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Monday
, December 7
210 C
10:45 •
Coffee Break
15:00 •
Coffee Break
210 C #1
19:00 •
Texture Synthesis Using Convolutional Neural Networks
210 C #10
19:00 •
Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation
210 C #100
19:00 •
Explore no more: Improved high-probability regret bounds for non-stochastic bandits
210 C #101
19:00 •
A Nonconvex Optimization Framework for Low Rank Matrix Estimation
210 C #102
19:00 •
Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability
210 C #11
19:00 •
Learning From Small Samples: An Analysis of Simple Decision Heuristics
210 C #12
19:00 •
3D Object Proposals for Accurate Object Class Detection
210 C #13
19:00 •
The Poisson Gamma Belief Network
210 C #14
19:00 •
Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data
210 C #15
19:00 •
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
210 C #16
19:00 •
Learning to Transduce with Unbounded Memory
210 C #17
19:00 •
Spectral Representations for Convolutional Neural Networks
210 C #18
19:00 •
A Theory of Decision Making Under Dynamic Context
210 C #19
19:00 •
Bidirectional Recurrent Neural Networks as Generative Models
210 C #2
19:00 •
Convolutional Neural Networks with Intra-Layer Recurrent Connections for Scene Labeling
210 C #20
19:00 •
Recognizing retinal ganglion cells in the dark
210 C #21
19:00 •
A Recurrent Latent Variable Model for Sequential Data
210 C #22
19:00 •
Deep Knowledge Tracing
210 C #23
19:00 •
Deep Temporal Sigmoid Belief Networks for Sequence Modeling
210 C #24
19:00 •
Hidden Technical Debt in Machine Learning Systems
210 C #25
19:00 •
Statistical Model Criticism using Kernel Two Sample Tests
210 C #26
19:00 •
Calibrated Structured Prediction
210 C #27
19:00 •
A Bayesian Framework for Modeling Confidence in Perceptual Decision Making
210 C #28
19:00 •
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation
210 C #29
19:00 •
Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models
210 C #3
19:00 •
Grammar as a Foreign Language
210 C #30
19:00 •
Robust Feature-Sample Linear Discriminant Analysis for Brain Disorders Diagnosis
210 C #31
19:00 •
Learning spatiotemporal trajectories from manifold-valued longitudinal data
210 C #32
19:00 •
Hessian-free Optimization for Learning Deep Multidimensional Recurrent Neural Networks
210 C #33
19:00 •
Scalable Inference for Gaussian Process Models with Black-Box Likelihoods
210 C #34
19:00 •
Variational Dropout and the Local Reparameterization Trick
210 C #35
19:00 •
Infinite Factorial Dynamical Model
210 C #36
19:00 •
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning
210 C #37
19:00 •
Copula variational inference
210 C #38
19:00 •
Fast Second Order Stochastic Backpropagation for Variational Inference
210 C #39
19:00 •
Rethinking LDA: Moment Matching for Discrete ICA
210 C #4
19:00 •
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction
210 C #40
19:00 •
Model-Based Relative Entropy Stochastic Search
210 C #41
19:00 •
Supervised Learning for Dynamical System Learning
210 C #42
19:00 •
Expectation Particle Belief Propagation
210 C #43
19:00 •
Embedding Inference for Structured Multilabel Prediction
210 C #44
19:00 •
Tractable Learning for Complex Probability Queries
210 C #45
19:00 •
Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing
210 C #46
19:00 •
Local Expectation Gradients for Black Box Variational Inference
210 C #47
19:00 •
Learning with a Wasserstein Loss
210 C #48
19:00 •
Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric
210 C #49
19:00 •
Fast and Accurate Inference of Plackett–Luce Models
210 C #5
19:00 •
