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Found 11 papers, 9 papers with code
Towards Non-Adversarial Algorithmic Recourse
We subsequently investigate how different components in the objective functions, e. g., the machine learning model or cost function used to measure distance, determine whether the outcome can be considered an adversarial example or not.
Adapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes
The GAEs minimise the reconstruction error between the original graph and its learned representation during training.
Gaussian Membership Inference Privacy
In particular, we derive a parametric family of $f$-MIP guarantees that we refer to as $\mu$-Gaussian Membership Inference Privacy ($\mu$-GMIP) by theoretically analyzing likelihood ratio-based membership inference attacks on stochastic gradient descent (SGD).
I Prefer not to Say: Protecting User Consent in Models with Optional Personal Data
In this work, we show that the decision not to share data can be considered as information in itself that should be protected to respect users' privacy.
Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanations
A better understanding of the needs of XAI users, as well as human-centered evaluations of explainable models are both a necessity and a challenge.
Explainable Artificial Intelligence (XAI)
Explainable Models
+2
Language Models are Realistic Tabular Data Generators
Tabular data is among the oldest and most ubiquitous forms of data.
On the Trade-Off between Actionable Explanations and the Right to be Forgotten
Thus, our work raises fundamental questions about the compatibility of "the right to an actionable explanation" in the context of the "right to be forgotten", while also providing constructive insights on the determining factors of recourse robustness.
When are Post-hoc Conceptual Explanations Identifiable?
Interest in understanding and factorizing learned embedding spaces through conceptual explanations is steadily growing.
A Consistent and Efficient Evaluation Strategy for Attribution Methods
With a variety of local feature attribution methods being proposed in recent years, follow-up work suggested several evaluation strategies.
Distribution Preserving Multiple Hypotheses Prediction for Uncertainty Modeling
Many supervised machine learning tasks, such as future state prediction in dynamical systems, require precise modeling of a forecast's uncertainty.
Deep Neural Networks and Tabular Data: A Survey
Moreover, we discuss deep learning approaches for generating tabular data, and we also provide an overview over strategies for explaining deep models on tabular data.
