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Found 12 papers, 5 papers with code
Two Simple Ways to Learn Individual Fairness Metric from Data
Individual fairness was proposed to address some of the shortcomings of group fairness.
Estimating Fréchet bounds for validating programmatic weak supervision
We develop methods for estimating Fr\'echet bounds on (possibly high-dimensional) distribution classes in which some variables are continuous-valued.
Conditional independence testing under misspecified inductive biases
Conditional independence (CI) testing is a fundamental and challenging task in modern statistics and machine learning.
Understanding new tasks through the lens of training data via exponential tilting
However, it is conceivable that the training data can be reweighted to be more representative of the new (target) task.
Predictor-corrector algorithms for stochastic optimization under gradual distribution shift
Time-varying stochastic optimization problems frequently arise in machine learning practice (e. g. gradual domain shift, object tracking, strategic classification).
Estimation of a score-explained non-randomized treatment effect in fixed and high dimensions
In this paper, we present a new model coined SCENTS: Score Explained Non-Randomized Treatment Systems, and a corresponding method that allows estimation of the treatment effect at $\sqrt{n}$ rate in the presence of fairly general forms of confoundedness, when the `score' variable on whose basis treatment is assigned can be explained via certain feature measurements of the individuals under study.
Methodology Statistics Theory Statistics Theory
A semi-parametric model for target localization in distributed systems
In this paper, we adopt a nonparametric approach that only assumes that the signal is nonincreasing as function of the distance between the sensor and the target.
Disaster Response
Methodology
Two Simple Ways to Learn Individual Fairness Metrics from Data
Individual fairness is an intuitive definition of algorithmic fairness that addresses some of the drawbacks of group fairness.
Minimax optimal approaches to the label shift problem in non-parametric settings
We study the minimax rates of the label shift problem in non-parametric classification.
Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions
We consider the task of meta-analysis in high-dimensional settings in which the data sources are similar but non-identical.
Inference In High-dimensional Single-Index Models Under Symmetric Designs
The problem of statistical inference for regression coefficients in a high-dimensional single-index model is considered.
Statistics Theory Other Statistics Statistics Theory
Change Point Estimation in a Dynamic Stochastic Block Model
Once the change point is identified, in the second step, all network data before and after it are used together with a clustering algorithm to obtain the corresponding community structures and subsequently estimate the generating stochastic block model parameters.
