“‘tabular ML’ Tag”,2019-09-02 (Transformer)⁠:

Bibliography for tag ai/tabular, most recent first: 1 related tag, 103 annotations, & 11 links (parent).

• See Also
• Gwern
  • “Fully-Connected Neural Nets”, Gwern2021
  • “Weather and My Productivity”, Gwern2013
• Links
  • “Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond”, Jeffares et al 2024
  • “Probing the Decision Boundaries of In-Context Learning in Large Language Models”, Zhao et al 2024
  • “Attention As an RNN”, Feng et al 2024
  • “The Harms of Class Imbalance Corrections for Machine Learning Based Prediction Models: a Simulation Study”, Carriero et al 2024
  • “Many-Shot In-Context Learning”, Agarwal et al 2024
  • “From Words to Numbers: Your Large Language Model Is Secretly A Capable Regressor When Given In-Context Examples”, Vacareanu et al 2024
  • “Chronos: Learning the Language of Time Series”, Ansari et al 2024
  • “StructLM: Towards Building Generalist Models for Structured Knowledge Grounding”, Zhuang et al 2024
  • “Why Do Random Forests Work? Understanding Tree Ensembles As Self-Regularizing Adaptive Smoothers”, Curth et al 2024
  • “Illusory Generalizability of Clinical Prediction Models”
  • “Attention versus Contrastive Learning of Tabular Data—A Data-Centric Benchmarking”, Rabbani et al 2024
  • “TabLib: A Dataset of 627M Tables With Context”, Eggert et al 2023
  • “Unambiguous Discrimination of All 20 Proteinogenic Amino Acids and Their Modifications by Nanopore”, Wang et al 2023d
  • “Generating and Imputing Tabular Data via Diffusion and Flow-Based Gradient-Boosted Trees”, Jolicoeur-Martineau et al 2023
  • “Generating Tabular Datasets under Differential Privacy”, Truda2023
  • “TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT”, Zha et al 2023
  • “Language Models Are Weak Learners”, Manikandan et al 2023
  • “RGD: Stochastic Re-Weighted Gradient Descent via Distributionally Robust Optimization”, Kumar et al 2023
  • “Large Language Models Are Few-Shot Health Learners”, Liu et al 2023
  • “Deep Learning Based Forecasting: a Case Study from the Online Fashion Industry”, Kunz et al 2023
  • “Learning and Memorization”, Chatterjee2023
  • “Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes”, Arora et al 2023
  • “TSMixer: An All-MLP Architecture for Time Series Forecasting”, Chen et al 2023
  • “Large Language Models Are Versatile Decomposers: Decompose Evidence and Questions for Table-Based Reasoning”, Ye et al 2023
  • “Fast Semi-Supervised Self-Training Algorithm Based on Data Editing”, Li et al 2023
  • “Table-To-Text Generation and Pre-Training With TabT5”, Andrejczuk et al 2022
  • “Language Models Are Realistic Tabular Data Generators”, Borisov et al 2022
  • “Forecasting With Trees”, Januschowski et al 2022
  • “Why Do Tree-Based Models Still Outperform Deep Learning on Tabular Data?”, Grinsztajn et al 2022
  • “Revisiting Pretraining Objectives for Tabular Deep Learning”, Rubachev et al 2022
  • “TabPFN: Meta-Learning a Real-Time Tabular AutoML Method For Small Data”, Hollmann et al 2022
  • “Transfer Learning With Deep Tabular Models”, Levin et al 2022
  • “Hopular: Modern Hopfield Networks for Tabular Data”, Schäfl et al 2022
  • “Predicting Romantic Interest during Early Relationship Development: A Preregistered Investigation Using Machine Learning”, Eastwick et al 2022
  • “On Embeddings for Numerical Features in Tabular Deep Learning”, Gorishniy et al 2022
  • “To SMOTE, or Not to SMOTE?”, Elor & Averbuch-Elor2022
  • “M5 Accuracy Competition: Results, Findings, and Conclusions”, Makridakis et al 2022
  • “The GatedTabTransformer: An Enhanced Deep Learning Architecture for Tabular Modeling”, Cholakov & Kolev2022
  • “PFNs: Transformers Can Do Bayesian Inference”, Müller et al 2021
  • “DANets: Deep Abstract Networks for Tabular Data Classification and Regression”, Chen et al 2021
  • “Deep Neural Networks and Tabular Data: A Survey”, Borisov et al 2021
  • “An Unsupervised Model for Identifying and Characterizing Dark Web Forums”, Nazah et al 2021
  • “TAPEX: Table Pre-Training via Learning a Neural SQL Executor”, Liu et al 2021
  • “ARM-Net: Adaptive Relation Modeling Network for Structured Data”, Cai et al 2021
  • “Decision Tree Heuristics Can Fail, Even in the Smoothed Setting”, Blanc et al 2021
  • “SCARF: Self-Supervised Contrastive Learning Using Random Feature Corruption”, Bahri et al 2021
  • “Revisiting Deep Learning Models for Tabular Data”, Gorishniy et al 2021
  • “The Epic Sepsis Model Falls Short—The Importance of External Validation”, An et al 2021
  • “Well-Tuned Simple Nets Excel on Tabular Datasets”, Kadra et al 2021
