Search Results for author: Vikas C. Raykar

Found 5 papers, 0 papers with code

TsSHAP: Robust model agnostic feature-based explainability for time series forecasting

no code implementations22 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.

Time Series Time Series Forecasting

Ranking annotators for crowdsourced labeling tasks

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.

Automatic online tuning for fast Gaussian summation

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.

BIG-bench Machine Learning

On Ranking in Survival Analysis: Bounds on the Concordance Index

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.

Survival Analysis

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