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Found 10 papers, 0 papers with code
Efficient Representation of the Activation Space in Deep Neural Networks
The representations of the activation space of deep neural networks (DNNs) are widely utilized for tasks like natural language processing, anomaly detection and speech recognition.
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.
EnsembleIV: Creating Instrumental Variables from Ensemble Learners for Robust Statistical Inference
Despite increasing popularity in empirical studies, the integration of machine learning generated variables into regression models for statistical inference suffers from the measurement error problem, which can bias estimation and threaten the validity of inferences.
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.
Pattern Detection in the Activation Space for Identifying Synthesized Content
Generative Adversarial Networks (GANs) have recently achieved unprecedented success in photo-realistic image synthesis from low-dimensional random noise.
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
The random forest algorithm performs best when comprised of a set of trees that are individually accurate in their predictions, yet which also make 'different' mistakes, i. e., have weakly correlated prediction errors.
Deep Mining: Detecting Anomalous Patterns in Neural Network Activations with Subset Scanning
This work introduces ``Subset Scanning methods from the anomalous pattern detection domain to the task of detecting anomalous inputs to neural networks.
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.