  • “Tabular Data: Deep Learning Is Not All You Need”, Shwartz-Ziv & Armon2021
  • “Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning”, Kossen et al 2021
  • “SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training”, Somepalli et al 2021
  • “Intelligence and General Psychopathology in the Vietnam Experience Study: A Closer Look”, Kirkegaard & Nyborg2021
  • “Converting Tabular Data into Images for Deep Learning With Convolutional Neural Networks”, Zhu et al 2021
  • “External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients”, Wong et al 2021
  • “Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting”, Zhou et al 2020
  • “TabTransformer: Tabular Data Modeling Using Contextual Embeddings”, Huang et al 2020
  • “Engineering In-Place (Shared-Memory) Sorting Algorithms”, Axtmann et al 2020
  • “Kaggle Forecasting Competitions: An Overlooked Learning Opportunity”, Bojer & Meldgaard2020
  • “TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data”, Yin et al 2020
  • “Neural Additive Models: Interpretable Machine Learning With Neural Nets”, Agarwal et al 2020
  • “TAPAS: Weakly Supervised Table Parsing via Pre-Training”, Herzig et al 2020
  • “A Market in Dream: the Rapid Development of Anonymous Cybercrime”, Zhou et al 2020b
  • “VIME: Extending the Success of Self-Supervised and Semi-Supervised Learning to Tabular Domain”, Yoon et al 2020
  • “Fooling LIME and SHAP: Adversarial Attacks on Post Hoc Explanation Methods”, Slack et al 2019
  • “The Bouncer Problem: Challenges to Remote Explainability”, Merrer & Tredan2019
  • “OHAC: Online Hierarchical Clustering Approximations”, Menon et al 2019
  • “LightGBM: A Highly Efficient Gradient Boosting Decision Tree”, Ke et al 2019
  • “TabNet: Attentive Interpretable Tabular Learning”, Arik & Pfister2019
  • “3D Human Pose Estimation via Human Structure-Aware Fully Connected Network”, Zhang et al 2019d
  • “ID3 Learns Juntas for Smoothed Product Distributions”, Brutzkus et al 2019
  • “Behavioral Patterns in Smartphone Usage Predict Big Five Personality Traits”, Stachl et al 2019
  • “Asymptotic Learning Curves of Kernel Methods: Empirical Data versus Teacher-Student Paradigm”, Spigler et al 2019
  • “N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting”, Oreshkin et al 2019
  • “SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data”, Sun et al 2019
  • “Fairwashing: the Risk of Rationalization”, Aïvodji et al 2019
  • “Tweedie Gradient Boosting for Extremely Unbalanced Zero-Inflated Data”, Zhou et al 2018
  • “Neural Arithmetic Logic Units”, Trask et al 2018
  • “Large-Scale Comparison of Machine Learning Methods for Drug Target Prediction on ChEMBL”, Mayr et al 2018
  • “Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery”, Simm et al 2018
  • “Improving Palliative Care With Deep Learning”, An et al 2018
  • “Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario”, Vie et al 2017
  • “Neural Collaborative Filtering”, He et al 2017
  • “OpenML Benchmarking Suites”, Bischl et al 2017
  • “CatBoost: Unbiased Boosting With Categorical Features”, Prokhorenkova et al 2017
  • “Resource-Efficient Machine Learning in 2 KB RAM for the Internet of Things”, Kumar et al 2017
  • “XGBoost: A Scalable Tree Boosting System”, Chen & Guestrin2016
  • “”Why Should I Trust You?”: Explaining the Predictions of Any Classifier”, Ribeiro et al 2016
  • “The MovieLens Datasets: History and Context”, Harper & Konstan2015
  • “Planning As Satisfiability: Heuristics”, Rintanen2012
  • “Leakage in Data Mining: Formulation, Detection, and Avoidance”, Kaufman2011
  • “Random Survival Forests”, Ishwaran et al 2008
  • “Tree Induction vs. Logistic Regression: A Learning-Curve Analysis”, Perlich et al 2003
  • “A Survey of Methods for Scaling Up Inductive Algorithms”, Provost & Kolluri1999
  • “On the Boosting Ability of Top-Down Decision Tree Learning Algorithms”, Kearns & Mansour1999
  • “On The Effect of Data Set Size on Bias And Variance in Classification Learning”, Brain & Webb1999
  • “The Effects of Training Set Size on Decision Tree Complexity”, Oates & Jensen1997
  • “Scaling up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid”, Kohavi1996
  • “Stupid Data Miner Tricks: Overfitting the S&P 500”, Leinweber1995
  • “The MONK’s Problems-A Performance Comparison of Different Learning Algorithms”, Thrun et al 1991
  • “Symbolic and Neural Learning Algorithms: An Experimental Comparison”, Shavlik et al 1991
  • “Statistical Modeling: The Two Cultures”, Breiman2024
  • “How Good Are LLMs at Doing ML on an Unknown Dataset?”
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