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Tuesday
, December 8
210 A
10:35 •
Coffee Break
12:00 •
Lunch Break
16:00 •
Coffee Break
210 C #1
19:00 •
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
210 C #10
19:00 •
Character-level Convolutional Networks for Text Classification
210 C #100
19:00 •
Structured Estimation with Atomic Norms: General Bounds and Applications
210 C #101
19:00 •
Subsampled Power Iteration: a Unified Algorithm for Block Models and Planted CSP's
210 C #11
19:00 •
Winner-Take-All Autoencoders
210 C #12
19:00 •
Learning both Weights and Connections for Efficient Neural Network
210 C #13
19:00 •
Interactive Control of Diverse Complex Characters with Neural Networks
210 C #14
19:00 •
Biologically Inspired Dynamic Textures for Probing Motion Perception
210 C #15
19:00 •
Unsupervised Learning by Program Synthesis
210 C #16
19:00 •
Deep Poisson Factor Modeling
210 C #17
19:00 •
Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings
210 C #18
19:00 •
Tensorizing Neural Networks
210 C #19
19:00 •
Training Restricted Boltzmann Machine via the Thouless-Anderson-Palmer free energy
210 C #2
19:00 •
Shepard Convolutional Neural Networks
210 C #20
19:00 •
The Brain Uses Reliability of Stimulus Information when Making Perceptual Decisions
210 C #21
19:00 •
Unlocking neural population non-stationarities using hierarchical dynamics models
210 C #22
19:00 •
Deeply Learning the Messages in Message Passing Inference
210 C #23
19:00 •
COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution
210 C #24
19:00 •
The Human Kernel
210 C #25
19:00 •
Latent Bayesian melding for integrating individual and population models
210 C #26
19:00 •
High-dimensional neural spike train analysis with generalized count linear dynamical systems
210 C #27
19:00 •
Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression
210 C #28
19:00 •
The Population Posterior and Bayesian Modeling on Streams
210 C #29
19:00 •
Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process
210 C #3
19:00 •
Learning Structured Output Representation using Deep Conditional Generative Models
210 C #30
19:00 •
Preconditioned Spectral Descent for Deep Learning
210 C #31
19:00 •
Learning Continuous Control Policies by Stochastic Value Gradients
210 C #32
19:00 •
Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels
210 C #33
19:00 •
Path-SGD: Path-Normalized Optimization in Deep Neural Networks
210 C #34
19:00 •
Automatic Variational Inference in Stan
210 C #35
19:00 •
Data Generation as Sequential Decision Making
210 C #36
19:00 •
Stochastic Expectation Propagation
210 C #37
19:00 •
Deep learning with Elastic Averaging SGD
210 C #38
19:00 •
Learning with Group Invariant Features: A Kernel Perspective.
210 C #39
19:00 •
Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes
210 C #4
19:00 •
Expressing an Image Stream with a Sequence of Natural Sentences
210 C #40
19:00 •
Probabilistic Line Searches for Stochastic Optimization
210 C #41
19:00 •
A hybrid sampler for Poisson-Kingman mixture models
210 C #42
19:00 •
Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models
210 C #43
19:00 •
Reflection, Refraction, and Hamiltonian Monte Carlo
210 C #44
19:00 •
Planar Ultrametrics for Image Segmentation
210 C #45
19:00 •
Learning Bayesian Networks with Thousands of Variables
210 C #46
19:00 •
Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions
210 C #47
19:00 •
Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width
210 C #48
19:00 •
On some provably correct cases of variational inference for topic models
210 C #49
19:00 •
Large-scale probabilistic predictors with and without guarantees of validity
210 C #5
19:00 •
Visalogy: Answering Visual Analogy Questions
210 C #50
19:00 •
On the Accuracy of Self-Normalized Log-Linear Models
210 C #51
19:00 •
Policy Evaluation Using the Ω-Return
210 C #52
19:00 •
Community Detection via Measure Space Embedding
210 C #53
19:00 •
The Consistency of Common Neighbors for Link Prediction in Stochastic Blockmodels
210 C #54
19:00 •
Inference for determinantal point processes without spectral knowledge
210 C #55
19:00 •
Sample Complexity of Learning Mahalanobis Distance Metrics
210 C #56
19:00 •
Matrix Manifold Optimization for Gaussian Mixtures
210 C #57
19:00 •
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees
210 C #58
19:00 •
Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients
210 C #59
19:00 •
