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Search All 2021 Events
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Algorithms
Algorithms; Algorithms
Algorithms -> Bandit Algorithms; Reinforcement Learning and Planning
Algorithms -> Bandit Algorithms; Theory
Algorithms -> Classification; Algorithms -> Meta-Learning; Applications
Algorithms -> Classification; Applications -> Computational Biology and Bioinformatics; Applications
Algorithms -> Classification; Deep Learning -> CNN Architectures; Reinforcement Learning and Planning
Algorithms -> Classification; Deep Learning -> Predictive Models; Neuroscience and Cognitive Science
Algorithms -> Clustering; Theory
Algorithms -> Large Scale Learning; Algorithms -> Sparsity and Compressed Sensing; Algorithms
Algorithms -> Large Scale Learning; Optimization -> Convex Optimization; Optimization
Algorithms -> Missing Data; Algorithms
Algorithms -> Online Learning; Optimization
Algorithms, Optimization and Computation Methods
Algorithms -> Regression; Algorithms -> Uncertainty Estimation; Probabilistic Methods; Probabilistic Methods
Algorithms -> Regression; Optimization -> Convex Optimization; Theory -> Learning Theory; Theory
Algorithms -> Regression; Probabilistic Methods; Probabilistic Methods
Algorithms -> Representation Learning; Applications -> Computer Vision; Applications
Algorithms -> Semi-Supervised Learning; Applications -> Computer Vision; Applications
Algorithms -> Semi-Supervised Learning; Deep Learning -> Optimization for Deep Networks; Optimization
Algorithms -> Unsupervised Learning; Applications -> Computational Photography; Deep Learning
Algorithms -> Unsupervised Learning; Applications -> Computer Vision; Deep Learning
Applications
Applications -> Computer Vision; Applications -> Object Recognition; Deep Learning
Applications -> Computer Vision; Deep Learning -> CNN Architectures; Deep Learning -> Deep Autoencoders; Deep Learning
Applications -> Denoising; Applications -> Signal Processing; Deep Learning
Applications -> Denoising; Theory
Data, Challenges, Implementations, and Software
Deep Learning
Deep Learning -> CNN Architectures; Deep Learning
Ethics and Safety
Learning Theory and Statistics
Models and Methods
Neuroscience and Cognitive Science
Neuroscience and Cognitive Science -> Human or Animal Learning; Probabilistic Methods
Optimization
Probabilistic Methods
Reinforcement Learning and Planning
Theory
Results
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Poster
Tue 14:00
Sharp Analysis of a Simple Model for Random Forests
Jason Klusowski
Poster
Tue 14:00
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning
Zachary Charles · Jakub Konečný
Oral
Thu 15:15
A constrained risk inequality for general losses
John Duchi · Feng Ruan
Poster
Tue 14:00
A constrained risk inequality for general losses
John Duchi · Feng Ruan
Poster
Thu 7:30
Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates
Sebastian Stich · Amirkeivan Mohtashami · Martin Jaggi
Poster
Wed 12:45
Towards Flexible Device Participation in Federated Learning
Yichen Ruan · Xiaoxi Zhang · Shu-Che Liang · Carlee Joe-Wong
Poster
Wed 12:45
Large Scale K-Median Clustering for Stable Clustering Instances
Konstantin Voevodski
Poster
Wed 12:45
Direct Loss Minimization for Sparse Gaussian Processes
Yadi Wei · Rishit Sheth · Roni Khardon
Poster
Wed 12:45
A Deterministic Streaming Sketch for Ridge Regression
Benwei Shi · Jeff Phillips
Poster
Thu 7:30
Hadamard Wirtinger Flow for Sparse Phase Retrieval
Fan Wu · Patrick Rebeschini
Poster
Wed 6:00
Differentiating the Value Function by using Convex Duality
Sheheryar Mehmood · Peter Ochs
Poster
Thu 7:30
LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads
Hossein Shokri Ghadikolaei · Sebastian Stich · Martin Jaggi
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