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Rich Caruana

  1. Do Deep Nets Really Need to be Deep?
    2013/12/21 by Jimmy Ba, Lei Jimmy Ba, Rich Caruana +2 · 4 voices · 100 citations
    Computer Science · #Generative Adversarial Networks and Image Synthesis #Music and Audio Processing #Speech Recognition and Synthesis #cs.LG #cs.NE
  2. Neural Additive Models: Interpretable Machine Learning with Neural Nets
    2020/04/29 by Rishabh Agarwal, Agarwal, Rishabh, Nicholas Frosst +9 · 37 citations
    Computer Science · #Explainable Artificial Intelligence (XAI) #Adversarial Robustness in Machine Learning #Machine Learning and Data Classification
  3. InterpretML: A Unified Framework for Machine Learning Interpretability
    2019/09/19 by Harsha Nori, Nori, Harsha, Samuel Jenkins +5 · 24 citations
    Computer Science · #Explainable Artificial Intelligence (XAI) #Adversarial Robustness in Machine Learning #Statistical and Computational Modeling
  4. Do Deep Convolutional Nets Really Need to be Deep and Convolutional?
    2016/03/17 by Gregor Urban, Urban, Gregor, Krzysztof J. Geras +15 · 17 citations
    Computer Science · #Advanced Neural Network Applications #Machine Learning and Data Classification #Adversarial Robustness in Machine Learning
  5. Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models
    2019/11/12 by Benjamin J. Lengerich, Sarah Tan, Lengerich, Benjamin +7 · 6 citations
    Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Fault Detection and Control Systems #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #Neural Networks and Applications
  6. Obtaining Calibrated Probabilities from Boosting
    2012/07/04 by Alexandru Niculescu-Mizil, Niculescu-Mizil, Alexandru, Rich Caruana +1 · 4 citations
    Computer Science · #Explainable Artificial Intelligence (XAI) #Imbalanced Data Classification Techniques #Machine Learning and Data Classification
  7. Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
    2021/06/17 by Harsha Nori, Rich Caruana, Nori, Harsha +7 · 4 citations
    Computer Science · #Privacy-Preserving Technologies in Data #Explainable Artificial Intelligence (XAI) #Adversarial Robustness in Machine Learning
  8. How Interpretable and Trustworthy are GAMs?
    2020/06/11 by Chun‐Hao Chang, Chang, Chun-Hao, Sarah Tan +7 · 3 citations
    Computer Science · #Explainable Artificial Intelligence (XAI) #Machine Learning and Data Classification #Adversarial Robustness in Machine Learning
  9. Differentially Private Estimation of Heterogeneous Causal Effects
    2022/02/22 by Fengshi Niu, Niu, Fengshi, Harsha Nori +9 · 3 citations
    Computer Science · Mathematics · #Advanced Causal Inference Techniques #Cryptography and Security (cs.CR) #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Privacy-Preserving Technologies in Data #Statistical Methods and Bayesian Inference
  10. Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models
    2024/04/09 by Sebastian Bordt, Bordt, Sebastian, Harsha Nori +7 · 5 citations
    Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Natural Language Processing Techniques #Topic Modeling
  11. Sparse Partially Linear Additive Models
    2014/07/17 by Yin Lou, Jacob Bien, Lou, Yin +5 · 1 citation
    Mathematics · Computer Science · Engineering · #Statistical Methods and Inference #Face and Expression Recognition #Control Systems and Identification
  12. Interpretable & Explorable Approximations of Black Box Models
    2017/07/04 by Himabindu Lakkaraju, Ece Kamar, Lakkaraju, Himabindu +5 · 1 citation
    Computer Science · #Artificial Intelligence (cs.AI) #Data Stream Mining Techniques #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning and Data Classification
  13. Elephants Never Forget: Testing Language Models for Memorization of Tabular Data
    2024/03/11 by Sebastian Bordt, Harsha Nori, Bordt, Sebastian +3 · 1 citation
    Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Natural Language Processing Techniques #Semantic Web and Ontologies #Topic Modeling