| Why Data Scientists Prefer Glassbox Machine Learning: Algorithms, Differential Privacy, Editing and Bias Mitigation | 0 | 0.34 | 2022 |
| Differentially Private Estimation of Heterogeneous Causal Effects. | 0 | 0.34 | 2022 |
| Automated interpretable discovery of heterogeneous treatment effectiveness: A COVID-19 case study | 0 | 0.34 | 2022 |
| NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning | 0 | 0.34 | 2022 |
| Accuracy, Interpretability, and Differential Privacy via Explainable Boosting | 0 | 0.34 | 2021 |
| Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data | 0 | 0.34 | 2021 |
| How Interpretable and Trustworthy are GAMs? | 0 | 0.34 | 2021 |
| Summarize with Caution: Comparing Global Feature Attributions. | 0 | 0.34 | 2021 |
| Intelligible and Explainable Machine Learning: Best Practices and Practical Challenges | 0 | 0.34 | 2020 |
| Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning | 2 | 0.39 | 2020 |
| Friends Don't Let Friends Deploy Black-Box Models: The Importance of Intelligibility in Machine Learning | 0 | 0.34 | 2019 |
| Gamut - A Design Probe to Understand How Data Scientists Understand Machine Learning Models. | 11 | 0.50 | 2019 |
| Axiomatic Interpretability for Multiclass Additive Models. | 0 | 0.34 | 2019 |
| Efficient Forward Architecture Search. | 2 | 0.38 | 2019 |
| Transparent Model Distillation. | 0 | 0.34 | 2018 |
| Distill-And-Compare: Auditing Black-Box Models Using Transparent Model Distillation | 4 | 0.54 | 2018 |
| Data Diff: Interpretable, Executable Summaries of Changes in Distributions for Data Wrangling. | 3 | 0.44 | 2018 |
| Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models. | 0 | 0.34 | 2018 |
| Do Deep Convolutional Nets Really Need to be Deep and Convolutional? | 0 | 0.34 | 2017 |
| Interpretable & Explorable Approximations of Black Box Models. | 12 | 0.56 | 2017 |
| Detecting Bias in Black-Box Models Using Transparent Model Distillation. | 4 | 0.43 | 2017 |
| Do Deep Convolutional Nets Really Need to be Deep and Convolutional? | 3 | 0.36 | 2017 |
| Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration. | 14 | 0.69 | 2017 |
| A Dual Embedding Space Model for Document Ranking. | 17 | 0.87 | 2016 |
| Detecting Migrating Birds At Night | 1 | 0.36 | 2016 |
| Improving Document Ranking with Dual Word Embeddings. | 40 | 1.38 | 2016 |
| Discovering Blind Spots of Predictive Models: Representations and Policies for Guided Exploration. | 1 | 0.37 | 2016 |
| Do Deep Convolutional Nets Really Need to be Deep (Or Even Convolutional)? | 29 | 1.02 | 2016 |
| Analysis of deep neural networks with the extended data Jacobian matrix | 2 | 0.38 | 2016 |
| Implicit Preference Labels for Learning Highly Selective Personalized Rankers | 3 | 0.38 | 2015 |
| Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission | 121 | 4.93 | 2015 |
| Compressing LSTMs into CNNs | 2 | 0.39 | 2015 |
| Structured labeling for facilitating concept evolution in machine learning | 29 | 1.01 | 2014 |
| Gauss meets Canadian traveler: shortest-path problems with correlated natural dynamics | 1 | 0.35 | 2014 |
| Do Deep Nets Really Need to be Deep? | 230 | 9.55 | 2013 |
| Clustering: probably approximately useless? | 0 | 0.34 | 2013 |
| Introduction to the Special Issue ACM SIGKDD 2012 | 0 | 0.34 | 2013 |
| Using Multiple Samples to Learn Mixture Models. | 2 | 0.44 | 2013 |
| Learning Likely Locations. | 2 | 0.39 | 2013 |
| Accurate intelligible models with pairwise interactions | 16 | 1.96 | 2013 |
| Intelligible models for classification and regression | 49 | 3.20 | 2012 |
| Learning speaker, addressee and overlap detection models from multimodal streams | 5 | 0.48 | 2012 |
| Inductive Transfer for Bayesian Network Structure Learning. | 0 | 0.34 | 2012 |
| Special issue on best of SIGKDD 2011 | 0 | 0.34 | 2012 |
| Bagging gradient-boosted trees for high precision, low variance ranking models | 72 | 2.23 | 2011 |
| Detecting and Interpreting Variable Interactions in Observational Ornithology Data | 5 | 0.53 | 2009 |
| On Feature Selection, Bias-Variance, and Bagging | 10 | 0.64 | 2009 |
| An empirical evaluation of supervised learning in high dimensions | 135 | 5.01 | 2008 |
| Efficient architectural design space exploration via predictive modeling | 33 | 1.17 | 2008 |
| Detecting statistical interactions with additive groves of trees | 2 | 0.58 | 2008 |
