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210 C

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

Level 2 room 210 E,F

TBA

09:00 • Conference Sessions Starter