Actionable Recourse in Linear Classification

18 September 2018

Yang Liu

Papers citing "Actionable Recourse in Linear Classification"

50 / 115 papers shown

Title
CLEAR: Generative Counterfactual Explanations on Graphs Jing Ma Ruocheng Guo Saumitra Mishra Aidong Zhang Jundong Li CML OOD 30 53 0 16 Oct 2022
Local and Regional Counterfactual Rules: Summarized and Robust Recourses Salim I. Amoukou Nicolas Brunel 26 0 0 29 Sep 2022
Counterfactual Explanations Using Optimization With Constraint Learning Donato Maragno Tabea E. Rober Ilker Birbil CML 53 10 0 22 Sep 2022
RAGUEL: Recourse-Aware Group Unfairness Elimination Aparajita Haldar Teddy Cunningham Hakan Ferhatosmanoglu FaML 38 3 0 30 Aug 2022
Alterfactual Explanations -- The Relevance of Irrelevance for Explaining AI Systems Silvan Mertes Christina Karle Tobias Huber Katharina Weitz Ruben Schlagowski Elisabeth André 21 12 0 19 Jul 2022
Use-Case-Grounded Simulations for Explanation Evaluation Valerie Chen Nari Johnson Nicholay Topin Gregory Plumb Ameet Talwalkar FAtt ELM 22 24 0 05 Jun 2022
Attribution-based Explanations that Provide Recourse Cannot be Robust H. Fokkema R. D. Heide T. Erven FAtt 44 18 0 31 May 2022
Gradient-based Counterfactual Explanations using Tractable Probabilistic Models Xiaoting Shao Kristian Kersting BDL 22 1 0 16 May 2022
Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAI Greta Warren Mark T. Keane R. Byrne CML 27 22 0 21 Apr 2022
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse Martin Pawelczyk Teresa Datta Johannes van-den-Heuvel Gjergji Kasneci Himabindu Lakkaraju 19 38 0 13 Mar 2022
LIMREF: Local Interpretable Model Agnostic Rule-based Explanations for Forecasting, with an Application to Electricity Smart Meter Data Dilini Sewwandi Rajapaksha Christoph Bergmeir AI4TS 14 16 0 15 Feb 2022
Causal Explanations and XAI Sander Beckers CML XAI 21 34 0 31 Jan 2022
Counterfactual Plans under Distributional Ambiguity N. Bui D. Nguyen Viet Anh Nguyen 59 24 0 29 Jan 2022
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis Giovanni De Toni Bruno Lepri Andrea Passerini CML 25 13 0 18 Jan 2022
On the Adversarial Robustness of Causal Algorithmic Recourse Ricardo Dominguez-Olmedo Amir-Hossein Karimi Bernhard Schölkopf 46 63 0 21 Dec 2021
On Two XAI Cultures: A Case Study of Non-technical Explanations in Deployed AI System Helen Jiang Erwen Senge 25 7 0 02 Dec 2021
Counterfactual Explanations via Latent Space Projection and Interpolation Brian Barr Matthew R. Harrington Samuel Sharpe Capital One BDL 28 10 0 02 Dec 2021
Counterfactual Shapley Additive Explanations Emanuele Albini Jason Long Danial Dervovic Daniele Magazzeni 26 49 0 27 Oct 2021
Deep Neural Networks and Tabular Data: A Survey V. Borisov Tobias Leemann Kathrin Seßler Johannes Haug Martin Pawelczyk Gjergji Kasneci LMTD 27 646 0 05 Oct 2021
Counterfactual Instances Explain Little Adam White Artur Garcez CML 27 5 0 20 Sep 2021
Self-learn to Explain Siamese Networks Robustly Chao Chen Yifan Shen Guixiang Ma Xiangnan Kong S. Rangarajan Xi Zhang Sihong Xie 40 5 0 15 Sep 2021
Model Explanations via the Axiomatic Causal Lens Gagan Biradar Vignesh Viswanathan Yair Zick XAI CML 25 1 0 08 Sep 2021
Reasoning about Counterfactuals and Explanations: Problems, Results and Directions Leopoldo Bertossi LRM 19 0 0 25 Aug 2021
Answer-Set Programs for Reasoning about Counterfactual Interventions and Responsibility Scores for Classification Leopoldo Bertossi G. Reyes 22 9 0 21 Jul 2021
