Getting Started
Schedule
Tutorials
Main Conference
Invited Talks
Orals
Spotlights
Awards
Test of Time Award
Papers
Workshops
Community
Affinity Events
Socials
Sponsors
Organizers
Help
FAQ
Presenters Instructions
Moderators Instructions
RocketChat Help
RocketChat Desktop Client
Login
firstbacksecondback
Search All 2021 Events
Filter by Keyword:
Algorithms
Algorithms -> Active Learning; Algorithms -> Classification; Algorithms
Algorithms -> Adaptive Data Analysis; Optimization
Algorithms -> Adversarial Learning; Algorithms
Algorithms -> Bandit Algorithms; Algorithms
Algorithms -> Bandit Algorithms; Reinforcement Learning and Planning
Algorithms -> Bandit Algorithms; Reinforcement Learning and Planning -> Reinforcement Learning; Theory
Algorithms -> Boosting and Ensemble Methods; Algorithms -> Model Selection and Structure Learning; Theory
Algorithms -> Boosting and Ensemble Methods; Probabilistic Methods; Probabilistic Methods
Algorithms -> Classification; Algorithms
Algorithms -> Classification; Algorithms -> Meta-Learning; Algorithms -> Multitask and Transfer Learning; Algorithms
Algorithms -> Classification; Applications -> Computational Social Science; Applications
Algorithms -> Classification; Deep Learning; Deep Learning
Algorithms -> Classification; Deep Learning; Deep Learning -> Predictive Models; Deep Learning
Algorithms -> Clustering; Applications -> Hardware and Systems; Applications
Algorithms -> Clustering; Theory
Algorithms -> Collaborative Filtering; Algorithms -> Large Scale Learning; Applications
Algorithms -> Collaborative Filtering; Applications -> Information Retrieval; Applications
Algorithms -> Density Estimation; Deep Learning -> Adversarial Networks; Deep Learning -> Generative Models; Theory
Algorithms -> Few-Shot Learning; Algorithms
Algorithms -> Image Segmentation; Algorithms -> Similarity and Distance Learning; Algorithms
Algorithms -> Image Segmentation; Applications -> Computer Vision; Applications -> Image Segmentation; Applications
Algorithms -> Kernel Methods; Optimization -> Non-Convex Optimization; Theory
Algorithms -> Large Scale Learning; Algorithms
Algorithms -> Large Scale Learning; Algorithms -> Regression; Algorithms -> Sparsity and Compressed Sensing; Algorithms
Algorithms -> Large Scale Learning; Applications -> Natural Language Processing; Deep Learning
Algorithms -> Large Scale Learning; Deep Learning -> Efficient Training Methods; Deep Learning
Algorithms -> Large Scale Learning; Probabilistic Methods
Algorithms -> Meta-Learning; Applications -> Object Recognition; Data, Challenges, Implementations, and Software
Algorithms -> Missing Data; Algorithms
Algorithms -> Missing Data; Algorithms -> Uncertainty Estimation; Probabilistic Methods
Algorithms -> Missing Data; Theory
Algorithms -> Multitask and Transfer Learning; Algorithms
Algorithms -> Multitask and Transfer Learning; Probabilistic Methods
Algorithms -> Online Learning; Theory -> Computational Complexity; Theory
Algorithms; Optimization -> Convex Optimization; Optimization -> Stochastic Optimization; Theory
Algorithms -> Ranking and Preference Learning; Theory
Algorithms -> Regression; Algorithms -> Spectral Methods; Optimization -> Convex Optimization; Theory
Algorithms -> Regression; Applications -> Health; Theory -> Learning Theory; Theory
Algorithms -> Regression; Optimization -> Convex Optimization; Theory -> Learning Theory; Theory
Algorithms -> Regression; Probabilistic Methods; Probabilistic Methods
Algorithms -> Representation Learning; Algorithms -> Sparse Coding and Dimensionality Expansion; Applications
Algorithms -> Representation Learning; Algorithms -> Structured Prediction; Applications
Algorithms -> Representation Learning; Neuroscience and Cognitive Science
Algorithms -> Representation Learning; Neuroscience and Cognitive Science; Neuroscience and Cognitive Science
Algorithms -> Representation Learning; Optimization
Algorithms -> Sparse Coding and Dimensionality Expansion; Applications -> Denoising; Applications
Algorithms -> Sparsity and Compressed Sensing; Applications -> Information Retrieval; Applications
Algorithms -> Sparsity and Compressed Sensing; Optimization; Theory
Algorithms -> Uncertainty Estimation; Applications; Probabilistic Methods
Algorithms -> Unsupervised Learning; Applications
