PALM: Machine Learning Explanations For Iterative Debugging

By Sanjay Krishnan, Eugene Wu. 2017

86 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems and Management, Safety, Robustness & Interpretability

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Citations in the corpus

DeltaGrad: Rapid retraining of machine learning models (2020, methodology): “For example, repeated retraining by removing different sub-sets of training data each time is essential in many existing data systems (Doshi-Velez & Kim, 2017; Krishnan & Wu, 2017) to understand the effect of those removed data over the model behavior.”
Principles and Practice of Explainable Machine Learning (2020, background): “Another perspective in simpliﬁcation is introduced in [48].”
Explainable Artificial Intelligence: a Systematic Review (2020, background): “Model Extraction [168] and Partition Aware Local Model (PALM) [169] utilize decision trees (DTs) to approximate complex models with the assumption that, as long as the approximation quality is good, the statistical properties of the complex model are reflected in the interpretable ones.”
PrIU: A Provenance-Based Approach for Incrementally Updating Regression Models (2020, methodology): “xtensive human subjects experiments in [45], as well the many references in these papers). The problem is being studied from several different perspectives (see Sec. 2). The data-driven approaches of [15, 35] discover factors of interpretability by performing repeated retraining of models using mult”
Explanation-Based Human Debugging of NLP Models: A Survey (2021, methodology): “This agrees with the correctability principles proposed by Kulesza et al. (2015) that the system should be actionable and reversible, honor user feedback, and show incremental changes.”
Machine Learning Testing: Survey, Landscapes and Horizons (2019, methodology): “Krishnan and Wu [166] presented PALM, a tool that explains a complex model with a two-part surrogate model: a meta-model that partitions the training data and a set of sub-models that approximate the patterns within each partition.”
Counterfactual Explanations for Machine Learning: A Review (2020, methodology): “Some approaches are model-agnostic, e.g. Golden Eye, PALM [59, 71, 116].”
Toward a System Building Agenda for Data Integration (and Data Science) (2018, background): “Over the past few decades, DI has received much attention (e.g., [37, 29, 31, 20, 34, 33, 6, 17, 39, 22, 23, 5, 8, 36, 15, 35, 4, 25, 38, 26, 32, 19, 2, 12, 11, 16, 2, 3]).”
Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges (2020, methodology): “Many IML methods are surrogate model approaches [89,75,72,95,34,10,18,61] and differ, e.”