The Self-Normalized Estimator for Counterfactual Learning
210 C #6
19:00 •
Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution
210 C #60
19:00 •
Distributionally Robust Logistic Regression
210 C #61
19:00 •
Top-k Multiclass SVM
210 C #62
19:00 •
Measuring Sample Quality with Stein's Method
210 C #63
19:00 •
Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization
210 C #64
19:00 •
Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems
210 C #65
19:00 •
Distributed Submodular Cover: Succinctly Summarizing Massive Data
210 C #66
19:00 •
Parallel Correlation Clustering on Big Graphs
210 C #67
19:00 •
Fast Bidirectional Probability Estimation in Markov Models
210 C #68
19:00 •
Evaluating the statistical significance of biclusters
210 C #69
19:00 •
Regularization Path of Cross-Validation Error Lower Bounds
210 C #7
19:00 •
SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals
210 C #70
19:00 •
Sampling from Probabilistic Submodular Models
210 C #71
19:00 •
Submodular Hamming Metrics
210 C #72
19:00 •
Extending Gossip Algorithms to Distributed Estimation of U-statistics
210 C #73
19:00 •
Newton-Stein Method: A Second Order Method for GLMs via Stein's Lemma
210 C #74
19:00 •
Collaboratively Learning Preferences from Ordinal Data
210 C #75
19:00 •
SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk
210 C #76
19:00 •
Alternating Minimization for Regression Problems with Vector-valued Outputs
210 C #77
19:00 •
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants
210 C #78
19:00 •
Subset Selection by Pareto Optimization
210 C #79
19:00 •
Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm
210 C #8
19:00 •
Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning
210 C #80
19:00 •
Minimum Weight Perfect Matching via Blossom Belief Propagation
210 C #81
19:00 •
b-bit Marginal Regression
210 C #82
19:00 •
LASSO with Non-linear Measurements is Equivalent to One With Linear Measurements
210 C #83
19:00 •
Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition
210 C #84
19:00 •
On the Pseudo-Dimension of Nearly Optimal Auctions
210 C #85
19:00 •
Closed-form Estimators for High-dimensional Generalized Linear Models
210 C #86
19:00 •
Fast, Provable Algorithms for Isotonic Regression in all L_p-norms
210 C #87
19:00 •
Semi-Proximal Mirror-Prox for Nonsmooth Composite Minimization
210 C #88
19:00 •
Competitive Distribution Estimation: Why is Good-Turing Good
210 C #89
19:00 •
A Universal Primal-Dual Convex Optimization Framework
210 C #9
19:00 •
Learning visual biases from human imagination
210 C #90
19:00 •
Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning
210 C #91
19:00 •
Private Graphon Estimation for Sparse Graphs
210 C #92
19:00 •
HONOR: Hybrid Optimization for NOn-convex Regularized problems
210 C #93
19:00 •
A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements
210 C #94
19:00 •
Super-Resolution Off the Grid
210 C #95
19:00 •
Optimal Rates for Random Fourier Features
210 C #96
19:00 •
Combinatorial Bandits Revisited
210 C #97
19:00 •
Fast Convergence of Regularized Learning in Games
210 C #98
19:00 •
On Elicitation Complexity
210 C #99
19:00 •
Online Learning with Adversarial Delays
210D
19:00 •
An interactive system for the extraction of meaningful visualizations from high-dimensional data
19:00 •
Claudico: The World's Strongest No-Limit Texas Hold'em Poker AI
19:00 •
Deep Learning using Approximate Hardware
19:00 •
DIANNE - Distributed Artificial Neural Networks
19:00 •
Fast sampling with neuromorphic hardware
19:00 •
Vitruvian Science: a visual editor for quickly building neural networks in the cloud
220 A
07:30 •
Breakfast
Level 2 room 210 AB
14:00 •
Incremental Methods for Additive Cost Convex Optimization
Room 210 A
09:50 •
Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition
10:10 •
Top-k Multiclass SVM
10:10 •
Optimal Rates for Random Fourier Features
10:10 •
Minimum Weight Perfect Matching via Blossom Belief Propagation
10:10 •
b-bit Marginal Regression
10:10 •
Super-Resolution Off the Grid
10:10 •
Submodular Hamming Metrics
10:10 •
LASSO with Non-linear Measurements is Equivalent to One With Linear Measurements
10:55 •
Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems
10:55 •
Sampling from Probabilistic Submodular Models
11:35 •
Extending Gossip Algorithms to Distributed Estimation of U-statistics
11:35 •
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees
11:35 •
