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Search All 2020 Events
Filter by Keyword:
Algorithms
Algorithms -> Active Learning; Algorithms -> Classification; Algorithms
Algorithms -> Active Learning; Algorithms -> Regression; Deep Learning
Algorithms -> Active Learning; Deep Learning
Algorithms -> Active Learning; Theory
Algorithms -> Adversarial Learning; Algorithms
Algorithms -> Adversarial Learning; Algorithms -> Classification; Deep Learning
Algorithms -> AutoML; Applications -> Fairness, Accountability, and Transparency; Optimization
Algorithms -> AutoML; Optimization -> Non-Convex Optimization; Probabilistic Methods; Probabilistic Methods
Algorithms -> Bandit Algorithms; Reinforcement Learning and Planning -> Reinforcement Learning; Theory
Algorithms -> Boosting and Ensemble Methods; Applications -> Hardware and Systems; Applications
Algorithms -> Classification; Algorithms -> Few-Shot Learning; Algorithms -> Missing Data; Applications
Algorithms -> Classification; Algorithms -> Large Scale Learning; Applications
Algorithms -> Classification; Algorithms -> Meta-Learning; Algorithms -> Multitask and Transfer Learning; Algorithms
Algorithms -> Classification; Algorithms -> Meta-Learning; Applications
Algorithms -> Classification; Algorithms -> Online Learning; Applications -> Computer Vision; Deep Learning; Deep Learning
Algorithms -> Classification; Applications -> Activity and Event Recognition; Applications -> Computer Vision; Applications
Algorithms -> Classification; Deep Learning; Deep Learning
Algorithms -> Classification; Theory
Algorithms -> Clustering; Algorithms -> Semi-Supervised Learning; Theory
Algorithms -> Components Analysis (e.g., CCA, ICA, LDA, PCA); Algorithms -> Kernel Methods; Algorithms
Algorithms -> Density Estimation; Algorithms
Algorithms -> Density Estimation; Algorithms -> Similarity and Distance Learning; Applications
Algorithms -> Density Estimation; Algorithms -> Uncertainty Estimation; Algorithms -> Unsupervised Learning; Deep Learning
Algorithms -> Density Estimation; Algorithms -> Unsupervised Learning; Applications
Algorithms -> Density Estimation; Deep Learning -> Deep Autoencoders; Deep Learning
Algorithms -> Image Segmentation; Algorithms -> Semi-Supervised Learning; Applications -> Computer Vision; Applications
Algorithms -> Image Segmentation; Applications -> Computer Vision; Applications -> Image Segmentation; Applications
Algorithms -> Kernel Methods; Algorithms -> Multitask and Transfer Learning; Probabilistic Methods
Algorithms -> Kernel Methods; Algorithms -> Nonlinear Dimensionality Reduction and Manifold Learning; Probabilistic Methods
Algorithms -> Large Margin Methods; Deep Learning
Algorithms -> Large Scale Learning; Algorithms -> Online Learning; Algorithms -> Regression; Algorithms
Algorithms -> Large Scale Learning; Applications -> Natural Language Processing; Applications
Algorithms -> Large Scale Learning; Optimization
Algorithms -> Large Scale Learning; Optimization -> Convex Optimization; Theory -> Learning Theory; Theory
Algorithms -> Meta-Learning; Algorithms
Algorithms -> Meta-Learning; Algorithms -> Unsupervised Learning; Applications -> Computational Social Science; Applications
Algorithms -> Meta-Learning; Applications -> Object Recognition; Data, Challenges, Implementations, and Software
Algorithms -> Metric Learning; Algorithms
Algorithms -> Metric Learning; Algorithms -> Structured Prediction; Applications
Algorithms -> Missing Data; Algorithms -> Uncertainty Estimation; Probabilistic Methods
Algorithms -> Missing Data; Applications -> Sustainability; Deep Learning -> Adversarial Networks; Deep Learning
Algorithms -> Model Selection and Structure Learning; Algorithms -> Representation Learning; Theory
Algorithms -> Multitask and Transfer Learning; Algorithms
Algorithms -> Multitask and Transfer Learning; Algorithms -> Representation Learning; Algorithms
Algorithms -> Multitask and Transfer Learning; Applications -> Tracking and Motion in Video; Applications
Algorithms -> Multitask and Transfer Learning; Deep Learning
Algorithms -> Multitask and Transfer Learning; Deep Learning -> Supervised Deep Networks; Theory -> Learning Theory; Theory
Algorithms -> Online Learning; Optimization
Algorithms -> Online Learning; Theory
Algorithms -> Online Learning; Theory -> Computational Complexity; Theory
Algorithms -> Relational Learning; Algorithms
Algorithms -> Relational Learning; Applications -> Network Analysis; Deep Learning -> Attention Models; Deep Learning
Algorithms -> Representation Learning; Algorithms -> Structured Prediction; Applications
Algorithms -> Semi-Supervised Learning; Applications
Algorithms -> Semi-Supervised Learning; Deep Learning -> Deep Autoencoders; Deep Learning
Algorithms -> Sparsity and Compressed Sensing; Applications -> Computer Vision; Applications
Algorithms -> Sparsity and Compressed Sensing; Applications -> Information Retrieval; Applications
Algorithms -> Stochastic Methods; Deep Learning
