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06/28/2023
UTOPIA: Universally Trainable Optimal Prediction Intervals Aggregation
Uncertainty quantification for prediction is an intriguing problem with ...
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02/12/2023
Deep Neural Networks for Nonparametric Interaction Models with Diverging Dimension
Deep neural networks have achieved tremendous success due to their repre...
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05/26/2022
Predictor-corrector algorithms for stochastic optimization under gradual distribution shift
Time-varying stochastic optimization problems frequently arise in machin...
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05/01/2022
Domain Adaptation meets Individual Fairness. And they get along
Many instances of algorithmic bias are caused by distributional shifts. ...
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10/26/2021
Post-processing for Individual Fairness
Post-processing in algorithmic fairness is a versatile approach for corr...
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05/25/2021
On robust learning in the canonical change point problem under heavy tailed errors in finite and growing dimensions
This paper presents a number of new findings about the canonical change ...
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02/22/2021
Regression discontinuity design: estimating the treatment effect with standard parametric rate
Regression discontinuity design models are widely used for the assessmen...
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12/14/2020
Outlier-Robust Optimal Transport
Optimal transport (OT) provides a way of measuring distances between dis...
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11/06/2020
There is no trade-off: enforcing fairness can improve accuracy
One of the main barriers to the broader adoption of algorithmic fairness...
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08/02/2020
Markovian And Non-Markovian Processes with Active Decision Making Strategies For Addressing The COVID-19 Pandemic
We study and predict the evolution of Covid-19 in six US states from the...
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06/19/2020
Two Simple Ways to Learn Individual Fairness Metrics from Data
Individual fairness is an intuitive definition of algorithmic fairness t...
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01/20/2020
Asymptotic normality of a linear threshold estimator in fixed dimension with near-optimal rate
Linear thresholding models postulate that the conditional distribution o...
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03/24/2019
