Getting Started
Schedule
Tutorials
Main Conference
Invited Talks
Orals
Awards
Test of Time Award
Papers
Workshops
Community
Affinity Events
Socials
Mentorship
Town Hall
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
89 Results
<<
<
Page 1 of 8
>
>>
Affinity Workshop
Mon 8:55
Computation of Discrete Flows Over Networks via Constrained Wasserstein Barycenters
Ferran Arque · Cesar Uribe
Spotlight
Thu 5:25
Variational Auto-Regressive Gaussian Processes for Continual Learning
Sanyam Kapoor · Theofanis Karaletsos · Thang Bui
Spotlight
Thu 20:30
Lenient Regret and Good-Action Identification in Gaussian Process Bandits
Xu Cai · Selwyn Gomes · Jonathan Scarlett
Workshop
Examining the Human Perceptibility of Black-Box Adversarial Attacks on Face Recognition
Benjamin Spetter-Goldstein · Nataniel Ruiz · Sarah Bargal
Poster
Thu 9:00
Variational Auto-Regressive Gaussian Processes for Continual Learning
Sanyam Kapoor · Theofanis Karaletsos · Thang Bui
Poster
Thu 21:00
Lenient Regret and Good-Action Identification in Gaussian Process Bandits
Xu Cai · Selwyn Gomes · Jonathan Scarlett
Spotlight
Wed 7:30
Bayesian Optimistic Optimisation with Exponentially Decaying Regret
Hung Tran-The · Sunil Gupta · Santu Rana · Svetha Venkatesh
Spotlight
Thu 20:45
High-Dimensional Gaussian Process Inference with Derivatives
Filip de Roos · Alexandra Gessner · Philipp Hennig
Spotlight
Thu 5:20
Isometric Gaussian Process Latent Variable Model for Dissimilarity Data
Martin Jørgensen · Søren Hauberg
Spotlight
Thu 5:30
Sparse within Sparse Gaussian Processes using Neighbor Information
Gia-Lac Tran · Dimitrios Milios · Pietro Michiardi · Maurizio Filippone
Spotlight
Thu 6:20
Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design
Gustavo Malkomes · Bolong Cheng · Eric Lee · Michael McCourt
Spotlight
Thu 17:35
Bias-Free Scalable Gaussian Processes via Randomized Truncations
Andres Potapczynski · Luhuan Wu · Dan Biderman · Geoff Pleiss · John Cunningham
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