Search Results for author:
Found 7 papers, 1 papers with code
Error Discovery by Clustering Influence Embeddings
We present a method for identifying groups of test examples -- slices -- on which a model under-performs, a task now known as slice discovery.
XAIR: A Framework of Explainable AI in Augmented Reality
The framework was based on a multi-disciplinary literature review of XAI and HCI research, a large-scale survey probing 500+ end-users' preferences for AR-based explanations, and three workshops with 12 experts collecting their insights about XAI design in AR.
Bias Mitigation Framework for Intersectional Subgroups in Neural Networks
We propose a fairness-aware learning framework that mitigates intersectional subgroup bias associated with protected attributes.
Extreme Dimension Reduction for Handling Covariate Shift
In the covariate shift learning scenario, the training and test covariate distributions differ, so that a predictor's average loss over the training and test distributions also differ.
Causal Falling Rule Lists
A causal falling rule list (CFRL) is a sequence of if-then rules that specifies heterogeneous treatment effects, where (i) the order of rules determines the treatment effect subgroup a subject belongs to, and (ii) the treatment effect decreases monotonically down the list.
Modeling Recovery Curves With Application to Prostatectomy
We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event.
Falling Rule Lists
Falling rule lists are classification models consisting of an ordered list of if-then rules, where (i) the order of rules determines which example should be classified by each rule, and (ii) the estimated probability of success decreases monotonically down the list.
