Search Results for author: Vishwali Mhasawade

Found 6 papers, 1 papers with code

Causal Multi-Level Fairness

no code implementations14 Oct 2020 Vishwali Mhasawade, Rumi Chunara

While work in algorithmic fairness to-date has primarily focused on addressing discrimination due to individually linked attributes, social science research elucidates how some properties we link to individuals can be conceptualized as having causes at macro (e. g. structural) levels, and it may be important to be fair to attributes at multiple levels.

Causal Inference Fairness

Machine Learning in Population and Public Health

no code implementations21 Jul 2020 Vishwali Mhasawade, Yuan Zhao, Rumi Chunara

Research in population and public health focuses on the mechanisms between different cultural, social, and environmental factors and their effect on the health, of not just individuals, but communities as a whole.

BIG-bench Machine Learning

Fairness Violations and Mitigation under Covariate Shift

no code implementations2 Nov 2019 Harvineet Singh, Rina Singh, Vishwali Mhasawade, Rumi Chunara

We study the problem of learning fair prediction models for unseen test sets distributed differently from the train set.

Domain Adaptation Fairness +1

Population-aware Hierarchical Bayesian Domain Adaptation via Multiple-component Invariant Learning

1 code implementation24 Aug 2019 Vishwali Mhasawade, Nabeel Abdur Rehman, Rumi Chunara

Based on sources of stability in the model, we posit that for human-sourced data and health prediction tasks we can combine environment and population information in a novel population-aware hierarchical Bayesian domain adaptation framework that harnesses multiple invariant components through population attributes when needed.

Domain Adaptation

Population-aware Hierarchical Bayesian Domain Adaptation

no code implementations21 Nov 2018 Vishwali Mhasawade, Nabeel Abdur Rehman, Rumi Chunara

Population attributes are essential in health for understanding who the data represents and precision medicine efforts.

Domain Adaptation

Neural Networks and Quantifier Conservativity: Does Data Distribution Affect Learnability?

no code implementations15 Sep 2018 Vishwali Mhasawade, Ildikó Emese Szabó, Melanie Tosik, Sheng-Fu Wang

In this work, we investigate whether the learnability bias exhibited by children is in part due to the distribution of quantifiers in natural language.

Language Acquisition

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