"Why Should I Trust You?": Explaining the Predictions of Any Classifier

16 February 2016

Papers citing ""Why Should I Trust You?": Explaining the Predictions of Any Classifier"

50 / 4,309 papers shown

Title
Efficient nonparametric statistical inference on population feature importance using Shapley values B. Williamson Jean Feng FAtt 13 70 0 16 Jun 2020
Model Explanations with Differential Privacy Neel Patel Reza Shokri Yair Zick SILM FedML 28 32 0 16 Jun 2020
How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks Kirill Bykov Marina M.-C. Höhne Klaus-Robert Muller Shinichi Nakajima Marius Kloft UQCV FAtt 32 31 0 16 Jun 2020
Scalable Cross Lingual Pivots to Model Pronoun Gender for Translation Kellie Webster Emily Pitler 22 5 0 16 Jun 2020
Self-supervised Learning: Generative or Contrastive Xiao Liu Fanjin Zhang Zhenyu Hou Zhaoyu Wang Li Mian Jing Zhang Jie Tang SSL 54 1,588 0 15 Jun 2020
Hindsight Logging for Model Training Rolando Garcia Eric Liu Vikram Sreekanti Bobby Yan Anusha Dandamudi Joseph E. Gonzalez J. M. Hellerstein Koushik Sen VLM 29 10 0 12 Jun 2020
Generalized SHAP: Generating multiple types of explanations in machine learning Dillon Bowen L. Ungar FAtt 11 41 0 12 Jun 2020
SegNBDT: Visual Decision Rules for Segmentation Alvin Wan Daniel Ho You Song Henk Tillman Sarah Adel Bargal Joseph E. Gonzalez SSeg 27 6 0 11 Jun 2020
Getting a CLUE: A Method for Explaining Uncertainty Estimates Javier Antorán Umang Bhatt T. Adel Adrian Weller José Miguel Hernández-Lobato UQCV BDL 50 112 0 11 Jun 2020
How Interpretable and Trustworthy are GAMs? C. Chang S. Tan Benjamin J. Lengerich Anna Goldenberg R. Caruana FAtt 22 77 0 11 Jun 2020
Scalable Partial Explainability in Neural Networks via Flexible Activation Functions S. Sun Chen Li Zhuangkun Wei Antonios Tsourdos Weisi Guo FAtt 32 2 0 10 Jun 2020
OptiLIME: Optimized LIME Explanations for Diagnostic Computer Algorithms Giorgio Visani Enrico Bagli F. Chesani FAtt 27 60 0 10 Jun 2020
Why Attentions May Not Be Interpretable? Bing Bai Jian Liang Guanhua Zhang Hao Li Kun Bai Fei Wang FAtt 25 56 0 10 Jun 2020
Adversarial Infidelity Learning for Model Interpretation Jian Liang Bing Bai Yuren Cao Kun Bai Fei Wang AAML 59 18 0 09 Jun 2020
Provable tradeoffs in adversarially robust classification Yan Sun Hamed Hassani David Hong Alexander Robey 23 53 0 09 Jun 2020
Stealing Deep Reinforcement Learning Models for Fun and Profit Kangjie Chen Shangwei Guo Tianwei Zhang Xiaofei Xie Yang Liu MLAU MIACV OffRL 24 45 0 09 Jun 2020
Model-agnostic Feature Importance and Effects with Dependent Features -- A Conditional Subgroup Approach Christoph Molnar Gunnar Konig B. Bischl Giuseppe Casalicchio 33 77 0 08 Jun 2020
BS-Net: learning COVID-19 pneumonia severity on a large Chest X-Ray dataset A. Signoroni Mattia Savardi Sergio Benini Nicola Adami R. Leonardi ... F. Vaccher M. Ravanelli A. Borghesi R. Maroldi D. Farina 21 37 0 08 Jun 2020
Propositionalization and Embeddings: Two Sides of the Same Coin Nada Lavrac Blaž Škrlj Marko Robnik-Šikonja 21 26 0 08 Jun 2020
Higher-Order Explanations of Graph Neural Networks via Relevant Walks Thomas Schnake Oliver Eberle Jonas Lederer Shinichi Nakajima Kristof T. Schütt Klaus-Robert Muller G. Montavon 34 217 0 05 Jun 2020
Location, location, location: Satellite image-based real-estate appraisal Jan-Peter Kucklick Oliver Müller 28 5 0 04 Jun 2020
Consistent feature selection for neural networks via Adaptive Group Lasso L. Ho Vu C. Dinh OOD 22 9 0 30 May 2020
AI Research Considerations for Human Existential Safety (ARCHES) Andrew Critch David M. Krueger 30 50 0 30 May 2020