Algorithms -> Unsupervised Learning; Deep Learning
Applications -> Body Pose, Face, and Gesture Analysis; Applications -> Computer Vision; Deep Learning
Applications -> Computational Biology and Bioinformatics; Applications -> Health; Deep Learning
Applications -> Computer Vision; Applications -> Object Detection; Applications
Applications -> Computer Vision; Applications -> Visual Scene Analysis and Interpretation; Deep Learning
Applications -> Computer Vision; Deep Learning
Applications -> Computer Vision; Deep Learning -> Adversarial Networks; Deep Learning
Applications -> Computer Vision; Deep Learning -> Deep Autoencoders; Deep Learning
Applications -> Object Detection; Deep Learning
Applications -> Object Detection; Neuroscience and Cognitive Science
Applications -> Time Series Analysis; Deep Learning
Applications -> Time Series Analysis; Probabilistic Methods; Probabilistic Methods
Data, Challenges, Implementations, and Software
Deep Learning -> Adversarial Networks; Deep Learning
Deep Learning -> Biologically Plausible Deep Networks; Deep Learning
Deep Learning -> Biologically Plausible Deep Networks; Neuroscience and Cognitive Science
Deep Learning -> CNN Architectures; Deep Learning
Deep Learning -> Deep Autoencoders; Deep Learning -> Generative Models; Probabilistic Methods; Probabilistic Methods
Deep Learning; Deep Learning
Deep Learning; Deep Learning -> CNN Architectures; Theory
Deep Learning; Deep Learning -> Predictive Models; Deep Learning
Deep Learning -> Efficient Training Methods; Deep Learning
Deep Learning -> Predictive Models; Deep Learning
Deep Learning -> Recurrent Networks; Theory
Neuroscience and Cognitive Science
Neuroscience and Cognitive Science -> Cognitive Science; Neuroscience and Cognitive Science
Neuroscience and Cognitive Science -> Human or Animal Learning; Probabilistic Methods
Neuroscience and Cognitive Science -> Memory; Optimization -> Combinatorial Optimization; Optimization
Neuroscience and Cognitive Science -> Neural Coding; Neuroscience and Cognitive Science
Neuroscience and Cognitive Science -> Reasoning; Optimization
Optimization -> Convex Optimization; Probabilistic Methods; Theory; Theory
Optimization; Optimization
Probabilistic Methods; Probabilistic Methods
Reinforcement Learning and Planning
Reinforcement Learning and Planning -> Markov Decision Processes; Reinforcement Learning and Planning
Social Aspects of Machine Learning
Theory; Theory
Results
<<
<
Page 1 of 19
>
>>
Spotlight
Tue 5:25
Explainable Automated Graph Representation Learning with Hyperparameter Importance
Xin Wang · Shuyi Fan · Kun Kuang · Wenwu Zhu
Poster
Tue 9:00
Explainable Automated Graph Representation Learning with Hyperparameter Importance
Xin Wang · Shuyi Fan · Kun Kuang · Wenwu Zhu
Spotlight
Tue 5:40
Interpretable Stability Bounds for Spectral Graph Filters
Henry Kenlay · Dorina Thanou · Xiaowen Dong
Poster
Tue 9:00
Interpretable Stability Bounds for Spectral Graph Filters
Henry Kenlay · Dorina Thanou · Xiaowen Dong
Poster
Tue 9:00
Size-Invariant Graph Representations for Graph Classification Extrapolations
Beatrice Bevilacqua · Yangze Zhou · Bruno Ribeiro
Oral
Tue 5:00
Size-Invariant Graph Representations for Graph Classification Extrapolations
Beatrice Bevilacqua · Yangze Zhou · Bruno Ribeiro
Spotlight
Thu 6:35
Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization
Aseem Baranwal · Kimon Fountoulakis · Aukosh Jagannath
Spotlight
Tue 5:20
Consistent Nonparametric Methods for Network Assisted Covariate Estimation
Xueyu Mao · Deepayan Chakrabarti · Purnamrita Sarkar
Spotlight
Thu 5:35
GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings
Matthias Fey · Jan Eric Lenssen · Frank Weichert · Jure Leskovec
Spotlight
Tue 7:45
DeepWalking Backwards: From Embeddings Back to Graphs
Sudhanshu Chanpuriya · Cameron Musco · Konstantinos Sotiropoulos · Charalampos Tsourakakis
Poster
Thu 9:00
Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization
Aseem Baranwal · Kimon Fountoulakis · Aukosh Jagannath
Spotlight
Tue 7:25
GLSearch: Maximum Common Subgraph Detection via Learning to Search
Yunsheng Bai · Derek Xu · Yizhou Sun · Wei Wang
ICML uses cookies to remember that you are logged in. By using our websites, you agree to the placement of these cookies.
Our Privacy Policy »
Accept Cookies