Search Results for author: Thomas Wies

Found 4 papers, 1 papers with code

Inverse-Weighted Survival Games

1 code implementation NeurIPS 2021 Xintian Han, Mark Goldstein, Aahlad Puli, Thomas Wies, Adler J Perotte, Rajesh Ranganath

When the loss is proper, we show that the games always have the true failure and censoring distributions as a stationary point.

Binary Classification Survival Analysis

Beyond The Text: Analysis of Privacy Statements through Syntactic and Semantic Role Labeling

no code implementations1 Oct 2020 Yan Shvartzshnaider, Ananth Balashankar, Vikas Patidar, Thomas Wies, Lakshminarayanan Subramanian

This paper formulates a new task of extracting privacy parameters from a privacy policy, through the lens of Contextual Integrity, an established social theory framework for reasoning about privacy norms.

Question Answering Semantic Role Labeling

RECIPE: Applying Open Domain Question Answering to Privacy Policies

no code implementations WS 2018 Yan Shvartzshanider, Ananth Balashankar, Thomas Wies, Lakshminarayanan Subramanian

We describe our experiences in using an open domain question answering model (Chen et al., 2017) to evaluate an out-of-domain QA task of assisting in analyzing privacy policies of companies.

Descriptive Open-Domain Question Answering +1

Learning Invariants using Decision Trees

no code implementations20 Jan 2015 Siddharth Krishna, Christian Puhrsch, Thomas Wies

This is a standard problem in machine learning: given a sample of good and bad points, one is asked to find a classifier that generalizes from the sample and separates the two sets.

Binary Classification

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