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← Core Methods
Machine Learning
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Core Methods
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Classification
15,289 papers
Papers per year
2000: 2
2001: 14
2002: 21
2003: 28
2004: 28
2005: 26
2006: 94
2007: 93
2008: 90
2009: 93
2010: 134
2011: 112
2012: 160
2013: 290
2014: 239
2015: 258
2016: 456
2017: 682
2018: 1145
2019: 1500
2020: 1638
2021: 1667
2022: 1636
2023: 1685
2024: 1600
2025: 1313
2026: 285
Papers
Towards Heterogeneous Long-tailed Learning: Benchmarking, Metrics, and Toolbox
NIPS 2024
Realizable $H$-Consistent and Bayes-Consistent Loss Functions for Learning to Defer
NIPS 2024
Conformal Alignment: Knowing When to Trust Foundation Models with Guarantees
NIPS 2024
UMB: Understanding Model Behavior for Open-World Object Detection
NIPS 2024
SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention
NIPS 2024
Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines
NIPS 2024
Fast Rates for Bandit PAC Multiclass Classification
NIPS 2024
NanoBaseLib: A Multi-Task Benchmark Dataset for Nanopore Sequencing
NIPS 2024
Testing Semantic Importance via Betting
NIPS 2024
Sm: enhanced localization in Multiple Instance Learning for medical imaging classification
NIPS 2024
Confidence Calibration of Classifiers with Many Classes
NIPS 2024
TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases
NIPS 2024
Dendritic Integration Inspired Artificial Neural Networks Capture Data Correlation
NIPS 2024
Explaining Datasets in Words: Statistical Models with Natural Language Parameters
NIPS 2024
Learning with Fitzpatrick Losses
NIPS 2024
Credal Deep Ensembles for Uncertainty Quantification
NIPS 2024
A Boosting-Type Convergence Result for AdaBoost.MH with Factorized Multi-Class Classifiers
NIPS 2024
Graph Neural Networks Need Cluster-Normalize-Activate Modules
NIPS 2024
Revive Re-weighting in Imbalanced Learning by Density Ratio Estimation
NIPS 2024
AutoPSV: Automated Process-Supervised Verifier
NIPS 2024
The Implicit Bias of Gradient Descent on Separable Multiclass Data
NIPS 2024
One-Layer Transformer Provably Learns One-Nearest Neighbor In Context
NIPS 2024
Error Correction Output Codes for Robust Neural Networks against Weight-errors: A Neural Tangent Kernel Point of View
NIPS 2024
Learning from Noisy Labels via Conditional Distributionally Robust Optimization
NIPS 2024
REDUCR: Robust Data Downsampling using Class Priority Reweighting
NIPS 2024
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