Richard A. Caruana
#114,807
Most Influential Person Now
Richard A. Caruana's AcademicInfluence.com Rankings
Richard A. Caruanacomputer-science Degrees
Computer Science
#4451
World Rank
#4692
Historical Rank
Machine Learning
#873
World Rank
#885
Historical Rank
Database
#1665
World Rank
#1745
Historical Rank
Download Badge
Computer Science
Richard A. Caruana's Degrees
- PhD Computer Science Carnegie Mellon University
- Masters Computer Science Carnegie Mellon University
- Bachelors Computer Science Cornell University
Similar Degrees You Can Earn
Why Is Richard A. Caruana Influential?
(Suggest an Edit or Addition)Richard A. Caruana's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- Multitask Learning (1997) (6223)
- An empirical comparison of supervised learning algorithms (2006) (2433)
- Model compression (2006) (1803)
- Do Deep Nets Really Need to be Deep? (2013) (1770)
- Predicting good probabilities with supervised learning (2005) (1178)
- Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission (2015) (1175)
- A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization (1989) (1119)
- Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping (2000) (962)
- Multitask Learning: A Knowledge-Based Source of Inductive Bias (1993) (819)
- Ensemble selection from libraries of models (2004) (780)
- Removing the Genetics from the Standard Genetic Algorithm (1995) (666)
- Greedy Attribute Selection (1994) (632)
- An empirical evaluation of supervised learning in high dimensions (2008) (550)
- Experience with a learning personal assistant (1994) (514)
- Self-Optimizing Memory Controllers: A Reinforcement Learning Approach (2008) (470)
- Biases in the Crossover Landscape (1989) (437)
- Intelligible models for classification and regression (2012) (371)
- Efficiently exploring architectural design spaces via predictive modeling (2006) (357)
- Data mining in metric space: an empirical analysis of supervised learning performance criteria (2004) (345)
- Bridging the lexical chasm: statistical approaches to answer-finding (2000) (335)
- Accurate intelligible models with pairwise interactions (2013) (333)
- Data-Intensive Science: A New Paradigm for Biodiversity Studies (2009) (305)
- Semi-Supervised Clustering with User Feedback (2003) (303)
- Learning Many Related Tasks at the Same Time with Backpropagation (1994) (278)
- Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning (2020) (276)
- Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms (1988) (267)
- InterpretML: A Unified Framework for Machine Learning Interpretability (2019) (254)
- Consensus Clusterings (2007) (244)
- An evaluation of machine-learning methods for predicting pneumonia mortality (1997) (222)
- Interpretable & Explorable Approximations of Black Box Models (2017) (207)
- Do Deep Convolutional Nets Really Need to be Deep and Convolutional? (2016) (206)
- Faithful and Customizable Explanations of Black Box Models (2019) (198)
- Using the Future to Sort Out the Present: Rankprop and Multitask Learning for Medical Risk Evaluation (1995) (188)
- Bagging gradient-boosted trees for high precision, low variance ranking models (2011) (186)
- Neural Additive Models: Interpretable Machine Learning with Neural Nets (2020) (186)
- Meta Clustering (2006) (185)
- Obtaining Calibrated Probabilities from Boosting (2005) (181)
- Classification with partial labels (2008) (169)
- Data-Mining Discovery of Pattern and Process in Ecological Systems (2007) (165)
- Improving Document Ranking with Dual Word Embeddings (2016) (162)
- Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models (2019) (162)
- Getting the Most Out of Ensemble Selection (2006) (147)
- Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration (2016) (142)
- Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500‐hPa Geopotential Height From Historical Weather Data (2019) (141)
- Improving Data‐Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere (2020) (125)
- Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation (2017) (124)
- A Dual Embedding Space Model for Document Ranking (2016) (122)
- Inductive Transfer for Bayesian Network Structure Learning (2007) (122)
- Using genetic search to exploit the emergent behavior of neural networks (1990) (115)
- (Not) Bounding the True Error (2001) (112)
- Structured labeling for facilitating concept evolution in machine learning (2014) (98)
- Case-based explanation of non-case-based learning methods (1999) (97)
- Benefitting from the Variables that Variable Selection Discards (2003) (91)
