1.1k citations · 5.4k across the 59 of their papers we have counts for
8 papers · 1 filter
Interpretations are useful: penalizing explanations to align neural networks with prior knowledge
Laura Rieger, Chandan Singh, W. James Murdoch +1
For an explanation of a deep learning model to be effective, it must provide both insight into a model and suggest a corresponding action in order to achieve some objective. Too of…
Incremental causal effects
Dominik Rothenhäusler, Bin Yu
Causal evidence is needed to act and it is often enough for the evidence to point towards a direction of the effect of an action. For example, policymakers might be interested in e…
A Debiased MDI Feature Importance Measure for Random Forests
Xiao Li, Yu Wang, Sumanta Basu +2
Tree ensembles such as Random Forests have achieved impressive empirical success across a wide variety of applications. To understand how these models make predictions, people rout…
Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees
Summer Devlin, Chandan Singh, W. James Murdoch +1
Tree ensembles, such as random forests and AdaBoost, are ubiquitous machine learning models known for achieving strong predictive performance across a wide variety of domains. Howe…
Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients
Yuansi Chen, Raaz Dwivedi, Martin J. Wainwright +1
Hamiltonian Monte Carlo (HMC) is a state-of-the-art Markov chain Monte Carlo sampling algorithm for drawing samples from smooth probability densities over continuous spaces. We stu…
Unique Sharp Local Minimum in -minimization Complete Dictionary Learning
Yu Wang, Siqi Wu, Bin Yu
We study the problem of globally recovering a dictionary from a set of signals via -minimization. We assume that the signals are generated as i.i.d. random linear combinati…