Generative Image Modeling Using Spatial LSTMs
210 C #50
19:00 •
BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions
210 C #51
19:00 •
Learning with Relaxed Supervision
210 C #52
19:00 •
M-Statistic for Kernel Change-Point Detection
210 C #53
19:00 •
Fast Two-Sample Testing with Analytic Representations of Probability Measures
210 C #54
19:00 •
Adversarial Prediction Games for Multivariate Losses
210 C #55
19:00 •
Regressive Virtual Metric Learning
210 C #56
19:00 •
Halting in Random Walk Kernels
210 C #57
19:00 •
Rate-Agnostic (Causal) Structure Learning
210 C #58
19:00 •
Online Prediction at the Limit of Zero Temperature
210 C #59
19:00 •
Lifted Symmetry Detection and Breaking for MAP Inference
210 C #6
19:00 •
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
210 C #60
19:00 •
Bandits with Unobserved Confounders: A Causal Approach
210 C #61
19:00 •
Sample Complexity Bounds for Iterative Stochastic Policy Optimization
210 C #62
19:00 •
Basis refinement strategies for linear value function approximation in MDPs
210 C #63
19:00 •
Probabilistic Variational Bounds for Graphical Models
210 C #64
19:00 •
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators
210 C #65
19:00 •
An Active Learning Framework using Sparse-Graph Codes for Sparse Polynomials and Graph Sketching
210 C #66
19:00 •
Discrete Rényi Classifiers
210 C #67
19:00 •
GAP Safe screening rules for sparse multi-task and multi-class models
210 C #68
19:00 •
Decomposition Bounds for Marginal MAP
210 C #69
19:00 •
Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks
210 C #7
19:00 •
Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis
210 C #70
19:00 •
Estimating Mixture Models via Mixtures of Polynomials
210 C #71
19:00 •
Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso
210 C #72
19:00 •
Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation
210 C #73
19:00 •
Robust PCA with compressed data
210 C #74
19:00 •
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications
210 C #75
19:00 •
Subspace Clustering with Irrelevant Features via Robust Dantzig Selector
210 C #76
19:00 •
A class of network models recoverable by spectral clustering
210 C #77
19:00 •
Monotone k-Submodular Function Maximization with Size Constraints
210 C #78
19:00 •
Smooth and Strong: MAP Inference with Linear Convergence
210 C #79
19:00 •
StopWasting My Gradients: Practical SVRG
210 C #8
19:00 •
Exploring Models and Data for Image Question Answering
210 C #80
19:00 •
Spectral Norm Regularization of Orthonormal Representations for Graph Transduction
210 C #81
19:00 •
Differentially Private Learning of Structured Discrete Distributions
210 C #82
19:00 •
Robust Portfolio Optimization
210 C #83
19:00 •
Bayesian Optimization with Exponential Convergence
210 C #84
19:00 •
Fast Randomized Kernel Ridge Regression with Statistical Guarantees
210 C #85
19:00 •
Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms
210 C #86
19:00 •
Beyond Convexity: Stochastic Quasi-Convex Optimization
210 C #87
19:00 •
On the Limitation of Spectral Methods: From the Gaussian Hidden Clique Problem to Rank-One Perturbations of Gaussian Tensors
210 C #88
19:00 •
Regularized EM Algorithms: A Unified Framework and Statistical Guarantees
210 C #89
19:00 •
Black-box optimization of noisy functions with unknown smoothness
210 C #9
19:00 •
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question
210 C #90
19:00 •
Combinatorial Cascading Bandits
210 C #91
19:00 •
Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing
210 C #92
19:00 •
Sum-of-Squares Lower Bounds for Sparse PCA
210 C #93
19:00 •
Online Gradient Boosting
