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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
TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks
NIPS 2024
Toward Efficient Inference for Mixture of Experts
NIPS 2024
Dynamic Subgroup Identification in Covariate-adjusted Response-adaptive Randomization Experiments
NIPS 2024
IWBVT: Instance Weighting-based Bias-Variance Trade-off for Crowdsourcing
NIPS 2024
Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers
NIPS 2024
How many classifiers do we need?
NIPS 2024
AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers
NIPS 2024
Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach
NIPS 2024
IDGen: Item Discrimination Induced Prompt Generation for LLM Evaluation
NIPS 2024
A Near-optimal Algorithm for Learning Margin Halfspaces with Massart Noise
NIPS 2024
What Makes Partial-Label Learning Algorithms Effective?
NIPS 2024
Rethinking the Evaluation of Out-of-Distribution Detection: A Sorites Paradox
NIPS 2024
On the Adversarial Robustness of Benjamini Hochberg
NIPS 2024
emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface Electromyography
NIPS 2024
Collaborative Refining for Learning from Inaccurate Labels
NIPS 2024
SSA-Seg: Semantic and Spatial Adaptive Pixel-level Classifier for Semantic Segmentation
NIPS 2024
Stable Minima Cannot Overfit in Univariate ReLU Networks: Generalization by Large Step Sizes
NIPS 2024
OxonFair: A Flexible Toolkit for Algorithmic Fairness
NIPS 2024
Computerized Adaptive Testing via Collaborative Ranking
NIPS 2024
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data
NIPS 2024
Classification Done Right for Vision-Language Pre-Training
NIPS 2024
Noisy Label Learning with Instance-Dependent Outliers: Identifiability via Crowd Wisdom
NIPS 2024
Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
NIPS 2024
Semi-supervised Multi-label Learning with Balanced Binary Angular Margin Loss
NIPS 2024
Statistical Multicriteria Benchmarking via the GSD-Front
NIPS 2024
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