Timezone: »
Understanding Instance-based Interpretability of Variational Auto-Encoders
· Zhifeng Kong · Kamalika Chaudhuri
Author Information
Zhifeng Kong (University of California San Diego)
Kamalika Chaudhuri (University of California at San Diego)
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 : 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-RaphaĆ«l 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 : Reliable graph neural network explanations through adversarial training »
· Donald Loveland · Bhavya Kailkhura · T. Yong-Jin Han -
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 Fast Sampling of Diffusion Probabilistic Models »
Zhifeng Kong -
2021 : Universal Approximation of Residual Flows in Maximum Mean Discrepancy »
Zhifeng Kong -
2021 : Privacy Amplification by Bernoulli Sampling »
Jacob Imola · Kamalika Chaudhuri -
2021 : A Shuffling Framework For Local Differential Privacy »
Casey M Meehan · Amrita Roy Chowdhury · Kamalika Chaudhuri · Somesh Jha -
2021 : Privacy Amplification by Subsampling in Time Domain »
Tatsuki Koga · Casey M Meehan · Kamalika Chaudhuri -
2022 : Understanding Rare Spurious Correlations in Neural Networks »
Yao-Yuan Yang · Chi-Ning Chou · Kamalika Chaudhuri -
2023 : Machine Learning with Feature Differential Privacy »
Saeed Mahloujifar · Chuan Guo · G. Edward Suh · Kamalika Chaudhuri -
2023 : Panel Discussion »
Peter Kairouz · Song Han · Kamalika Chaudhuri · Florian Tramer -
2023 : Kamalika Chaudhuri »
Kamalika Chaudhuri -
2023 Poster: Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design »
Chuan Guo · Kamalika Chaudhuri · Pierre Stock · Michael Rabbat -
2023 Oral: Why does Throwing Away Data Improve Worst-Group Error? »
Kamalika Chaudhuri · Kartik Ahuja · Martin Arjovsky · David Lopez-Paz -
2023 Poster: Data-Copying in Generative Models: A Formal Framework »
Robi Bhattacharjee · Sanjoy Dasgupta · Kamalika Chaudhuri -
2023 Poster: A Two-Stage Active Learning Algorithm for k-Nearest Neighbors »
Nicholas Rittler · Kamalika Chaudhuri -
2023 Poster: Why does Throwing Away Data Improve Worst-Group Error? »
Kamalika Chaudhuri · Kartik Ahuja · Martin Arjovsky · David Lopez-Paz -
2022 : Panel Discussion »
Mirco Ravanelli · Chris Donahue · Zhifeng Kong · Wei-Ning Hsu · Rachel Manzelli · Sadie Allen -
2022 : DiffWave: A Versatile Diffusion Model for Audio Synthesis »
Zhifeng Kong -
2022 Poster: Thompson Sampling for Robust Transfer in Multi-Task Bandits »
Zhi Wang · Chicheng Zhang · Kamalika Chaudhuri -
2022 Spotlight: Thompson Sampling for Robust Transfer in Multi-Task Bandits »
Zhi Wang · Chicheng Zhang · Kamalika Chaudhuri -
2022 Poster: Bounding Training Data Reconstruction in Private (Deep) Learning »
Chuan Guo · Brian Karrer · Kamalika Chaudhuri · Laurens van der Maaten -
2022 Oral: Bounding Training Data Reconstruction in Private (Deep) Learning »
Chuan Guo · Brian Karrer · Kamalika Chaudhuri · Laurens van der Maaten -
2021 : Discussion Panel #2 »
Bo Li · Nicholas Carlini · Andrzej Banburski · Kamalika Chaudhuri · Will Xiao · Cihang Xie -
2021 : Invited Talk #9 »
Kamalika Chaudhuri -
2021 : Invited Talk: Kamalika Chaudhuri »
Kamalika Chaudhuri -
2021 : Invited Talk: Kamalika Chaudhuri »
Kamalika Chaudhuri -
2021 : Live Panel Discussion »
Thomas Dietterich · Chelsea Finn · Kamalika Chaudhuri · Yarin Gal · Uri Shalit -
2021 Poster: Sample Complexity of Robust Linear Classification on Separated Data »
Robi Bhattacharjee · Somesh Jha · Kamalika Chaudhuri -
2021 Spotlight: Sample Complexity of Robust Linear Classification on Separated Data »
Robi Bhattacharjee · Somesh Jha · Kamalika Chaudhuri -
2021 Poster: Connecting Interpretability and Robustness in Decision Trees through Separation »
Michal Moshkovitz · Yao-Yuan Yang · Kamalika Chaudhuri -
2021 Spotlight: Connecting Interpretability and Robustness in Decision Trees through Separation »
Michal Moshkovitz · Yao-Yuan Yang · Kamalika Chaudhuri -
2020 Poster: When are Non-Parametric Methods Robust? »
Robi Bhattacharjee · Kamalika Chaudhuri -
2019 Talk: Opening Remarks »
Kamalika Chaudhuri · Ruslan Salakhutdinov -
2018 Poster: Active Learning with Logged Data »
Songbai Yan · Kamalika Chaudhuri · Tara Javidi -
2018 Poster: Analyzing the Robustness of Nearest Neighbors to Adversarial Examples »
Yizhen Wang · Somesh Jha · Kamalika Chaudhuri -
2018 Oral: Active Learning with Logged Data »
Songbai Yan · Kamalika Chaudhuri · Tara Javidi -
2018 Oral: Analyzing the Robustness of Nearest Neighbors to Adversarial Examples »
Yizhen Wang · Somesh Jha · Kamalika Chaudhuri -
2017 Workshop: Picky Learners: Choosing Alternative Ways to Process Data. »
Corinna Cortes · Kamalika Chaudhuri · Giulia DeSalvo · Ningshan Zhang · Chicheng Zhang -
2017 Poster: Active Heteroscedastic Regression »
Kamalika Chaudhuri · Prateek Jain · Nagarajan Natarajan -
2017 Talk: Active Heteroscedastic Regression »
Kamalika Chaudhuri · Prateek Jain · Nagarajan Natarajan