210 C #94
19:00 •
Regularization-Free Estimation in Trace Regression with Symmetric Positive Semidefinite Matrices
210 C #95
19:00 •
Convergence Analysis of Prediction Markets via Randomized Subspace Descent
210 C #96
19:00 •
Accelerated Proximal Gradient Methods for Nonconvex Programming
210 C #97
19:00 •
Nearly Optimal Private LASSO
210 C #98
19:00 •
Minimax Time Series Prediction
210 C #99
19:00 •
Communication Complexity of Distributed Convex Learning and Optimization
220 A
08:00 •
Breakfast
Level 2 room 210 AB
09:30 •
Deep Learning
13:00 •
Monte Carlo Inference Methods
15:30 •
Introduction to Reinforcement Learning with Function Approximation
Level 2 room 210 E,F
09:30 •
Large-Scale Distributed Systems for Training Neural Networks
13:00 •
Probabilistic Programming
15:30 •
High-Performance Hardware for Machine Learning
TBA
09:00 •
Conference Sessions Starter
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
Wednesday
, December 9
210 A
10:35 •
Coffee Break
12:00 •
Lunch Break
16:00 •
Coffee Break
210 C #1
19:00 •
Deep Visual Analogy-Making
210 C #10
19:00 •
Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze–like Environments
210 C #100
19:00 •
Adaptive Online Learning
210 C #11
19:00 •
Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding
210 C #12
19:00 •
The Return of the Gating Network: Combining Generative Models and Discriminative Training in Natural Image Priors
210 C #13
19:00 •
Backpropagation for Energy-Efficient Neuromorphic Computing
210 C #14
19:00 •
Learning Wake-Sleep Recurrent Attention Models
210 C #15
19:00 •
On-the-Job Learning with Bayesian Decision Theory
210 C #16
19:00 •
Color Constancy by Learning to Predict Chromaticity from Luminance
210 C #17
19:00 •
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
210 C #18
19:00 •
Action-Conditional Video Prediction using Deep Networks in Atari Games
210 C #19
19:00 •
Bayesian Active Model Selection with an Application to Automated Audiometry
210 C #2
19:00 •
Where are they looking?
210 C #20
19:00 •
Efficient and Robust Automated Machine Learning
210 C #21
19:00 •
A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure
210 C #22
19:00 •
Pointer Networks
210 C #23
19:00 •
A Reduced-Dimension fMRI Shared Response Model
210 C #24
19:00 •
Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets
210 C #25
19:00 •
Precision-Recall-Gain Curves: PR Analysis Done Right
210 C #26
19:00 •
A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding
210 C #27
19:00 •
Equilibrated adaptive learning rates for non-convex optimization
210 C #28
19:00 •
NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning
210 C #29
19:00 •
Gaussian Process Random Fields
210 C #3
19:00 •
Spatial Transformer Networks
210 C #30
19:00 •
MCMC for Variationally Sparse Gaussian Processes
210 C #31
19:00 •
Streaming, Distributed Variational Inference for Bayesian Nonparametrics
210 C #32
19:00 •
Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial
210 C #33
19:00 •
Human Memory Search as Initial-Visit Emitting Random Walk
210 C #34
19:00 •
Structured Transforms for Small-Footprint Deep Learning
210 C #35
19:00 •
Spectral Learning of Large Structured HMMs for Comparative Epigenomics
210 C #36
19:00 •
A Structural Smoothing Framework For Robust Graph Comparison
210 C #37
19:00 •
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference
210 C #38
19:00 •
Inverse Reinforcement Learning with Locally Consistent Reward Functions
210 C #39
19:00 •
Consistent Multilabel Classification
210 C #4
19:00 •
Training Very Deep Networks
210 C #40
19:00 •
Is Approval Voting Optimal Given Approval Votes?
