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Explaining Groups of Points in Low-Dimensional Representations
v1v2v3 (latest)

Explaining Groups of Points in Low-Dimensional Representations

International Conference on Machine Learning (ICML), 2020
3 March 2020
Gregory Plumb
Jonathan Terhorst
S. Sankararaman
Ameet Talwalkar
ArXiv (abs)PDFHTML

Papers citing "Explaining Groups of Points in Low-Dimensional Representations"

15 / 15 papers shown
POLygraph: Polish Fake News Dataset
POLygraph: Polish Fake News Dataset
Daniel Dzienisiewicz
Filip Graliñski
Piotr Jabłoński
Marek Kubis
Paweł Skórzewski
Piotr Wierzchoñ
242
2
0
01 Jul 2024
VERA: Generating Visual Explanations of Two-Dimensional Embeddings via
  Region Annotation
VERA: Generating Visual Explanations of Two-Dimensional Embeddings via Region Annotation
P. G. Policar
Blaž Zupan
144
3
0
07 Jun 2024
Unifying Perspectives: Plausible Counterfactual Explanations on Global, Group-wise, and Local Levels
Unifying Perspectives: Plausible Counterfactual Explanations on Global, Group-wise, and Local Levels
Patryk Wielopolski
Oleksii Furman
Łukasz Lenkiewicz
Jerzy Stefanowski
Maciej Ziȩba
434
6
0
27 May 2024
A Two-Stage Algorithm for Cost-Efficient Multi-instance Counterfactual
  Explanations
A Two-Stage Algorithm for Cost-Efficient Multi-instance Counterfactual Explanations
André Artelt
Andreas Gregoriades
350
6
0
02 Mar 2024
Distributional Counterfactual Explanations With Optimal Transport
Distributional Counterfactual Explanations With Optimal TransportInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Lei You
Lele Cao
Mattias Nilsson
Bo Zhao
Lei Lei
OTOffRL
739
4
0
23 Jan 2024
How Well Do Feature-Additive Explainers Explain Feature-Additive
  Predictors?
How Well Do Feature-Additive Explainers Explain Feature-Additive Predictors?
Zachariah Carmichael
Walter J. Scheirer
FAtt
309
9
0
27 Oct 2023
GLOBE-CE: A Translation-Based Approach for Global Counterfactual
  Explanations
GLOBE-CE: A Translation-Based Approach for Global Counterfactual ExplanationsInternational Conference on Machine Learning (ICML), 2023
Dan Ley
Saumitra Mishra
Daniele Magazzeni
LRM
405
30
0
26 May 2023
Explaining Groups of Instances Counterfactually for XAI: A Use Case,
  Algorithm and User Study for Group-Counterfactuals
Explaining Groups of Instances Counterfactually for XAI: A Use Case, Algorithm and User Study for Group-Counterfactuals
Greta Warren
Markt. Keane
Christophe Guéret
Eoin Delaney
254
15
0
16 Mar 2023
Bayesian Hierarchical Models for Counterfactual Estimation
Bayesian Hierarchical Models for Counterfactual EstimationInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Natraj Raman
Daniele Magazzeni
Sameena Shah
206
7
0
21 Jan 2023
Global Counterfactual Explanations: Investigations, Implementations and
  Improvements
Global Counterfactual Explanations: Investigations, Implementations and Improvements
Dan Ley
Saumitra Mishra
Daniele Magazzeni
LRM
325
15
0
14 Apr 2022
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic
  Review on Evaluating Explainable AI
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIACM Computing Surveys (ACM CSUR), 2022
Meike Nauta
Jan Trienes
Shreyasi Pathak
Elisa Nguyen
Michelle Peters
Yasmin Schmitt
Jorg Schlotterer
M. V. Keulen
C. Seifert
ELMXAI
813
640
0
20 Jan 2022
Diverse, Global and Amortised Counterfactual Explanations for
  Uncertainty Estimates
Diverse, Global and Amortised Counterfactual Explanations for Uncertainty EstimatesAAAI Conference on Artificial Intelligence (AAAI), 2021
Dan Ley
Umang Bhatt
Adrian Weller
UQCV
591
25
0
05 Dec 2021
Finding and Fixing Spurious Patterns with Explanations
Finding and Fixing Spurious Patterns with Explanations
Gregory Plumb
Marco Tulio Ribeiro
Ameet Talwalkar
406
46
0
03 Jun 2021
XOmiVAE: an interpretable deep learning model for cancer classification
  using high-dimensional omics data
XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data
Eloise Withnell
Xiaoyu Zhang
Kai Sun
Wenhan Luo
278
80
0
26 May 2021
Interpretable Machine Learning: Moving From Mythos to Diagnostics
Interpretable Machine Learning: Moving From Mythos to DiagnosticsQueue (ACM Queue), 2021
Valerie Chen
Jeffrey Li
Joon Sik Kim
Gregory Plumb
Ameet Talwalkar
304
34
0
10 Mar 2021
1
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