| Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI. | 0 | 0.34 | 2022 |
| Enhancing Simple Models by Exploiting What They Already Know | 0 | 0.34 | 2020 |
| Leveraging Simple Model Predictions for Enhancing its Performance. | 0 | 0.34 | 2019 |
| Generating Contrastive Explanations with Monotonic Attribute Functions. | 1 | 0.35 | 2019 |
| Beyond Backprop: Online Alternating Minimization with Auxiliary Variables. | 0 | 0.34 | 2019 |
| Detecting and Counting Panicles in Sorghum Images. | 0 | 0.34 | 2018 |
| Stochastic Gradient Descent with Biased but Consistent Gradient Estimators. | 0 | 0.34 | 2018 |
| Beyond Backprop: Alternating Minimization with co-Activation Memory. | 0 | 0.34 | 2018 |
| TIP: Typifying the Interpretability of Procedures. | 3 | 0.40 | 2017 |
| A Formal Framework to Characterize Interpretability of Procedures. | 1 | 0.36 | 2017 |
| Interpretable Policies for Dynamic Product Recommendations. | 3 | 0.43 | 2016 |
| Orthogonal Matching Pursuit for Sparse Quantile Regression | 4 | 0.42 | 2014 |
| Sparse Quantile Huber Regression for Efficient and Robust Estimation. | 1 | 0.42 | 2014 |
| Isotonic Recursive Partitioning | 1 | 0.36 | 2011 |
| Conditional Gradient Algorithmsfor Rank-One Matrix Approximationswith a Sparsity Constraint | 24 | 1.45 | 2011 |
| Convex approximations to sparse PCA via Lagrangian duality | 6 | 0.55 | 2011 |
| Decomposing Isotonic Regression for Efficiently Solving Large Problems. | 5 | 0.47 | 2010 |
| Support Vector Machine Classification with Indefinite Kernels | 43 | 1.50 | 2009 |
| A Cutting Plane Method for Multiple Kernel Learning | 0 | 0.34 | 2008 |
| Clustering and Feature Selection using Sparse Principal Component Analysis | 10 | 0.89 | 2007 |
