429 citations · 450 across the 13 of their papers we have counts for
7 papers · 1 filter
A Consistent and Differentiable Lp Canonical Calibration Error Estimator
Teodora Popordanoska, Raphael Sayer, Matthew B. Blaschko
Calibrated probabilistic classifiers are models whose predicted probabilities can directly be interpreted as uncertainty estimates. It has been shown recently that deep neural netw…
Meta-Cal: Well-controlled Post-hoc Calibration by Ranking
Xingchen Ma, Matthew B. Blaschko
In many applications, it is desirable that a classifier not only makes accurate predictions, but also outputs calibrated posterior probabilities. However, many existing classifiers…
Additive Tree-Structured Conditional Parameter Spaces in Bayesian Optimization: A Novel Covariance Function and a Fast Implementation
Xingchen Ma, Matthew B. Blaschko
Bayesian optimization (BO) is a sample-efficient global optimization algorithm for black-box functions which are expensive to evaluate. Existing literature on model based optimizat…
Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization
Xingchen Ma, Matthew B. Blaschko
Bayesian optimization (BO) is a sample-efficient global optimization algorithm for black-box functions which are expensive to evaluate. Existing literature on model based optimizat…
Intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks
Amal Rannen Triki, Matthew B. Blaschko, Yoon Mo Jung +4
Objective: In this work, we perform margin assessment of human breast tissue from optical coherence tomography (OCT) images using deep neural networks (DNNs). This work simulates a…
A Convex Surrogate Operator for General Non-Modular Loss Functions
Jiaqian Yu, Matthew Blaschko
Empirical risk minimization frequently employs convex surrogates to underlying discrete loss functions in order to achieve computational tractability during optimization. However,…