167 citations · 405 across the 21 of their papers we have counts for
5 papers · 1 filter
Probing neural networks with t-SNE, class-specific projections and a guided tour
Christopher R. Hoyt, Art B. Owen
We use graphical methods to probe neural nets that classify images. Plots of t-SNE outputs at successive layers in a network reveal increasingly organized arrangement of the data p…
Cohort Shapley value for algorithmic fairness
Masayoshi Mase, Art B. Owen, Benjamin B. Seiler
Cohort Shapley value is a model-free method of variable importance grounded in game theory that does not use any unobserved and potentially impossible feature combinations. We use…
Quasi-Newton Quasi-Monte Carlo for variational Bayes
Sifan Liu, Art B. Owen
Many machine learning problems optimize an objective that must be measured with noise. The primary method is a first order stochastic gradient descent using one or more Monte Carlo…
Explaining black box decisions by Shapley cohort refinement
Masayoshi Mase, Art B. Owen, Benjamin Seiler
We introduce a variable importance measure to quantify the impact of individual input variables to a black box function. Our measure is based on the Shapley value from cooperative…
One Permutation Hashing for Efficient Search and Learning
Ping Li, Art Owen, Cun-Hui Zhang
Recently, the method of b-bit minwise hashing has been applied to large-scale linear learning and sublinear time near-neighbor search. The major drawback of minwise hashing is the…