Specific Data Augmentations When Scaling Self-Supervised Learning
633. NeurIPS 2024ZOPP: A Framework of Zero-shot Offboard Panoptic Perception for Autonomous Driving
634. NeurIPS 2023A General Framework for Equivariant Neural Networks on Reductive Lie Groups
635. NeurIPS 2023A General Framework for Robust G-Invariance in G-Equivariant Networks
636. NeurIPS 2023A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning
637. NeurIPS 2023A Unified Approach to Count-Based Weakly Supervised Learning
638. NeurIPS 2023AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking
639. NeurIPS 2023Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions
640. NeurIPS 2023An Adaptive Algorithm for Learning with Unknown Distribution Drift
641. NeurIPS 2023Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods
642. NeurIPS 2023Asymptotics of Bayesian Uncertainty Estimation in Random Features Regression
643. NeurIPS 2023Binary Classification with Confidence Difference
644. NeurIPS 2023Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders
645. NeurIPS 2023CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning
646. NeurIPS 2023Can semi-supervised learning use all the data effectively? A lower bound perspective
647. NeurIPS 2023CAPro: Webly Supervised Learning with Cross-modality Aligned Prototypes
648. NeurIPS 2023Characterizing the Impacts of Semi-supervised Learning for Weak Supervision
649. NeurIPS 2023Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning
650. NeurIPS 2023ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

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.