210 C #41
19:00 •
A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks
210 C #42
19:00 •
Efficient Non-greedy Optimization of Decision Trees
210 C #43
19:00 •
Statistical Topological Data Analysis - A Kernel Perspective
210 C #44
19:00 •
Variational Consensus Monte Carlo
210 C #45
19:00 •
Softstar: Heuristic-Guided Probabilistic Inference
210 C #46
19:00 •
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families
210 C #47
19:00 •
A Complete Recipe for Stochastic Gradient MCMC
210 C #48
19:00 •
Barrier Frank-Wolfe for Marginal Inference
210 C #49
19:00 •
Practical and Optimal LSH for Angular Distance
210 C #5
19:00 •
Attention-Based Models for Speech Recognition
210 C #50
19:00 •
Principal Differences Analysis: Interpretable Characterization of Differences between Distributions
210 C #51
19:00 •
Kullback-Leibler Proximal Variational Inference
210 C #52
19:00 •
Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring
210 C #53
19:00 •
Streaming Min-max Hypergraph Partitioning
210 C #54
19:00 •
Efficient Output Kernel Learning for Multiple Tasks
210 C #55
19:00 •
Gradient Estimation Using Stochastic Computation Graphs
210 C #56
19:00 •
Lifted Inference Rules With Constraints
210 C #57
19:00 •
Sparse PCA via Bipartite Matchings
210 C #58
19:00 •
Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces
210 C #59
19:00 •
Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization
210 C #6
19:00 •
Deep Convolutional Inverse Graphics Network
210 C #60
19:00 •
Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach
210 C #61
19:00 •
Segregated Graphs and Marginals of Chain Graph Models
210 C #62
19:00 •
Approximating Sparse PCA from Incomplete Data
210 C #63
19:00 •
Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection
210 C #64
19:00 •
Recovering Communities in the General Stochastic Block Model Without Knowing the Parameters
210 C #65
19:00 •
Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets
210 C #66
19:00 •
Testing Closeness With Unequal Sized Samples
210 C #67
19:00 •
Learning Causal Graphs with Small Interventions
210 C #68
19:00 •
Regret-Based Pruning in Extensive-Form Games
210 C #69
19:00 •
Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations
210 C #7
19:00 •
End-To-End Memory Networks
210 C #70
19:00 •
Bounding errors of Expectation-Propagation
210 C #71
19:00 •
Market Scoring Rules Act As Opinion Pools For Risk-Averse Agents
210 C #72
19:00 •
Local Smoothness in Variance Reduced Optimization
210 C #73
19:00 •
High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality
210 C #74
19:00 •
Associative Memory via a Sparse Recovery Model
210 C #75
19:00 •
Matrix Completion Under Monotonic Single Index Models
210 C #76
19:00 •
Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent
210 C #77
19:00 •
Convergence rates of sub-sampled Newton methods
210 C #78
19:00 •
Variance Reduced Stochastic Gradient Descent with Neighbors
210 C #79
19:00 •
Non-convex Statistical Optimization for Sparse Tensor Graphical Model
210 C #8
19:00 •
Learning to Segment Object Candidates
210 C #80
19:00 •
Convergence Rates of Active Learning for Maximum Likelihood Estimation
210 C #81
19:00 •
When are Kalman-Filter Restless Bandits Indexable?