- Learning Global Additive Explanations for Neural Nets Using Model Distillation (2018) (88)
- An Empirical Comparison of Supervised Learning Algorithms Using Different Performance Metrics (2005) (81)
- Detecting statistical interactions with additive groves of trees (2008) (78)
- Efficient architectural design space exploration via predictive modeling (2008) (75)
- Predicting parallel application performance via machine learning approaches (2007) (71)
- Algorithms and Applications for Multitask Learning (1996) (71)
- Fast algorithm for the resolution of spectra (1986) (67)
- A method for measuring the relative information content of data from different monitoring protocols (2010) (61)
- Blending LSTMs into CNNs (2015) (58)
- Predicting dire outcomes of patients with community acquired pneumonia (2005) (56)
- KDD-Cup 2004: results and analysis (2004) (55)
- FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness (2000) (53)
- Identifying Temporal Patterns and Key Players in Document Collections (1995) (52)
- Representation and Hidden Bias II: Eliminating Defining Length Bias in Genetic Search via Shuffle Crossover (1989) (49)
- Mining citizen science data to predict orevalence of wild bird species (2006) (49)
- Sparse Partially Linear Additive Models (2014) (48)
- Introduction in IND and recursive partitioning (1991) (47)
- Sub‐Seasonal Forecasting With a Large Ensemble of Deep‐Learning Weather Prediction Models (2021) (46)
- How Useful Is Relevance (1994) (46)
- Promoting Poor Features to Supervisors: Some Inputs Work Better as Outputs (1996) (45)
- Predicting cesarean delivery with decision tree models. (2000) (45)
- A Non-Parametric EM-Style Algorithm for Imputing Missing Values (2001) (45)
- How Interpretable and Trustworthy are GAMs? (2020) (43)
- Additive Groves of Regression Trees (2007) (43)
- Detecting Bias in Black-Box Models Using Transparent Model Distillation (2017) (40)
- A Dozen Tricks with Multitask Learning (1996) (40)
- Do Deep Convolutional Nets Really Need to be Deep (Or Even Convolutional)? (2016) (40)
- On Feature Selection, Bias-Variance, and Bagging (2009) (39)
- Transparent Model Distillation (2018) (39)
- Distributed tuning of machine learning algorithms using MapReduce Clusters (2011) (37)
- Data Diff: Interpretable, Executable Summaries of Changes in Distributions for Data Wrangling (2018) (36)
- Efficient Forward Architecture Search (2019) (34)
- Analysis of Deep Neural Networks with Extended Data Jacobian Matrix (2016) (31)
- Active Learning with Model Selection (2014) (30)
- Improving Classification with Pairwise Constraints: A Margin-Based Approach (2008) (25)
- Axiomatic Interpretability for Multiclass Additive Models (2018) (23)
- NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning (2021) (23)
- Optimizing to Arbitrary NLP Metrics using Ensemble Selection (2005) (22)
- Intelligible and Explainable Machine Learning: Best Practices and Practical Challenges (2020) (21)
- Statistical Machine Learning for Large-Scale Optimization (2000) (21)
- An Empirical Evaluation of Supervised Learning for ROC Area (2004) (19)
- Auditing Black-Box Models Using Transparent Model Distillation With Side Information (2017) (18)
- Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models (2019) (17)
- A Massively Distributed Parallel Genetic Algorithm (mdpGA (1992) (17)
- Learning From Imbalanced Data: Rank Metrics and Extra Tasks (2000) (16)
- Case-Based Explanation for Artificial Neural Nets (2000) (16)
- Using Multiple Representations to Improve Inductive Bias: Gray and Binary Coding for Genetic Algorithms (1989) (15)
- Introduction to IND and recursive partitioning, version 1.0 (1991) (15)
- Structured Labeling to Facilitate Concept Evolution in Machine Learning (2014) (14)
- Best Of NIPS 2005: Highlights on the 'Inductive Transfer : 10 Years Later' Workshop (2006) (14)
- Accuracy, Interpretability, and Differential Privacy via Explainable Boosting (2021) (14)
- Learning speaker, addressee and overlap detection models from multimodal streams (2012) (14)
- Learning Likely Locations (2013) (13)
- GAM Changer: Editing Generalized Additive Models with Interactive Visualization (2021) (12)
- Gamut (2019) (12)
- Discovering Blind Spots of Predictive Models: Representations and Policies for Guided Exploration (2016) (11)
- Estimating the Number of Local Minima in Big, Nasty Search Spaces (1999) (11)
- C2FS: An Algorithm for Feature Selection in Cascade Neural Networks (2006) (11)
- Gauss meets Canadian traveler: shortest-path problems with correlated natural dynamics (2014) (10)
- Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, California, USA, August 12-15, 2007 (2007) (10)
- On Dropout, Overfitting, and Interaction Effects in Deep Neural Networks (2020) (10)
- Multitask pattern recognition for autonomous robots (1998) (9)
- The automatic training of rule bases that use numerical uncertainty representations (1987) (9)
- Automatic Machine Learning (AutoML) (2015) (9)
- Detecting and Interpreting Variable Interactions in Observational Ornithology Data (2009) (8)
- Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values (2022) (8)
- Additional capabilities of a fast algorithm for the resolution of spectra (1988) (7)
- Cluster Ensembles for Network Anomaly Detection (2006) (6)
- Classifier Loss Under Metric Uncertainty (2007) (6)
- Compressing LSTMs into CNNs (2015) (6)
- Evaluating the C-section Rate of Different Physician Practices: Using Machine Learning to Model Standard Practice (2003) (6)
- Intelligible Machine Learning for Critical Applications Such As Health Care (2017) (5)
- Implicit Preference Labels for Learning Highly Selective Personalized Rankers (2015) (5)
- Considerations When Learning Additive Explanations for Black-Box Models (2018) (4)
- Detecting Migrating Birds at Night (2016) (4)
- Discovering Unknown Unknowns of Predictive Models (2016) (4)
- Friends Don't Let Friends Deploy Black-Box Models: The Importance of Intelligibility in Machine Learning (2019) (4)
- Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning (2017) (3)
- Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data (2021) (3)
- Dropout as a Regularizer of Interaction Effects (2020) (2)
- Machine learning for sub-population assessment: evaluating the C-section rate of different physician practices (2002) (2)
- Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models (2018) (2)
- Using multiple samples to learn mixture models (2013) (2)
- Using Feature Selection to Find Inputs that Work Better as Extra Outputs (1998) (2)
- Differentially Private Estimation of Heterogeneous Causal Effects (2022) (1)
- Outside the Machine Learning Blackbox: Supporting Analysts Before and After the Learning Algorithm (2010) (1)
- A Method for Automatically Finding Interpretations of Reduced Dimension Representations (2004) (1)
- Circulating Tumor Cell Assay Enables Prediction of Recurrence Following Stereotactic Body Radiotherapy for Early-Stage Non-Small Cell Lung Cancer: An Interpretable Machine Learning Study. (2021) (1)
- A Survey on Multi Objective Optimization Challenges in Swarm Intelligence (2021) (1)
- Fit Interpretable Machine Learning Models [R package interpret version 0.1.26] (2020) (0)
- Friends Don’t Let Friends Deploy Black-Box Models: The Importance of Transparency and Intelligibility in Machine Learning (2018) (0)
- Book Review: A Programmer's Guide to COMMON LISP by Deborah G. Tatar, Digital Press, 1987 (1987) (0)
- Introduction to the Special Issue ACM SIGKDD 2012 (2013) (0)
- Extracting Clinician's Goals by What-if Interpretable Modeling (2021) (0)
- Robust Learning with FeatureBoostJoseph (2007) (0)
- N EED TO BE D EEP AND C ONVOLUTIONAL ? (2017) (0)
- Multitask Pattern Recognition for Vision-Based Autonomous Robots (1998) (0)
- Special issue on best of SIGKDD 2011 (2012) (0)
- Special Issue on Responsible AI and Human-AI Interaction (2022) (0)
- Using Multiple Samples to Learn Mixture Models - Extended Version (2013) (0)
- Can Omics Help in Prognostic Machine Learning Interpretability? (2021) (0)
- Dec . 7 , 2018 Critiquing and Correcting Trends in Machine Learning (0)
- Augmenting Interpretable Models with LLMs during Training (2022) (0)
- Why Data Scientists Prefer Glassbox Machine Learning: Algorithms, Differential Privacy, Editing and Bias Mitigation (2022) (0)
- Statistical Machine Learning for Large-Scale Optimization Contributors (0)
- Getting the Most out of your Data: Multitask Bayesian Network Structure Learning, Predicting Good Probabilities and Ensemble Selection (2008) (0)
- Supplementary Material for Sparse Partially Linear Additive Models (2016) (0)
- Neural Cognitive Architectures for Never-Ending Learning (2019) (0)
- Clustering: probably approximately useless? (2013) (0)
- 2.2.1 Artiicial and Natural Evolution (1991) (0)
- Learning to Detect Vandalism in Social Content Systems: A Study on Wikipedia - Vandalism Detection in Wikipedia (2013) (0)
- GAM Coach: Towards Interactive and User-centered Algorithmic Recourse (2023) (0)
This paper list is powered by the following services:
What Schools Are Affiliated With Richard A. Caruana?
Richard A. Caruana is affiliated with the following schools:
