General Keywords

[ Algorithms ] [ Algorithms; Optimization ] [ Applications ] [ Data, Challenges, Implementations, and Software ] [ Deep Learning ] [ Deep Learning; Deep Learning ] [ Neuroscience and Cognitive Science ] [ Optimization ] [ Optimization; Optimization ] [ Probabilistic Methods ] [ Probabilistic Methods; Probabilistic Methods ] [ Reinforcement Learning and Planning ] [ Social Aspects of Machine Learning ] [ Theory ] [ Theory; Theory ]

Topic Keywords

[ Active Learning ] [ Active Learning; Algorithms ] [ Activity and Event Recognition ] [ Adaptive Data Analysis; Optimization ] [ Adversarial Examples ] [ Adversarial Learning ] [ Adversarial Learning; Algorithms ] [ Adversarial Networks ] [ Adversarial Networks ] [ Adversarial Networks; Deep Learning ] [ Adversarial Networks; Deep Learning ] [ AI Safety ] [ Algorithms Evaluation ] [ Approximate Inference ] [ Architectures ] [ Attention Models ] [ Audio and Speech Processing ] [ AutoML ] [ Bandit Algorithms ] [ Bandit Algorithms; Algorithms ] [ Bandit Algorithms; Reinforcement Learning and Planning ] [ Bandit Algorithms; Reinforcement Learning and Planning ] [ Bandits ] [ Bayesian Deep Learning ] [ Bayesian Methods ] [ Bayesian Nonparametrics ] [ Bayesian Theory ] [ Bayesian Theory ] [ Benchmarks ] [ Biologically Plausible Deep Networks ] [ Biologically Plausible Deep Networks; Deep Learning ] [ Biologically Plausible Deep Networks; Neuroscience and Cognitive Science ] [ Body Pose, Face, and Gesture Analysis ] [ Body Pose, Face, and Gesture Analysis; Applications ] [ Boosting and Ensemble Methods ] [ Boosting and Ensemble Methods; Algorithms ] [ Boosting and Ensemble Methods; Probabilistic Methods; Probabilistic Methods ] [ Causal Inference ] [ Classification ] [ Classification; Algorithms ] [ Classification; Algorithms ] [ Classification; Applications ] [ Classification; Deep Learning; Deep Learning ] [ Classification; Deep Learning; Deep Learning ] [ Clustering ] [ Clustering; Applications ] [ Clustering; Theory ] [ CNN Architectures; Deep Learning ] [ CNN Architectures; Deep Learning ] [ CNN Architectures; Theory ] [ Cognitive Science; Neuroscience and Cognitive Science ] [ Collaborative Filtering ] [ Collaborative Filtering; Algorithms ] [ Collaborative Filtering; Applications ] [ Combinatorial Optimization ] [ Components Analysis (e.g., CCA, ICA, LDA, PCA) ] [ Computational Biology and Bioinformatics ] [ Computational Biology and Bioinformatics; Applications ] [ Computational Complexity ] [ Computational Learning Theory ] [ Computational Photography ] [ Computational Social Science ] [ Computer Vision ] [ Computer Vision; Applications ] [ Computer Vision; Applications ] [ Computer Vision; Deep Learning ] [ Computer Vision; Deep Learning ] [ Computer Vision; Deep Learning ] [ Computer Vision; Deep Learning ] [ Continual Learning ] [ Convex Optimization ] [ Convex Optimization; Optimization ] [ Convex Optimization; Probabilistic Methods; Theory; Theory ] [ Convex Optimization; Theory ] [ Crowdsourcing ] [ Decision and Control ] [ Deep Autoencoders; Deep Learning ] [ Deep learning Theory ] [ Deep RL ] [ Density Estimation ] [ Density Estimation; Deep Learning ] [ Derivative Free Optimization ] [ Dialog- or Communication-Based Learning ] [ Dimensionality Reduction ] [ Distributed and Parallel Optimization ] [ Distributed Inference ] [ Efficient Inference Methods ] [ Efficient Training Methods; Deep Learning ] [ Embedding and Representation learning ] [ Embedding Approaches ] [ Exploration ] [ Fairness, Accountability, and Transparency ] [ Fairness, Accountability, and Transparency ] [ Few-Shot Learning ] [ Few-Shot Learning; Algorithms ] [ Frequentist Statistics ] [ Game Theory and Computational Economics ] [ Gaussian Processes ] [ Gaussian Processes and Bayesian non-parametrics ] [ Generative Models ] [ Generative Models ] [ Graphical Models ] [ Graphical Models ] [ Hardware and Systems ] [ Healthcare ] [ Human or Animal Learning ] [ Human or Animal Learning; Probabilistic Methods ] [ Image Segmentation ] [ Image Segmentation; Algorithms ] [ Image Segmentation; Applications ] [ Information Theory ] [ Kernel Methods ] [ Kernel Methods; Optimization ] [ Large Deviations and Asymptotic Analysis ] [ Large Scale Learning ] [ Large Scale Learning; Algorithms ] [ Large Scale Learning; Algorithms ] [ Large Scale Learning; Applications ] [ Large Scale Learning; Deep Learning ] [ Large Scale Learning; Probabilistic Methods ] [ Latent Variable Models ] [ Learning Theory ] [ Markov Decision Processes ] [ Markov Decision Processes; Reinforcement Learning and Planning ] [ Markov Decision Processes; Reinforcement Learning and Planning ] [ Matrix and Tensor Factorization ] [ MCMC ] [ Memory ] [ Memory; Optimization ] [ Meta-Learning ] [ Meta-Learning; Applications ] [ Metric Learning ] [ Missing Data; Algorithms ] [ Missing Data; Algorithms ] [ Missing Data; Theory ] [ Model Selection and Structure Learning ] [ Models of Learning and Generalization ] [ Monte Carlo Methods ] [ Multi-Agent RL ] [ Multimodal Learning ] [ Multitask and Transfer Learning ] [ Multitask and Transfer Learning; Algorithms ] [ Multitask and Transfer Learning; Probabilistic Methods ] [ Multitask, Transfer, and Meta Learning ] [ Natural Language Processing ] [ Network Analysis ] [ Networks and Relational Learning ] [ Neural Coding; Neuroscience and Cognitive Science ] [ Neuroscience ] [ Neuroscience and Cognitive Science ] [ Non-Convex Optimization ] [ Non-Convex Optimization ] [ Non-Convex Optimization; Theory ] [ Non-parametric models ] [ Object Detection; Deep Learning ] [ Object Detection; Neuroscience and Cognitive Science ] [ Online Learning ] [ Online Learning Algorithms ] [ Online Learning Theory ] [ Online Learning; Theory ] [ Optimal Transport ] [ Optimization for Deep Networks ] [ Others ] [ Others ] [ Others ] [ Others ] [ Others ] [ Planning and Control ] [ Plasticity and Adaptation ] [ Predictive Models ] [ Predictive Models; Deep Learning ] [ Predictive Models; Deep Learning ] [ Privacy, Anonymity, and Security ] [ Privacy, Anonymity, and Security ] [ Probabilistic Methods ] [ Probabilistic Programming ] [ Program Understanding and Generation ] [ Quantitative Finance and Econometrics ] [ Ranking and Preference Learning ] [ Ranking and Preference Learning; Theory ] [ Reasoning; Optimization ] [ Recommender Systems ] [ Recurrent Networks ] [ Recurrent Networks; Theory ] [ Regression ] [ Regression; Algorithms ] [ Regression; Applications ] [ Regression; Optimization ] [ Regression; Probabilistic Methods; Probabilistic Methods ] [ Regularization ] [ Regularization ] [ Reinforcement Learning ] [ Reinforcement Learning and Planning ] [ Relational Learning ] [ Representation Learning ] [ Representation Learning; Algorithms ] [ Representation Learning; Algorithms ] [ Representation Learning; Neuroscience and Cognitive Science ] [ Representation Learning; Neuroscience and Cognitive Science; Neuroscience and Cognitive Science ] [ Representation Learning; Optimization ] [ RL, Decisions and Control Theory ] [ Robotics ] [ Robust statistics ] [ Semi-Supervised Learning ] [ Social Aspects of Machine Learning ] [ Software Toolkits ] [ Spaces of Functions and Kernels ] [ Sparse Coding and Dimensionality Expansion; Applications ] [ Sparsity and Compressed Sensing ] [ Sparsity and Compressed Sensing; Applications ] [ Sparsity and Compressed Sensing; Optimization; Theory ] [ Speech Recognition ] [ Statistical Learning Theory ] [ Statistical Physics of Learning ] [ Stochastic Optimization ] [ Structured Prediction ] [ Submodular Optimization ] [ Supervised Learning ] [ Sustainability and Environment ] [ Theory ] [ Time Series Analysis ] [ Time Series Analysis; Deep Learning ] [ Time Series Analysis; Probabilistic Methods; Probabilistic Methods ] [ Time Series and Sequences ] [ Topic Models ] [ Uncertainty Estimation ] [ Uncertainty Estimation; Applications; Probabilistic Methods ] [ Unsupervised Learning ] [ Unsupervised Learning; Applications ] [ Unsupervised Learning; Deep Learning ] [ Variational Inference ] [ Visualization or Exposition Techniques for Deep Networks ] [ Visual Question Answering ] [ Visual Scene Analysis and Interpretation ]

64 Results

Spotlight
Tue 17:20 Re-understanding Finite-State Representations of Recurrent Policy Networks
Mohamad H Danesh, Anurag Koul, Alan Fern, Saeed Khorram
Spotlight
Tue 18:25 Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data
Amnon Catav, Boyang Fu, Yazeed Zoabi, Ahuva Weiss Meilik, Noam Shomron, Jason Ernst, Sriram Sankararaman, Ran Gilad-Bachrach
Spotlight
Tue 18:30 Connecting Interpretability and Robustness in Decision Trees through Separation
Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri
Poster
Tue 21:00 Re-understanding Finite-State Representations of Recurrent Policy Networks
Mohamad H Danesh, Anurag Koul, Alan Fern, Saeed Khorram
Poster
Tue 21:00 Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data
Amnon Catav, Boyang Fu, Yazeed Zoabi, Ahuva Weiss Meilik, Noam Shomron, Jason Ernst, Sriram Sankararaman, Ran Gilad-Bachrach
Poster
Tue 21:00 Connecting Interpretability and Robustness in Decision Trees through Separation
Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri
Spotlight
Wed 5:35 Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity
Ryan Henderson, Djork-Arné Clevert, Floriane Montanari
Spotlight
Wed 5:40 Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, Hima Lakkaraju
Spotlight
Wed 6:40 What does LIME really see in images?
Damien Garreau, Dina Mardaoui
Spotlight
Wed 6:40 Approximate Group Fairness for Clustering
Bo Li, Lijun Li, Ankang Sun, Chenhao Wang, Yingfan Wang
Spotlight
Wed 6:40 Towards Rigorous Interpretations: a Formalisation of Feature Attribution
Darius Afchar, Vincent Guigue, Romain Hennequin
Spotlight
Wed 7:40 Multi-group Agnostic PAC Learnability
Guy Rothblum, Gal Yona
Spotlight
Wed 7:40 Collaborative Bayesian Optimization with Fair Regret
Rachael Hwee Ling Sim, Yehong Zhang, Bryan Kian Hsiang Low, Patrick Jaillet
Poster
Wed 9:00 Towards Rigorous Interpretations: a Formalisation of Feature Attribution
Darius Afchar, Vincent Guigue, Romain Hennequin
Poster
Wed 9:00 Multi-group Agnostic PAC Learnability
Guy Rothblum, Gal Yona
Poster
Wed 9:00 Approximate Group Fairness for Clustering
Bo Li, Lijun Li, Ankang Sun, Chenhao Wang, Yingfan Wang
Poster
Wed 9:00 Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity
Ryan Henderson, Djork-Arné Clevert, Floriane Montanari
Poster
Wed 9:00 What does LIME really see in images?
Damien Garreau, Dina Mardaoui
Poster
Wed 9:00 Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, Hima Lakkaraju
Poster
Wed 9:00 Collaborative Bayesian Optimization with Fair Regret
Rachael Hwee Ling Sim, Yehong Zhang, Bryan Kian Hsiang Low, Patrick Jaillet
Spotlight
Wed 17:20 Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data
Esther Rolf, Theodora Worledge, Benjamin Recht, Michael Jordan
Spotlight
Wed 18:25 Fair Classification with Noisy Protected Attributes: A Framework with Provable Guarantees
L. Elisa Celis, Lingxiao Huang, Vijay Keswani, Nisheeth K. Vishnoi
Poster
Wed 21:00 Fair Classification with Noisy Protected Attributes: A Framework with Provable Guarantees
L. Elisa Celis, Lingxiao Huang, Vijay Keswani, Nisheeth K. Vishnoi
Poster
Wed 21:00 Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data
Esther Rolf, Theodora Worledge, Benjamin Recht, Michael Jordan
Oral
Thu 5:00 Fair Selective Classification Via Sufficiency
Joshua Lee, Yuheng Bu, Deepta Rajan, Prasanna Sattigeri, Rameswar Panda, Subhro Das, Gregory Wornell
Spotlight
Thu 5:20 Learning Representations by Humans, for Humans
Sophie Hilgard, Nir Rosenfeld, Mahzarin Banaji, Jack Cao, David Parkes
Spotlight
Thu 5:25 Strategic Classification in the Dark
Ganesh Ghalme, Vineet Nair, Itay Eilat, Inbal Talgam-Cohen, Nir Rosenfeld
Spotlight
Thu 5:30 Fairness for Image Generation with Uncertain Sensitive Attributes
Ajil Jalal, Sushrut Karmalkar, Jessica Hoffmann, Alex Dimakis, Eric Price
Spotlight
Thu 5:35 Characterizing Fairness Over the Set of Good Models Under Selective Labels
Amanda Coston, Ashesh Rambachan, Alexandra Chouldechova
Spotlight
Thu 5:40 GANMEX: One-vs-One Attributions using GAN-based Model Explainability
Sheng-Min Shih, Pin-Ju Tien, Zohar Karnin
Spotlight
Thu 5:45 Directional Bias Amplification
Angelina Wang, Olga Russakovsky
Spotlight
Thu 7:25 Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability
Mihaela Curmei, Sarah Dean, Benjamin Recht
Spotlight
Thu 7:45 Fairness of Exposure in Stochastic Bandits
Lequn Wang, Yiwei Bai, Wen Sun, Thorsten Joachims
Poster
Thu 9:00 Characterizing Fairness Over the Set of Good Models Under Selective Labels
Amanda Coston, Ashesh Rambachan, Alexandra Chouldechova
Poster
Thu 9:00 Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability
Mihaela Curmei, Sarah Dean, Benjamin Recht
Poster
Thu 9:00 GANMEX: One-vs-One Attributions using GAN-based Model Explainability
Sheng-Min Shih, Pin-Ju Tien, Zohar Karnin
Poster
Thu 9:00 Directional Bias Amplification
Angelina Wang, Olga Russakovsky
Poster
Thu 9:00 Learning Representations by Humans, for Humans
Sophie Hilgard, Nir Rosenfeld, Mahzarin Banaji, Jack Cao, David Parkes
Poster
Thu 9:00 Fairness of Exposure in Stochastic Bandits
Lequn Wang, Yiwei Bai, Wen Sun, Thorsten Joachims
Poster
Thu 9:00 Fair Selective Classification Via Sufficiency
Joshua Lee, Yuheng Bu, Deepta Rajan, Prasanna Sattigeri, Rameswar Panda, Subhro Das, Gregory Wornell
Poster
Thu 9:00 Strategic Classification in the Dark
Ganesh Ghalme, Vineet Nair, Itay Eilat, Inbal Talgam-Cohen, Nir Rosenfeld
Poster
Thu 9:00 Fairness for Image Generation with Uncertain Sensitive Attributes
Ajil Jalal, Sushrut Karmalkar, Jessica Hoffmann, Alex Dimakis, Eric Price
Spotlight
Thu 17:30 Explanations for Monotonic Classifiers.
Joao Marques-Silva, Thomas Gerspacher, Martin Cooper, Alexey Ignatiev, Nina Narodytska
Spotlight
Thu 18:20 Generative Causal Explanations for Graph Neural Networks
Wanyu Lin, Hao Lan, Baochun Li
Spotlight
Thu 18:25 Towards Understanding and Mitigating Social Biases in Language Models
Paul Liang, Chiyu Wu, Louis-Philippe Morency, Russ Salakhutdinov
Spotlight
Thu 18:45 Correcting Exposure Bias for Link Recommendation
Shantanu Gupta, Hao Wang, Zachary Lipton, Bernie Wang
Spotlight
Thu 18:45 Optimal Counterfactual Explanations in Tree Ensembles
Axel Parmentier, Thibaut Vidal
Spotlight
Thu 19:00 Explaining Time Series Predictions with Dynamic Masks
Jonathan Crabbé, Mihaela van der Schaar
Spotlight
Thu 19:10 Understanding and Mitigating Accuracy Disparity in Regression
Jianfeng Chi, Yuan Tian, Geoff Gordon, Han Zhao
Spotlight
Thu 19:20 DANCE: Enhancing saliency maps using decoys
Yang Lu, Wenbo Guo, Xinyu Xing, William Stafford Noble
Spotlight
Thu 19:25 Blind Pareto Fairness and Subgroup Robustness
Natalia Martinez Gil, Martin Bertran, Afroditi Papadaki, Miguel Rodrigues, Guillermo Sapiro
Spotlight
Thu 19:35 On the Problem of Underranking in Group-Fair Ranking
Sruthi Gorantla, Amit Jayant Deshpande, Anand Louis
Spotlight
Thu 19:40 Testing Group Fairness via Optimal Transport Projections
Nian Si, Karthyek Murthy, Jose Blanchet, Viet Anh Nguyen
Poster
Thu 21:00 Understanding and Mitigating Accuracy Disparity in Regression
Jianfeng Chi, Yuan Tian, Geoff Gordon, Han Zhao
Poster
Thu 21:00 On the Problem of Underranking in Group-Fair Ranking
Sruthi Gorantla, Amit Jayant Deshpande, Anand Louis
Poster
Thu 21:00 Explaining Time Series Predictions with Dynamic Masks
Jonathan Crabbé, Mihaela van der Schaar
Poster
Thu 21:00 Blind Pareto Fairness and Subgroup Robustness
Natalia Martinez Gil, Martin Bertran, Afroditi Papadaki, Miguel Rodrigues, Guillermo Sapiro
Poster
Thu 21:00 Towards Understanding and Mitigating Social Biases in Language Models
Paul Liang, Chiyu Wu, Louis-Philippe Morency, Russ Salakhutdinov
Poster
Thu 21:00 DANCE: Enhancing saliency maps using decoys
Yang Lu, Wenbo Guo, Xinyu Xing, William Stafford Noble
Poster
Thu 21:00 Optimal Counterfactual Explanations in Tree Ensembles
Axel Parmentier, Thibaut Vidal
Poster
Thu 21:00 Explanations for Monotonic Classifiers.
Joao Marques-Silva, Thomas Gerspacher, Martin Cooper, Alexey Ignatiev, Nina Narodytska
Poster
Thu 21:00 Generative Causal Explanations for Graph Neural Networks
Wanyu Lin, Hao Lan, Baochun Li
Poster
Thu 21:00 Testing Group Fairness via Optimal Transport Projections
Nian Si, Karthyek Murthy, Jose Blanchet, Viet Anh Nguyen
Poster
Thu 21:00 Correcting Exposure Bias for Link Recommendation
Shantanu Gupta, Hao Wang, Zachary Lipton, Bernie Wang