210 C #82
19:00 •
Policy Gradient for Coherent Risk Measures
210 C #83
19:00 •
A Dual Augmented Block Minimization Framework for Learning with Limited Memory
210 C #84
19:00 •
On the Global Linear Convergence of Frank-Wolfe Optimization Variants
210 C #85
19:00 •
Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling
210 C #86
19:00 •
A Generalization of Submodular Cover via the Diminishing Return Property on the Integer Lattice
210 C #87
19:00 •
A Universal Catalyst for First-Order Optimization
210 C #88
19:00 •
Fast and Memory Optimal Low-Rank Matrix Approximation
210 C #89
19:00 •
Stochastic Online Greedy Learning with Semi-bandit Feedbacks
210 C #9
19:00 •
Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets
210 C #90
19:00 •
Linear Multi-Resource Allocation with Semi-Bandit Feedback
210 C #91
19:00 •
Exactness of Approximate MAP Inference in Continuous MRFs
210 C #92
19:00 •
On the consistency theory of high dimensional variable screening
210 C #93
19:00 •
Finite-Time Analysis of Projected Langevin Monte Carlo
210 C #94
19:00 •
Optimal Testing for Properties of Distributions
210 C #95
19:00 •
Learning Theory and Algorithms for Forecasting Non-stationary Time Series
210 C #96
19:00 •
Accelerated Mirror Descent in Continuous and Discrete Time
210 C #97
19:00 •
Information-theoretic lower bounds for convex optimization with erroneous oracles
210 C #98
19:00 •
Bandit Smooth Convex Optimization: Improving the Bias-Variance Tradeoff
210 C #99
19:00 •
Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs
210D
19:00 •
Accelerated Deep Learning on GPUs: From Large Scale Training to Embedded Deployment
19:00 •
CodaLab Worksheets for Reproducible, Executable Papers
19:00 •
Data-Driven Speech Animation
19:00 •
Interactive Incremental Question Answering
19:00 •
Scaling up visual search for product recommendation
19:00 •
The pMMF multiresolution matrix factorization library
220 A
07:30 •
Breakfast
Level 2 room 210 AB
09:00 •
Post-selection Inference for Forward Stepwise Regression, Lasso and other Adaptive Statistical procedures
14:00 •
Diagnosis and Therapy of Psychiatric Disorders Based on Brain Dynamics
16:30 •
Computational Principles for Deep Neuronal Architectures
Room 210 A
09:50 •
Learning Theory and Algorithms for Forecasting Non-stationary Time Series
10:10 •
Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces
10:10 •
Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection
10:10 •
Market Scoring Rules Act As Opinion Pools For Risk-Averse Agents
10:10 •
Accelerated Mirror Descent in Continuous and Discrete Time
10:10 •
Bandit Smooth Convex Optimization: Improving the Bias-Variance Tradeoff
10:10 •
Information-theoretic lower bounds for convex optimization with erroneous oracles
10:10 •
Adaptive Online Learning
10:10 •
Optimal Testing for Properties of Distributions
10:55 •
Deep Visual Analogy-Making
10:55 •
End-To-End Memory Networks
11:35 •
Where are they looking?
11:35 •
Attention-Based Models for Speech Recognition
11:35 •
Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding
11:35 •
Learning to Segment Object Candidates
11:35 •
Spatial Transformer Networks
11:35 •
Training Very Deep Networks
11:35 •
Deep Convolutional Inverse Graphics Network
11:35 •
The Return of the Gating Network: Combining Generative Models and Discriminative Training in Natural Image Priors
14:50 •
A Reduced-Dimension fMRI Shared Response Model
14:50 •
Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze–like Environments
15:30 •
Color Constancy by Learning to Predict Chromaticity from Luminance
15:30 •
Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets
15:30 •
Backpropagation for Energy-Efficient Neuromorphic Computing
15:30 •
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
15:30 •
A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding
15:30 •
Learning Wake-Sleep Recurrent Attention Models
