Search Results for author:
Found 15 papers, 3 papers with code
Auditing Predictive Models for Intersectional Biases
Predictive models that satisfy group fairness criteria in aggregate for members of a protected class, but do not guarantee subgroup fairness, could produce biased predictions for individuals at the intersection of two or more protected classes.
Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness
In this paper, we develop a new criterion, "insufficiently justified disparate impact" (IJDI), for assessing whether recommendations (binarized predictions) made by an algorithmic decision support tool are fair.
Provable Detection of Propagating Sampling Bias in Prediction Models
We provide a theoretical analysis of how a specific form of data bias, differential sampling bias, propagates from the data stage to the prediction stage.
Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs
We propose a new approach, the calibrated nonparametric scan statistic (CNSS), for more accurate detection of anomalous patterns in large-scale, real-world graphs.
Positional Encoder Graph Neural Networks for Geographic Data
Graph neural networks (GNNs) provide a powerful and scalable solution for modeling continuous spatial data.
SPATE-GAN: Improved Generative Modeling of Dynamic Spatio-Temporal Patterns with an Autoregressive Embedding Loss
In this study, we propose a novel loss objective combined with COT-GAN based on an autoregressive embedding to reinforce the learning of spatio-temporal dynamics.
Policing Chronic and Temporary Hot Spots of Violent Crime: A Controlled Field Experiment
Hot-spot-based policing programs aim to deter crime through increased proactive patrols at high-crime locations.
Auxiliary-task learning for geographic data with autoregressive embeddings
In this study, we propose SXL, a method for embedding information on the autoregressive nature of spatial data directly into the learning process using auxiliary tasks.
Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction
We provide a model-agnostic formalization of change surfaces, illustrating how they can provide variable, heterogeneous, and non-monotonic rates of change across multiple dimensions.
Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data
We introduce methods for identifying anomalous patterns in non-iid data by combining Gaussian processes with novel log-likelihood ratio statistic and subset scanning techniques.
Efficient Discovery of Heterogeneous Quantile Treatment Effects in Randomized Experiments via Anomalous Pattern Detection
In the recent literature on estimating heterogeneous treatment effects, each proposed method makes its own set of restrictive assumptions about the intervention's effects and which subpopulations to explicitly estimate.
Machine Learning for Drug Overdose Surveillance
We describe two recently proposed machine learning approaches for discovering emerging trends in fatal accidental drug overdoses.
Graph Structure Learning from Unlabeled Data for Event Detection
Processes such as disease propagation and information diffusion often spread over some latent network structure which must be learned from observation.
Identifying Significant Predictive Bias in Classifiers
We present a novel subset scan method to detect if a probabilistic binary classifier has statistically significant bias -- over or under predicting the risk -- for some subgroup, and identify the characteristics of this subgroup.
Semantic Scan: Detecting Subtle, Spatially Localized Events in Text Streams
Many methods have been proposed for detecting emerging events in text streams using topic modeling.
