Infinite Mixtures of Gaussian Process Experts

By Carl E. Rasmussen, Z. Ghahramani. 2001

1,103 citations recorded by the source corpus.

Subfields: Probabilistic & Bayesian Methods

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Citations in the corpus

Rationalizing Neural Predictions (2016, background): “Examples include extracting “if-then” rules (Thrun, 1995) and decision trees (Craven and Shavlik, 1996) from trained networks.”
Stealing Machine Learning Models via Prediction APIs (2016, methodology): “In a non-malicious setting, improper model extraction techniques have been applied for interpreting [2, 19, 52] and compressing [16, 27] complex neural networks.”
Local Rule-Based Explanations of Black Box Decision Systems (2018, methodology): “ion system. Generally, these methods are designed for explaining specific black box models, i.e., they are not black box agnostic. Decision trees have been adopted to globally explain neural networks [9, 22] and tree ensembles [18, 34]. Classification rules have been widely used to explain neural ne”
Anchors: High-Precision Model-Agnostic Explanations (2018, background): “a decision tree (Craven and Shavlik 1996) or a set of rules (Sanchez et al.”
A Survey of Methods for Explaining Black Box Models (2018, methodology): “Trepan queries the neural network b to induce a decision tree ACM Computing Surveys, Vol. 51, No. 5, Article 93.”
Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation (2017, methodology): “We assess fidelity, the notion that the student model should match the teacher model (Craven and Shavlik 1995), using the closeness of the student model to the teacher’s predictions.”
Interpretable Two-level Boolean Rule Learning for Classification (2015, background): “The first is an integer program whose objective function is a combination of the total number of errors and the total number of features used in the rule.”
Learning Fair Rule Lists (2019, methodology): “Black-box explanations techniques consist in providing a posteriori explanations that are either global [17, 41] or local approximations [53] of the black-box model.”
Causal Interpretability for Machine Learning - Problems, Methods and Evaluation (2020, background): “Traditional Interpretability Interpretable Models: [106], [103], [65], [102], [66], [105], [51], [13], [40] Causal Interpretability Model-based: [77], [38], [12], [8], [112], [81], [7], Example-based: [35], [46], [39], [101], [76], [86], [73], [62], [61], [33], [34] Post-hoc: [48], [89], [67], [93],”
A modeling framework for adaptive lifelong learning with transfer and savings through gating in the prefrontal cortex (2020, neutral): “1a) widely found in machine learning applications [19],[20],[21],[22].”
A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning (2020, positive): “The joint distribution modeled by each component can be decomposed as p(x, y|z) = p(y|x, z)p(x|z) (Rasmussen & Ghahramani, 2002; Shahbaba & Neal, 2009).”
Interpretable Neural Predictions with Differentiable Binary Variables (2019, methodology): “For example, Thrun (1995) extract if-then rules, while Craven and Shavlik (1996)”
XNAS: Neural Architecture Search with Expert Advice (2019, neutral): “Mentions of Experts in deep learning [32, 52, 11, 1] literature go decades back [18, 5], typically combining models as separable experts sub-models.”
Efficient Search for Diverse Coherent Explanations (2019, methodology): “These approximate models are either fitted globally (Craven and Shavlik, 1996; Martens et al., 2007; Sanchez et al., 2015) over the entire space of valid datapoints, or as a local approximation (Lundberg and Lee, 2017; Montavon et al.”