Timezone: »
Reliable graph neural network explanations through adversarial training
· Donald Loveland · Bhavya Kailkhura · T. Yong-Jin Han
Author Information
Donald Loveland (Lawrence Livermore National Lab)
Bhavya Kailkhura (Lawrence Livermore National Laboratory)
T. Yong-Jin Han (LLNL)
More from the Same Authors
-
2021 : Poster Session Test »
Jie Ren · -
2021 : A Turing Test for Transparency »
· Felix Biessmann -
2021 : Dynamic Defense Approach for Adversarial Robustness in Deep Neural Networks via Stochastic Ensemble Smoothed Model »
· Ruoxi Qin -
2021 : Quality Metrics for Transparent Machine Learning With and Without Humans In the Loop Are Not Correlated »
· Felix Biessmann -
2021 : Minimum sharpness: Scale-invariant parameter-robustness of neural networks »
· Hikaru Ibayashi -
2021 : Understanding Instance-based Interpretability of Variational Auto-Encoders »
· Zhifeng Kong · Kamalika Chaudhuri -
2021 : Informative Class Activation Maps: Estimating Mutual Information Between Regions and Labels »
· Zhenyue Qin -
2021 : This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks »
· Adrian Hoffmann · Claudio Fanconi · Rahul Rade · Jonas Kohler -
2021 : How Not to Measure Disentanglement »
· Julia Kiseleva · Maarten de Rijke -
2021 : Diverse and Amortised Counterfactual Explanations for Uncertainty Estimates »
· Dan Ley · Umang Bhatt · Adrian Weller -
2021 : Towards the Unification and Robustness of Perturbation and Gradient Based Explanations »
· Sushant Agarwal · Shahin Jabbari · Chirag Agarwal · Sohini Upadhyay · Steven Wu · Hima Lakkaraju -
2021 : Improving the Transferability of Adversarial Examples with New Iteration Framework and Input Dropout »
· Pengfei Xie -
2021 : Interpretable Face Manipulation Detection via Feature Whitening »
· Yingying Hua · Pengju Wang · Shiming Ge -
2021 : Synthetic Benchmarks for Scientific Research in Explainable Machine Learning »
· Yang Liu · Colin White · Willie Neiswanger -
2021 : A Probabilistic Representation of DNNs: Bridging Mutual Information and Generalization »
· Xinjie Lan -
2021 : A MaxSAT Approach to Inferring Explainable Temporal Properties »
· Rajarshi Roy · Zhe Xu · Ufuk Topcu · Jean-Raphaël Gaglione -
2021 : Active Automaton Inference for Reinforcement Learning using Queries and Counterexamples »
· Aditya Ojha · Zhe Xu · Ufuk Topcu -
2021 : Learned Interpretable Residual Extragradient ISTA for Sparse Coding »
· Connie Kong · Fanhua Shang -
2021 : Neural Network Classifier as Mutual Information Evaluator »
· Zhenyue Qin -
2021 : Evaluation of Saliency-based Explainability Methods »
· Sam Zabdiel Samuel · Vidhya Kamakshi · Narayanan Chatapuram Krishnan -
2021 : Order in the Court: Explainable AI Methods Prone to Disagreement »
· Michael Neely · Stefan F. Schouten · Ana Lucic -
2021 : On the overlooked issue of defining explanation objectives for local-surrogate explainers »
· Rafael Poyiadzi · Xavier Renard · Thibault Laugel · Raul Santos-Rodriguez · Marcin Detyniecki -
2021 : How Well do Feature Visualizations Support Causal Understanding of CNN Activations? »
· Roland S. Zimmermann · Judith Borowski · Robert Geirhos · Matthias Bethge · Thomas SA Wallis · Wieland Brendel -
2021 : On the Connections between Counterfactual Explanations and Adversarial Examples »
· Martin Pawelczyk · Shalmali Joshi · Chirag Agarwal · Sohini Upadhyay · Hima Lakkaraju -
2021 : Promises and Pitfalls of Black-Box Concept Learning Models »
· Anita Mahinpei · Justin Clark · Isaac Lage · Finale Doshi-Velez · Weiwei Pan -
2021 : On the (Un-)Avoidability of Adversarial Examples »
· Ruth Urner -
2021 : Towards a Rigorous Theoretical Analysis and Evaluation of GNN Explanations »
· Chirag Agarwal · Marinka Zitnik · Hima Lakkaraju -
2021 : Towards Fully Interpretable Deep Neural Networks: Are We There Yet? »
· Sandareka Wickramanayake -
2021 : Towards Automated Evaluation of Explanations in Graph Neural Networks »
· Balaji Ganesan · Devbrat Sharma -
2021 : A Source-Criticism Debiasing Method for GloVe Embeddings »
-
2021 : Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property prediction »
· Jiahua Rao · SHUANGJIA ZHENG -
2021 : What will it take to generate fairness-preserving explanations? »
· Jessica Dai · Sohini Upadhyay · Hima Lakkaraju -
2021 : Gradient-Based Interpretability Methods and Binarized Neural Networks »
· Amy Widdicombe -
2021 : Meaningfully Explaining a Model's Mistakes »
· Abubakar Abid · James Zou -
2021 : Feature Attributions and Counterfactual Explanations Can Be Manipulated »
· Dylan Slack · Sophie Hilgard · Sameer Singh · Hima Lakkaraju -
2021 : SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning »
· Aaron Chan · Xiang Ren -
2021 : Re-imagining GNN Explanations with ideas from Tabular Data »
· Anjali Singh · Shamanth Nayak K · Balaji Ganesan -
2021 : Learning Sparse Representations with Alternating Back-Propagation »
· Tian Han -
2021 : Deep Interpretable Criminal Charge Prediction Based on Temporal Trajectory »
· Jia Xu · Abdul Khan -
2021 : On the Effectiveness of Poisoning against Unsupervised Domain Adaptation »
Akshay Mehra · Bhavya Kailkhura · Pin-Yu Chen · Jihun Hamm -
2022 : Models Out of Line: A Fourier Lens on Distribution Shift Robustness »
Sara Fridovich-Keil · Brian Bartoldson · James Diffenderfer · Bhavya Kailkhura · Peer-Timo Bremer -
2020 Poster: Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning »
Jize Zhang · Bhavya Kailkhura · T. Yong-Jin Han -
2020 Poster: Adversarial Mutual Information for Text Generation »
Boyuan Pan · Yazheng Yang · Kaizhao Liang · Bhavya Kailkhura · Zhongming Jin · Xian-Sheng Hua · Deng Cai · Bo Li