Algorithms -> Uncertainty Estimation; Theory -> Frequentist Statistics; Theory
Algorithms -> Unsupervised Learning; Probabilistic Methods -> Graphical Models; Probabilistic Methods
Algorithms; Algorithms -> Online Learning; Optimization -> Combinatorial Optimization; Optimization
Algorithms; Algorithms -> Regression; Algorithms -> Similarity and Distance Learning; Optimization
Applications
Applications -> Computational Biology and Bioinformatics; Applications -> Health; Applications
Applications -> Computer Vision; Applications -> Denoising; Deep Learning -> Deep Autoencoders; Deep Learning
Applications -> Computer Vision; Applications -> Visual Scene Analysis and Interpretation; Deep Learning
Applications -> Computer Vision; Deep Learning
Applications -> Computer Vision; Deep Learning -> Deep Autoencoders; Deep Learning
Applications -> Privacy, Anonymity, and Security; Theory
Applications -> Robotics; Neuroscience and Cognitive Science
Applications -> Robotics; Reinforcement Learning and Planning -> Exploration; Reinforcement Learning and Planning
Applications -> Time Series Analysis; Probabilistic Methods
Applications -> Time Series Analysis; Theory
Applications -> Web Applications and Internet Data; Theory
Applications; Data, Challenges, Implementations, and Software; Data, Challenges, Implementations, and Software
Data, Challenges, Implementations, and Software
Data, Challenges, Implementations, and Software -> Benchmarks; Deep Learning
Data, Challenges, Implementations, and Software -> Virtual Environments; Deep Learning
Deep Learning
Deep Learning -> Adversarial Networks; Deep Learning -> Deep Autoencoders; Deep Learning
Deep Learning -> Optimization for Deep Networks; Optimization
Deep Learning -> Optimization for Deep Networks; Theory
Deep Learning -> Optimization for Deep Networks; Theory -> Computational Complexity; Theory
Deep Learning; Deep Learning -> CNN Architectures; Theory
Deep Learning; Deep Learning -> Optimization for Deep Networks; Theory
Neuroscience and Cognitive Science
Neuroscience and Cognitive Science -> Memory; Optimization -> Combinatorial Optimization; Optimization
Neuroscience and Cognitive Science -> Neuroscience; Neuroscience and Cognitive Science
Optimization
Optimization -> Convex Optimization; Optimization -> Non-Convex Optimization; Optimization
Optimization -> Non-Convex Optimization; Optimization
Optimization -> Non-Convex Optimization; Theory
Optimization -> Non-Convex Optimization; Theory -> Computational Complexity; Theory
Probabilistic Methods
Probabilistic Methods -> Bayesian Nonparametrics; Probabilistic Methods
Probabilistic Methods -> Causal Inference; Theory
Probabilistic Methods -> Gaussian Processes; Theory
Probabilistic Methods -> MCMC; Probabilistic Methods
Reinforcement Learning and Planning
Reinforcement Learning and Planning -> Exploration; Theory
Reinforcement Learning and Planning -> Markov Decision Processes; Reinforcement Learning and Planning
Reinforcement Learning and Planning -> Model-Based RL; Reinforcement Learning and Planning
Reinforcement Learning and Planning -> Planning; Reinforcement Learning and Planning
Theory
Theory -> Hardness of Learning and Approximations; Theory -> Large Deviations and Asymptotic Analysis; Theory
Theory; Theory
41 Results
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Poster
Wed 21:00
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
Oindrila Saha · Aditya Kusupati · Harsha Vardhan Simhadri · Manik Varma · Prateek Jain
Poster
Wed 9:00
Gradient Regularized V-Learning for Dynamic Treatment Regimes
Yao Zhang · Mihaela van der Schaar
Poster
Tue 21:00
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning
Xinyue Chen · Zijian Zhou · Zheng Wang · Che Wang · Yanqiu Wu · Keith Ross
Workshop
Fri 16:10
Invited Talk 11: Tensor Methods for Efficient and Interpretable Spatiotemporal Learning
Rose Yu
Poster
Tue 9:00
Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function
Lingkai Kong · Molei Tao
Spotlight
Tue 20:20
Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality
Nian Si · Jose Blanchet · Soumyadip Ghosh · Mark Squillante
Poster
Wed 9:00
Learning Differentiable Programs with Admissible Neural Heuristics
Ameesh Shah · Eric Zhan · Jennifer J Sun · Abhinav Verma · Yisong Yue · Swarat Chaudhuri
Poster
Tue 21:00
Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality
Nian Si · Jose Blanchet · Soumyadip Ghosh · Mark Squillante
Poster
Thu 9:00
Deep active inference agents using Monte-Carlo methods
Zafeirios Fountas · Noor Sajid · Pedro Mediano · Karl Friston
Poster
Mon 21:00
One-sample Guided Object Representation Disassembling
Zunlei Feng · Yongming He · Xinchao Wang · Xin Gao · Jie Lei · Cheng Jin · Mingli Song
Poster
Mon 21:00
Independent Policy Gradient Methods for Competitive Reinforcement Learning
Constantinos Daskalakis · Dylan Foster · Noah Golowich
Poster
Wed 21:00
Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method
Qi Zhou · Yufei Kuang · Zherui Qiu · Houqiang Li · Jie Wang
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