• Publications
  • Influence
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
TLDR
LIME is proposed, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning aninterpretable model locally varound the prediction. Expand
Anchors: High-Precision Model-Agnostic Explanations
We introduce a novel model-agnostic system that explains the behavior of complex models with high-precision rules called anchors, representing local, “sufficient” conditions for predictions. WeExpand
Model-Agnostic Interpretability of Machine Learning
TLDR
This paper argues for explaining machine learning predictions using model-agnostic approaches, treating the machine learning models as black-box functions, which provide crucial flexibility in the choice of models, explanations, and representations, improving debugging, comparison, and interfaces for a variety of users and models. Expand
Semantically Equivalent Adversarial Rules for Debugging NLP models
TLDR
This work presents semantically equivalent adversaries (SEAs) – semantic-preserving perturbations that induce changes in the model’s predictions that induce adversaries on many instances that are extremely similar semantically. Expand
Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance
TLDR
This work proposes anchor-LIME (aLIME), a model-agnostic technique that produces high-precision rule-based explanations for which the coverage boundaries are very clear and is compared to linear LIME with simulated experiments, and demonstrates the flexibility of aLIME with qualitative examples from a variety of domains and tasks. Expand
Pareto-efficient hybridization for multi-objective recommender systems
TLDR
A hybrid recommendation approach that combines existing algorithms which differ in their level of accuracy, novelty and diversity, and allows for adjusting the compromise between accuracy, diversity and novelty, so that the recommendation emphasis can be adjusted dynamically according to the needs of different users. Expand
Multiobjective Pareto-Efficient Approaches for Recommender Systems
TLDR
The proposed Pareto-efficient approaches are effective in suggesting items that are likely to be simultaneously accurate, diverse, and novel and discussed scenarios where the system achieves high levels of diversity and novelty without compromising its accuracy. Expand
Errudite: Scalable, Reproducible, and Testable Error Analysis
TLDR
This paper codifies model and task agnostic principles for informative error analysis, and presents Errudite, an interactive tool for better supporting this process, and enables users to perform high quality and reproducible error analyses with less effort. Expand
Validity and reproducibility of the revised oral assessment guide applied by community health workers.
TLDR
When used by trained CHWs, the ROAG is a tool with high sensitivity and specificity to assess voice, swallowing, tongue and teeth/dentures and it can efficiently detect patients showing no alteration in lips, saliva, mucosa and gums. Expand
...
1
2
...