A Causal Perspective on Meaningful and Robust Algorithmic Recourse Gunnar Konig Timo Freiesleben Moritz Grosse-Wentrup 27 16 0 16 Jul 2021
Model Transferability With Responsive Decision Subjects Yatong Chen Zeyu Tang Kun Zhang Yang Liu 38 10 0 13 Jul 2021
Counterfactual Explanations for Arbitrary Regression Models Thomas Spooner Danial Dervovic Jason Long Jon Shepard Jiahao Chen Daniele Magazzeni 19 26 0 29 Jun 2021
How Well do Feature Visualizations Support Causal Understanding of CNN Activations? Roland S. Zimmermann Judy Borowski Robert Geirhos Matthias Bethge Thomas S. A. Wallis Wieland Brendel FAtt 39 31 0 23 Jun 2021
Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects Julius von Kügelgen N. Agarwal Jakob Zeitler Afsaneh Mastouri Bernhard Schölkopf CML 17 2 0 22 Jun 2021
Rational Shapley Values David S. Watson 23 20 0 18 Jun 2021
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis Martin Pawelczyk Chirag Agarwal Shalmali Joshi Sohini Upadhyay Himabindu Lakkaraju AAML 24 51 0 18 Jun 2021
Optimal Counterfactual Explanations in Tree Ensembles Axel Parmentier Thibaut Vidal 19 54 0 11 Jun 2021
Fair Normalizing Flows Mislav Balunović Anian Ruoss Martin Vechev AAML 13 36 0 10 Jun 2021
Leveraging Sparse Linear Layers for Debuggable Deep Networks Eric Wong Shibani Santurkar A. Madry FAtt 20 88 0 11 May 2021
Optimal Counterfactual Explanations for Scorecard modelling Guillermo Navas-Palencia 11 9 0 17 Apr 2021
Consequence-aware Sequential Counterfactual Generation Philip Naumann Eirini Ntoutsi OffRL 17 24 0 12 Apr 2021
Interpretable Machine Learning: Moving From Mythos to Diagnostics Valerie Chen Jeffrey Li Joon Sik Kim Gregory Plumb Ameet Talwalkar 32 29 0 10 Mar 2021
Strategic Classification Made Practical Sagi Levanon Nir Rosenfeld 37 55 0 02 Mar 2021
Contrastive Explanations for Model Interpretability Alon Jacovi Swabha Swayamdipta Shauli Ravfogel Yanai Elazar Yejin Choi Yoav Goldberg 35 95 0 02 Mar 2021
Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms Miguel Á. Carreira-Perpiñán Suryabhan Singh Hada CML AAML 16 33 0 01 Mar 2021
Towards Robust and Reliable Algorithmic Recourse Sohini Upadhyay Shalmali Joshi Himabindu Lakkaraju 22 108 0 26 Feb 2021
Towards a Unified Framework for Fair and Stable Graph Representation Learning Chirag Agarwal Himabindu Lakkaraju Marinka Zitnik 24 157 0 25 Feb 2021
Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs Harini Suresh Steven R. Gomez K. Nam Arvind Satyanarayan 34 126 0 24 Jan 2021
Declarative Approaches to Counterfactual Explanations for Classification Leopoldo Bertossi 32 17 0 15 Nov 2020
Incorporating Interpretable Output Constraints in Bayesian Neural Networks Wanqian Yang Lars Lorch Moritz Graule Himabindu Lakkaraju Finale Doshi-Velez UQCV BDL 17 16 0 21 Oct 2020
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges Christoph Molnar Giuseppe Casalicchio B. Bischl AI4TS AI4CE 15 397 0 19 Oct 2020
Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses Kaivalya Rawal Himabindu Lakkaraju 27 11 0 15 Sep 2020
The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples Timo Freiesleben GAN 33 62 0 11 Sep 2020
Model extraction from counterfactual explanations Ulrich Aivodji Alexandre Bolot Sébastien Gambs MIACV MLAU 27 51 0 03 Sep 2020
On Counterfactual Explanations under Predictive Multiplicity Martin Pawelczyk Klaus Broelemann Gjergji Kasneci 22 85 0 23 Jun 2020