15:30 •
Action-Conditional Video Prediction using Deep Networks in Atari Games
15:30 •
On-the-Job Learning with Bayesian Decision Theory
17:20 •
Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets
17:40 •
Precision-Recall-Gain Curves: PR Analysis Done Right
17:40 •
Equilibrated adaptive learning rates for non-convex optimization
17:40 •
NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning
17:40 •
Pointer Networks
17:40 •
Structured Transforms for Small-Footprint Deep Learning
Thursday
, December 10
210 A
10:50 •
Coffee Break
210 a,b Level 2
15:00 •
Deep Learning Symposium
210 C #1
11:00 •
Teaching Machines to Read and Comprehend
210 C #10
11:00 •
Convolutional Networks on Graphs for Learning Molecular Fingerprints
210 C #100
11:00 •
Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models
210 C #11
11:00 •
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
210 C #12
11:00 •
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
210 C #13
11:00 •
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction
210 C #14
11:00 •
Max-Margin Deep Generative Models
210 C #15
11:00 •
Cross-Domain Matching for Bag-of-Words Data via Kernel Embeddings of Latent Distributions
210 C #16
11:00 •
A Gaussian Process Model of Quasar Spectral Energy Distributions
210 C #17
11:00 •
Neural Adaptive Sequential Monte Carlo
210 C #18
11:00 •
Convolutional spike-triggered covariance analysis for neural subunit models
210 C #19
11:00 •
Rectified Factor Networks
210 C #2
11:00 •
Saliency, Scale and Information: Towards a Unifying Theory
210 C #20
11:00 •
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
210 C #21
11:00 •
Bayesian dark knowledge
210 C #22
11:00 •
GP Kernels for Cross-Spectrum Analysis
210 C #23
11:00 •
End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture
210 C #24
11:00 •
Particle Gibbs for Infinite Hidden Markov Models
210 C #25
11:00 •
Sparse Local Embeddings for Extreme Multi-label Classification
210 C #26
11:00 •
Robust Spectral Inference for Joint Stochastic Matrix Factorization
210 C #27
11:00 •
Space-Time Local Embeddings
210 C #28
11:00 •
A fast, universal algorithm to learn parametric nonlinear embeddings
210 C #29
11:00 •
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)
210 C #3
11:00 •
Semi-supervised Learning with Ladder Networks
210 C #30
11:00 •
Local Causal Discovery of Direct Causes and Effects
210 C #31
11:00 •
Discriminative Robust Transformation Learning
210 C #32
11:00 •
Max-Margin Majority Voting for Learning from Crowds
210 C #33
11:00 •
M-Best-Diverse Labelings for Submodular Energies and Beyond
210 C #34
11:00 •
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling
210 C #35
11:00 •
Time-Sensitive Recommendation From Recurrent User Activities
210 C #36
11:00 •
Parallel Recursive Best-First AND/OR Search for Exact MAP Inference in Graphical Models
210 C #37
11:00 •
Logarithmic Time Online Multiclass prediction
210 C #38
11:00 •
Scalable Semi-Supervised Aggregation of Classifiers
210 C #39
11:00 •
Bounding the Cost of Search-Based Lifted Inference
210 C #4
11:00 •
Enforcing balance allows local supervised learning in spiking recurrent networks
210 C #40
11:00 •
Efficient Learning by Directed Acyclic Graph For Resource Constrained Prediction
210 C #41
11:00 •
Estimating Jaccard Index with Missing Observations: A Matrix Calibration Approach
210 C #42
11:00 •
Sample Efficient Path Integral Control under Uncertainty
210 C #43
11:00 •
Efficient Thompson Sampling for Online Matrix-Factorization Recommendation
210 C #44
11:00 •
Parallelizing MCMC with Random Partition Trees
210 C #45
11:00 •
Fast Lifted MAP Inference via Partitioning
210 C #46
11:00 •
Active Learning from Weak and Strong Labelers
210 C #47
11:00 •
Fast and Guaranteed Tensor Decomposition via Sketching
210 C #48
11:00 •
Spherical Random Features for Polynomial Kernels