Newton-Stein Method: A Second Order Method for GLMs via Stein's Lemma
11:35 •
Distributionally Robust Logistic Regression
11:35 •
On some provably correct cases of variational inference for topic models
11:35 •
Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization
11:35 •
Distributed Submodular Cover: Succinctly Summarizing Massive Data
11:35 •
The Self-Normalized Estimator for Counterfactual Learning
14:50 •
Probabilistic Line Searches for Stochastic Optimization
14:50 •
COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution
15:30 •
Automatic Variational Inference in Stan
15:30 •
Deep learning with Elastic Averaging SGD
15:30 •
Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes
15:30 •
Stochastic Expectation Propagation
15:30 •
Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width
15:30 •
Data Generation as Sequential Decision Making
15:30 •
Latent Bayesian melding for integrating individual and population models
16:30 •
Competitive Distribution Estimation: Why is Good-Turing Good
16:30 •
Fast Convergence of Regularized Learning in Games
16:30 •
Interactive Control of Diverse Complex Characters with Neural Networks
17:30 •
On the Pseudo-Dimension of Nearly Optimal Auctions
17:30 •
Measuring Sample Quality with Stein's Method
17:30 •
Closed-form Estimators for High-dimensional Generalized Linear Models
17:30 •
Biologically Inspired Dynamic Textures for Probing Motion Perception
17:30 •
High-dimensional neural spike train analysis with generalized count linear dynamical systems
17:30 •
The Human Kernel
17:30 •
Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings
17:30 •
Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels
Room 210 AB
09:00 •
Probabilistic Machine Learning: Foundations and Frontiers
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NIPS 2015
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210
210 A
210 a,b Level 2
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210 C #13
210 C #14
210 C #15
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210 C #27
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210 C #3
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210 C #32
210 C #33
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210 C #35
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210 C #39
210 C #4
210 C #40
210 C #41
210 C #42
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210 C #44
210 C #45
210 C #46
210 C #47
210 C #48
210 C #49
210 C #5
210 C #50
210 C #51
210 C #52
210 C #53
210 C #54
210 C #55
210 C #56
210 C #57
210 C #58
210 C #59
210 C #6
210 C #60
210 C #61
210 C #62
210 C #63
210 C #64
210 C #65
210 C #66
210 C #67
210 C #68
210 C #69
210 C #7
210 C #70
210 C #71
210 C #72
210 C #73
210 C #74
210 C #75
210 C #76
210 C #77
210 C #78
210 C #79
210 C #8
210 C #80
210 C #81
210 C #82
210 C #83
210 C #84
210 C #85
210 C #86
210 C #87
210 C #88
210 C #89
210 C #9
210 C #90
210 C #91
210 C #92
210 C #93
210 C #94
210 C #95
210 C #96
210 C #97
210 C #98
210 C #99
210 e, f Level 2
210D
220 A
220A
510 ac
510 db
511 a
511 b
511 c
511 d
511 e
511 f
512 a
512 bf
512 cg
512 dh
512 e
513 ab
513 cd
513 ef
514 a
514 bc
515 bc
Foyer - 5th floor
Level 2 room 210 AB
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210
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210 a,b Level 2
210 C
210 C #1
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210 C #33
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210 C #35
210 C #36
210 C #37
210 C #38
210 C #39
210 C #4
210 C #40
210 C #41
210 C #42
210 C #43
210 C #44
210 C #45
210 C #46
210 C #47
210 C #48
210 C #49
210 C #5
210 C #50
210 C #51
210 C #52
210 C #53
210 C #54
210 C #55
210 C #56
210 C #57
210 C #58
210 C #59
210 C #6
210 C #60
210 C #61
210 C #62
210 C #63
210 C #64
210 C #65
210 C #66
210 C #67
210 C #68
210 C #69
210 C #7
210 C #70
210 C #71
210 C #72
210 C #73
210 C #74
210 C #75
210 C #76
210 C #77
210 C #78
210 C #79
210 C #8
210 C #80
210 C #81
210 C #82
210 C #83
210 C #84
210 C #85
210 C #86
210 C #87
210 C #88
210 C #89
210 C #9
210 C #90
210 C #91
210 C #92
210 C #93
210 C #94
210 C #95
210 C #96
210 C #97
210 C #98
210 C #99
210 e, f Level 2
210D
220 A
220A
510 ac
510 db
511 a
511 b
511 c
511 d
511 e
511 f
512 a
512 bf
512 cg
512 dh
512 e
513 ab
513 cd
513 ef
514 a
514 bc
515 bc
Foyer - 5th floor
Level 2 room 210 AB
Level 2 room 210 E,F
Level 5 Room 510 BD
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