no code implementations • 22 Mar 2023 • Vikas C. Raykar, Arindam Jati, Sumanta Mukherjee, Nupur Aggarwal, Kanthi Sarpatwar, Giridhar Ganapavarapu, Roman Vaculin
The explanations are in terms of the SHAP values obtained by applying the TreeSHAP algorithm on a surrogate model that learns a mapping between the interpretable feature space and the forecast of the black-box model.
no code implementations • 1 Dec 2015 • Amrita Saha, Sathish Indurthi, Shantanu Godbole, Subendhu Rongali, Vikas C. Raykar
We describe the problem of aggregating the label predictions of diverse classifiers using a class taxonomy.
no code implementations • NeurIPS 2011 • Vikas C. Raykar, Shipeng Yu
With the advent of crowdsourcing services it has become quite cheap and reasonably effective to get a dataset labeled by multiple annotators in a short amount of time.
no code implementations • NeurIPS 2008 • Vlad I. Morariu, Balaji V. Srinivasan, Vikas C. Raykar, Ramani Duraiswami, Larry S. Davis
To solve the second problem, we present an online tuning approach that results in a black box method that automatically chooses the evaluation method and its parameters to yield the best performance for the input data, desired accuracy, and bandwidth.
no code implementations • NeurIPS 2007 • Harald Steck, Balaji Krishnapuram, Cary Dehing-Oberije, Philippe Lambin, Vikas C. Raykar
In contrast, the standard approach to \emph{learning} the popular proportional hazard (PH) model is based on Cox's partial likelihood.