210 C #49
11:00 •
Learnability of Influence in Networks
210 C #5
11:00 •
Semi-supervised Sequence Learning
210 C #50
11:00 •
A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA
210 C #51
11:00 •
Differentially private subspace clustering
210 C #52
11:00 •
Compressive spectral embedding: sidestepping the SVD
210 C #53
11:00 •
Generalization in Adaptive Data Analysis and Holdout Reuse
210 C #54
11:00 •
Online F-Measure Optimization
210 C #55
11:00 •
Matrix Completion with Noisy Side Information
210 C #56
11:00 •
A Market Framework for Eliciting Private Data
210 C #57
11:00 •
Optimal Ridge Detection using Coverage Risk
210 C #58
11:00 •
Fast Distributed k-Center Clustering with Outliers on Massive Data
210 C #59
11:00 •
Orthogonal NMF through Subspace Exploration
210 C #6
11:00 •
Skip-Thought Vectors
210 C #60
11:00 •
Fast Classification Rates for High-dimensional Gaussian Generative Models
210 C #61
11:00 •
Efficient and Parsimonious Agnostic Active Learning
210 C #62
11:00 •
Collaborative Filtering with Graph Information: Consistency and Scalable Methods
210 C #63
11:00 •
Less is More: Nyström Computational Regularization
210 C #64
11:00 •
Predtron: A Family of Online Algorithms for General Prediction Problems
210 C #65
11:00 •
On the Optimality of Classifier Chain for Multi-label Classification
210 C #66
11:00 •
Smooth Interactive Submodular Set Cover
210 C #67
11:00 •
Tractable Bayesian Network Structure Learning with Bounded Vertex Cover Number
210 C #68
11:00 •
Secure Multi-party Differential Privacy
210 C #69
11:00 •
Adaptive Stochastic Optimization: From Sets to Paths
210 C #7
11:00 •
Learning to Linearize Under Uncertainty
210 C #70
11:00 •
Learning structured densities via infinite dimensional exponential families
210 C #71
11:00 •
Lifelong Learning with Non-i.i.d. Tasks
210 C #72
11:00 •
Learning with Symmetric Label Noise: The Importance of Being Unhinged
210 C #73
11:00 •
Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits
210 C #74
11:00 •
From random walks to distances on unweighted graphs
210 C #75
11:00 •
Robust Regression via Hard Thresholding
210 C #76
11:00 •
Column Selection via Adaptive Sampling
210 C #77
11:00 •
Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms
210 C #78
11:00 •
Optimal Linear Estimation under Unknown Nonlinear Transform
210 C #79
11:00 •
Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach
210 C #8
11:00 •
Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring
210 C #80
11:00 •
Learning with Incremental Iterative Regularization
210 C #81
11:00 •
No-Regret Learning in Bayesian Games
210 C #82
11:00 •
Sparse and Low-Rank Tensor Decomposition
210 C #83
11:00 •
Analysis of Robust PCA via Local Incoherence
210 C #84
11:00 •
Algorithmic Stability and Uniform Generalization
210 C #85
11:00 •
Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path
210 C #86
11:00 •
Efficient Compressive Phase Retrieval with Constrained Sensing Vectors
210 C #87
11:00 •
Unified View of Matrix Completion under General Structural Constraints
210 C #88
11:00 •
Copeland Dueling Bandits
210 C #89
11:00 •
Regret Lower Bound and Optimal Algorithm in Finite Stochastic Partial Monitoring
210 C #9
11:00 •
Natural Neural Networks
210 C #90
11:00 •
Online Learning for Adversaries with Memory: Price of Past Mistakes
210 C #91
11:00 •
Revenue Optimization against Strategic Buyers
210 C #92
11:00 •
On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs
210 C #93
11:00 •
Improved Iteration Complexity Bounds of Cyclic Block Coordinate Descent for Convex Problems
210 C #94
11:00 •
Cornering Stationary and Restless Mixing Bandits with Remix-UCB
210 C #95
11:00 •
Fighting Bandits with a New Kind of Smoothness
210 C #96
11:00 •
Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care
210 C #97
11:00 •
The Pareto Regret Frontier for Bandits
210 C #98
11:00 •
