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Bio
My research interests lie within the broad area of trustworthy machine learning. More specifically, my research spans explainable, fair, and robust ML. I am also very interested in reinforcement learning and causal inference.
I develop machine learning tools and techniques which enable human decision makers to make better decisions. More specifically, my research addresses the following fundamental questions pertaining to human and algorithmic decision-making:
How to build fair and interpretable models that can aid human decision-making?
How to ensure that models and their explanations are robust to adversarial attacks?
How to train and evaluate models in the presence of missing counterfactuals?
How to detect and correct underlying biases in human decisions and algorithmic predictions?
These questions have far-reaching implications in domains involving high-stakes decisions such as health care, policy, law, and business.
I develop machine learning tools and techniques which enable human decision makers to make better decisions. More specifically, my research addresses the following fundamental questions pertaining to human and algorithmic decision-making:
How to build fair and interpretable models that can aid human decision-making?
How to ensure that models and their explanations are robust to adversarial attacks?
How to train and evaluate models in the presence of missing counterfactuals?
How to detect and correct underlying biases in human decisions and algorithmic predictions?
These questions have far-reaching implications in domains involving high-stakes decisions such as health care, policy, law, and business.
Research Interests
Papers共 124 篇Author StatisticsCo-AuthorSimilar Experts
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Jiaqi Ma,Vivian Lai, Yiming Zhang,Chacha Chen, Paul Hamilton, Davor Ljubenkov,Himabindu Lakkaraju,Chenhao Tan
CoRR (2024)
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arxiv(2024)
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