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Found 16 papers, 9 papers with code
Prospector Heads: Generalized Feature Attribution for Large Models & Data
Feature attribution, the ability to localize regions of the input data that are relevant for classification, is an important capability for machine learning models in scientific and biomedical domains.
Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces
In this paper, we propose a novel approach that examines the relationship between individual fairness, adversarial robustness, and structural causal models in heterogeneous data spaces, particularly when dealing with discrete sensitive attributes.
Robustness Implies Fairness in Causal Algorithmic Recourse
Algorithmic recourse aims to disclose the inner workings of the black-box decision process in situations where decisions have significant consequences, by providing recommendations to empower beneficiaries to achieve a more favorable outcome.
On the Relationship Between Explanation and Prediction: A Causal View
Our work borrows tools from causal inference to systematically assay this relationship.
On the Adversarial Robustness of Causal Algorithmic Recourse
Algorithmic recourse seeks to provide actionable recommendations for individuals to overcome unfavorable classification outcomes from automated decision-making systems.
On the Fairness of Causal Algorithmic Recourse
Algorithmic fairness is typically studied from the perspective of predictions.
Scaling Guarantees for Nearest Counterfactual Explanations
Counterfactual explanations (CFE) are being widely used to explain algorithmic decisions, especially in consequential decision-making contexts (e. g., loan approval or pretrial bail).
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Machine learning is increasingly used to inform decision-making in sensitive situations where decisions have consequential effects on individuals' lives.
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
Recent work has discussed the limitations of counterfactual explanations to recommend actions for algorithmic recourse, and argued for the need of taking causal relationships between features into consideration.
Algorithmic Recourse: from Counterfactual Explanations to Interventions
As machine learning is increasingly used to inform consequential decision-making (e. g., pre-trial bail and loan approval), it becomes important to explain how the system arrived at its decision, and also suggest actions to achieve a favorable decision.
Model-Agnostic Counterfactual Explanations for Consequential Decisions
Predictive models are being increasingly used to support consequential decision making at the individual level in contexts such as pretrial bail and loan approval.
Deep Variational Sufficient Dimensionality Reduction
We consider the problem of sufficient dimensionality reduction (SDR), where the high-dimensional observation is transformed to a low-dimensional sub-space in which the information of the observations regarding the label variable is preserved.
SRP: Efficient class-aware embedding learning for large-scale data via supervised random projections
While stochastic approximation strategies have been explored for unsupervised dimensionality reduction to tackle this challenge, such approaches are not well-suited for accelerating computational speed for supervised dimensionality reduction.
JADE: Joint Autoencoders for Dis-Entanglement
The problem of feature disentanglement has been explored in the literature, for the purpose of image and video processing and text analysis.
A Summary Of The Kernel Matrix, And How To Learn It Effectively Using Semidefinite Programming
Kernel-based learning algorithms are widely used in machine learning for problems that make use of the similarity between object pairs.
Key-Value Memory Networks for Directly Reading Documents
Directly reading documents and being able to answer questions from them is an unsolved challenge.
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