black-box model

Explanations for Monotonic Classifiers. ICML 2021 Explanations for Occluded Images ICCV 2021 Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model JMLR 2021 Learning Groupwise Explanations for Black-Box Models IJCAI 2021 Provably efficient, succinct, and precise explanations NeurIPS 2021 Right for Better Reasons: Training Differentiable Models by Constraining their Influence Functions AAAI 2021 Understanding Pre-Editing for Black-Box Neural Machine Translation EACL 2021 Zero-Shot Knowledge Distillation from a Decision-Based Black-Box Model ICML 2021 Black-Box Ripper: Copying black-box models using generative evolutionary algorithms NeurIPS 2020 Domain Adaptation of Thai Word Segmentation Models using Stacked Ensemble EMNLP 2020 Interpretable Companions for Black-Box Models AISTATS 2020 Neural Networks Are More Productive Teachers Than Human Raters: Active Mixup for Data-Efficient Knowledge Distillation From a Blackbox Model CVPR 2020 On Second-Order Group Influence Functions for Black-Box Predictions ICML 2020 REST: Performance Improvement of a Black Box Model via RL-Based Spatial Transformation AAAI 2020 Robust and Stable Black Box Explanations ICML 2020 Towards Interpretation of Pairwise Learning AAAI 2020 Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources ICML 2020 Transparency Promotion with Model-Agnostic Linear Competitors ICML 2020 What went wrong and when? Instance-wise feature importance for time-series black-box models NeurIPS 2020 Collective Model Fusion for Multiple Black-Box Experts ICML 2019 Demystifying Black-box Models with Symbolic Metamodels NeurIPS 2019 Fairwashing: the risk of rationalization ICML 2019 Gaining Free or Low-Cost Interpretability with Interpretable Partial Substitute ICML 2019 Interpreting Black Box Predictions using Fisher Kernels AISTATS 2019 Knockoff Nets: Stealing Functionality of Black-Box Models CVPR 2019