A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers Kevin Fauvel Véronique Masson Elisa Fromont AI4TS 44 17 0 29 May 2020
CausaLM: Causal Model Explanation Through Counterfactual Language Models Amir Feder Nadav Oved Uri Shalit Roi Reichart CML LRM 49 157 0 27 May 2020
Who is this Explanation for? Human Intelligence and Knowledge Graphs for eXplainable AI I. Celino 12 5 0 27 May 2020
Rationalizing Text Matching: Learning Sparse Alignments via Optimal Transport Kyle Swanson L. Yu Tao Lei OT 29 37 0 27 May 2020
Review of Mathematical frameworks for Fairness in Machine Learning E. del Barrio Paula Gordaliza Jean-Michel Loubes FaML FedML 15 39 0 26 May 2020
The best way to select features? Xin Man Ernest P. Chan 16 60 0 26 May 2020
NILE : Natural Language Inference with Faithful Natural Language Explanations Sawan Kumar Partha P. Talukdar XAI LRM 27 160 0 25 May 2020
Towards Analogy-Based Explanations in Machine Learning Eyke Hüllermeier XAI 16 20 0 23 May 2020
An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challenges D. Charte F. Charte M. J. D. Jesus Francisco Herrera SSL OOD 27 51 0 21 May 2020
A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation T. Dissanayake Tharindu Fernando Simon Denman Sridha Sridharan H. Ghaemmaghami Clinton Fookes 8 8 0 21 May 2020
Interpretable and Accurate Fine-grained Recognition via Region Grouping Zixuan Huang Yin Li 17 138 0 21 May 2020
An Adversarial Approach for Explaining the Predictions of Deep Neural Networks Arash Rahnama A.-Yu Tseng FAtt AAML FaML 25 5 0 20 May 2020
The challenges of deploying artificial intelligence models in a rapidly evolving pandemic Yipeng Hu J. Jacob Geoffrey J. M. Parker D. Hawkes J. Hurst Danail Stoyanov OOD 23 65 0 19 May 2020
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps Tobias Huber Katharina Weitz Elisabeth André Ofra Amir FAtt 21 64 0 18 May 2020
Applying Genetic Programming to Improve Interpretability in Machine Learning Models Leonardo Augusto Ferreira F. G. Guimarães Rodrigo C. P. Silva 14 37 0 18 May 2020
Reliable Local Explanations for Machine Listening Saumitra Mishra Emmanouil Benetos Bob L. T. Sturm S. Dixon AAML FAtt 12 20 0 15 May 2020
Evolved Explainable Classifications for Lymph Node Metastases Iam Palatnik de Sousa M. Vellasco E. C. Silva 19 6 0 14 May 2020
Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions Xiaochuang Han Byron C. Wallace Yulia Tsvetkov MILM FAtt AAML TDI 28 165 0 14 May 2020
Ensembled sparse-input hierarchical networks for high-dimensional datasets Jean Feng N. Simon 19 4 0 11 May 2020
Explainable Matrix -- Visualization for Global and Local Interpretability of Random Forest Classification Ensembles Mário Popolin Neto F. Paulovich FAtt 38 88 0 08 May 2020
Beyond Accuracy: Behavioral Testing of NLP models with CheckList Marco Tulio Ribeiro Tongshuang Wu Carlos Guestrin Sameer Singh ELM 52 1,084 0 08 May 2020
XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification Kevin Fauvel Elisa Fromont Véronique Masson P. Faverdin Alexandre Termier AI4TS 33 41 0 07 May 2020
A Locally Adaptive Interpretable Regression Lkhagvadorj Munkhdalai Tsendsuren Munkhdalai K. Ryu 14 5 0 07 May 2020
Contextualizing Hate Speech Classifiers with Post-hoc Explanation Brendan Kennedy Xisen Jin Aida Mostafazadeh Davani Morteza Dehghani Xiang Ren 27 138 0 05 May 2020
Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition Mahsan Nourani Chiradeep Roy Tahrima Rahman Eric D. Ragan Nicholas Ruozzi Vibhav Gogate AAML 15 17 0 05 May 2020
A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds M. Kovalev Lev V. Utkin AAML 43 31 0 05 May 2020
Post-hoc explanation of black-box classifiers using confident itemsets M. Moradi Matthias Samwald 62 98 0 05 May 2020