Online Learning with Gaussian Payoffs and Side Observations
210 C #99
11:00 •
Fast Rates for Exp-concave Empirical Risk Minimization
210 e, f Level 2
15:00 •
Algorithms Among Us: the Societal Impacts of Machine Learning
220 A
07:30 •
Breakfast
220A
18:00 •
Hors d'oeuvres
Level 2 room 210 AB
09:00 •
Learning with Intelligent Teacher: Similarity Control and Knowledge Transfer
Level 5 Room 510 BD
15:00 •
Brains, Minds and Machines
Room 210 A
09:50 •
Less is More: Nyström Computational Regularization
10:10 •
Learning with Symmetric Label Noise: The Importance of Being Unhinged
10:10 •
Logarithmic Time Online Multiclass prediction
10:10 •
Fast and Guaranteed Tensor Decomposition via Sketching
10:10 •
Scalable Semi-Supervised Aggregation of Classifiers
10:10 •
Spherical Random Features for Polynomial Kernels
10:10 •
Collaborative Filtering with Graph Information: Consistency and Scalable Methods
10:10 •
Efficient and Parsimonious Agnostic Active Learning
10:10 •
Matrix Completion with Noisy Side Information
Friday
, December 11
220 A
07:30 •
Breakfast
510 ac
08:30 •
Optimization for Machine Learning (OPT2015)
510 db
08:30 •
Machine Learning For Healthcare (MLHC)
511 a
08:30 •
ABC in Montreal
511 b
08:30 •
Machine Learning for Spoken Language Understanding and Interactions
511 c
08:30 •
Nonparametric Methods for Large Scale Representation Learning
511 d
08:30 •
Learning Faster from Easy Data II
511 e
08:30 •
Modelling and inference for dynamics on complex interaction networks: joining up machine learning and statistical physics
511 f
08:30 •
Statistical Methods for Understanding Neural Systems
512 a
08:30 •
Probabilistic Integration
512 bf
08:30 •
Bounded Optimality and Rational Metareasoning
512 cg
08:30 •
Cognitive Computation: Integrating neural and symbolic approaches
512 dh
08:30 •
Multimodal Machine Learning
512 e
08:30 •
Machine Learning for (e-)Commerce
513 ab
08:30 •
Advances in Approximate Bayesian Inference
513 cd
08:30 •
Deep Reinforcement Learning
513 ef
08:30 •
The 1st International Workshop 'Feature Extraction: Modern Questions and Challenges'
514 a
08:30 •
Adaptive Data Analysis
514 bc
08:30 •
Time Series Workshop
515 bc
08:30 •
Applying (machine) Learning to Experimental Physics (ALEPH) and «Flavours of Physics» challenge
Foyer - 5th floor
10:00 •
Coffee Break
16:00 •
Coffee Break
Room 515 a
08:30 •
Machine Learning and Interpretation in Neuroimaging (day 1)
Saturday
, December 12
210
19:00 •
Banquet
220 A
07:30 •
Breakfast
510 ac
08:30 •
Reasoning, Attention, Memory (RAM) Workshop
510 db
08:30 •
Machine Learning in Computational Biology
511 a
08:30 •
Learning, Inference and Control of Multi-Agent Systems
511 b
08:30 •
Bayesian Optimization: Scalability and Flexibility
511 c
08:30 •
Multiresolution methods for large-scale learning
511 d
08:30 •
Machine Learning Systems
511 e
08:30 •
BigNeuro 2015: Making sense of big neural data
511 f
08:00 •
Extreme Classification 2015: Multi-class and Multi-label Learning in Extremely Large Label Spaces
512 a
08:30 •
Quantum Machine Learning
512 bf
08:30 •
Networks in the Social and Information Sciences
512 dh
08:30 •
Learning and privacy with incomplete data and weak supervision
512 e
08:30 •
Challenges in Machine Learning (CiML 2015): 'Open Innovation' and 'Coopetitions'
513 ab
08:30 •
Scalable Monte Carlo Methods for Bayesian Analysis of Big Data
513 cd
08:30 •
Non-convex Optimization for Machine Learning: Theory and Practice
513 ef
08:30 •
Black box learning and inference
514 a
08:30 •
Machine Learning From and For Adaptive User Technologies: From Active Learning & Experimentation to Optimization & Personalization
514 bc
08:30 •
Transfer and Multi-Task Learning: Trends and New Perspectives
515 bc
08:30 •
Bayesian Nonparametrics: The Next Generation
Foyer - 5th floor
10:00 •
Coffee Break
16:00 •
Coffee Break
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