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Indicates the beginning of a session

 Thu Dec 10, 2015

Time 210 C #20 210 C #18 210 C #24 210 C #98 210 C #81 210 C #34 210 C #71 210 C #46 210 C #85 210 C #54 210 C #3 210 C #94 210 C #83 210 C #51 210 C #62 210 C #58 210 C #8 210 C #21 210 C #10 210 C #97 210 C #95 210 C #13 210 C #61 210 C #15 210 C #11 210 C #100 210 C #68 210 C #66 210 C #76 210 C #57 210 C #48 210 C #14 210 C #52 210 C #28 210 C #26 210 C #40 210 C #43 210 C #42 210 C #4 210 C #37 210 C #45 210 C #80 210 C #82 210 C #6 210 C #49 210 C #30 210 C #87 210 C #1 210 C #78 210 C #96 210 C #12 210 C #70 210 C #22 210 C #39 210 C #89 210 C #77 210 C #75 210 C #84 210 C #67 210 C #25 210 C #19 210 C #32 210 C #2 210 C #7 210 C #29 210 C #72 210 C #59 210 C #60 210 C #38 210 C #33 210 C #63 210 C #90 210 C #50 210 C #88 210 C #86 210 C #92 210 C #56 210 C #35 210 C #99 210 C #55 210 C #16 210 C #5 210 C #73 210 C #41 210 C #53 210 C #69 210 C #47 210 C #91 210 C #65 210 C #36 210 C #74 210 C #44 210 C #79 210 C #64 210 C #9 210 C #31 210 C #27 210 C #17 210 C #93 210 C #23
11:00 AM (Posters)
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images 210 C #20
Convolutional spike-triggered covariance analysis for neural subunit models 210 C #18
Particle Gibbs for Infinite Hidden Markov Models 210 C #24
Online Learning with Gaussian Payoffs and Side Observations 210 C #98
No-Regret Learning in Bayesian Games 210 C #81
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling 210 C #34
Lifelong Learning with Non-i.i.d. Tasks 210 C #71
Active Learning from Weak and Strong Labelers 210 C #46
Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path 210 C #85
Online F-Measure Optimization 210 C #54
Semi-supervised Learning with Ladder Networks 210 C #3
Cornering Stationary and Restless Mixing Bandits with Remix-UCB 210 C #94
Analysis of Robust PCA via Local Incoherence 210 C #83
Differentially private subspace clustering 210 C #51
Collaborative Filtering with Graph Information: Consistency and Scalable Methods 210 C #62
Fast Distributed k-Center Clustering with Outliers on Massive Data 210 C #58
Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring 210 C #8
Bayesian dark knowledge 210 C #21
Convolutional Networks on Graphs for Learning Molecular Fingerprints 210 C #10
The Pareto Regret Frontier for Bandits 210 C #97
Fighting Bandits with a New Kind of Smoothness 210 C #95
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction 210 C #13
Efficient and Parsimonious Agnostic Active Learning 210 C #61
Cross-Domain Matching for Bag-of-Words Data via Kernel Embeddings of Latent Distributions 210 C #15
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting 210 C #11
Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models 210 C #100
Secure Multi-party Differential Privacy 210 C #68
Smooth Interactive Submodular Set Cover 210 C #66
Column Selection via Adaptive Sampling 210 C #76
Optimal Ridge Detection using Coverage Risk 210 C #57
Spherical Random Features for Polynomial Kernels 210 C #48
Max-Margin Deep Generative Models 210 C #14
Compressive spectral embedding: sidestepping the SVD 210 C #52
A fast, universal algorithm to learn parametric nonlinear embeddings 210 C #28
Robust Spectral Inference for Joint Stochastic Matrix Factorization 210 C #26
Efficient Learning by Directed Acyclic Graph For Resource Constrained Prediction 210 C #40
Efficient Thompson Sampling for Online Matrix-Factorization Recommendation 210 C #43
Sample Efficient Path Integral Control under Uncertainty 210 C #42
Enforcing balance allows local supervised learning in spiking recurrent networks 210 C #4
Logarithmic Time Online Multiclass prediction 210 C #37
Fast Lifted MAP Inference via Partitioning 210 C #45
Learning with Incremental Iterative Regularization 210 C #80
Sparse and Low-Rank Tensor Decomposition 210 C #82
Skip-Thought Vectors 210 C #6
Learnability of Influence in Networks 210 C #49
Local Causal Discovery of Direct Causes and Effects 210 C #30
Unified View of Matrix Completion under General Structural Constraints 210 C #87
Teaching Machines to Read and Comprehend 210 C #1
Optimal Linear Estimation under Unknown Nonlinear Transform 210 C #78
Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care 210 C #96
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks 210 C #12
Learning structured densities via infinite dimensional exponential families 210 C #70
GP Kernels for Cross-Spectrum Analysis 210 C #22
Bounding the Cost of Search-Based Lifted Inference 210 C #39
Regret Lower Bound and Optimal Algorithm in Finite Stochastic Partial Monitoring 210 C #89
Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms 210 C #77
Robust Regression via Hard Thresholding 210 C #75
Algorithmic Stability and Uniform Generalization 210 C #84
Tractable Bayesian Network Structure Learning with Bounded Vertex Cover Number 210 C #67
Sparse Local Embeddings for Extreme Multi-label Classification 210 C #25
Rectified Factor Networks 210 C #19
Max-Margin Majority Voting for Learning from Crowds 210 C #32
Saliency, Scale and Information: Towards a Unifying Theory 210 C #2
Learning to Linearize Under Uncertainty 210 C #7
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM) 210 C #29
Learning with Symmetric Label Noise: The Importance of Being Unhinged 210 C #72
Orthogonal NMF through Subspace Exploration 210 C #59
Fast Classification Rates for High-dimensional Gaussian Generative Models 210 C #60
Scalable Semi-Supervised Aggregation of Classifiers 210 C #38
M-Best-Diverse Labelings for Submodular Energies and Beyond 210 C #33
Less is More: Nyström Computational Regularization 210 C #63
Online Learning for Adversaries with Memory: Price of Past Mistakes 210 C #90
A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA 210 C #50
Copeland Dueling Bandits 210 C #88
Efficient Compressive Phase Retrieval with Constrained Sensing Vectors 210 C #86
On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs 210 C #92
A Market Framework for Eliciting Private Data 210 C #56
Time-Sensitive Recommendation From Recurrent User Activities 210 C #35
Fast Rates for Exp-concave Empirical Risk Minimization 210 C #99
Matrix Completion with Noisy Side Information 210 C #55
A Gaussian Process Model of Quasar Spectral Energy Distributions 210 C #16
Semi-supervised Sequence Learning 210 C #5
Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits 210 C #73
Estimating Jaccard Index with Missing Observations: A Matrix Calibration Approach 210 C #41
Generalization in Adaptive Data Analysis and Holdout Reuse 210 C #53
Adaptive Stochastic Optimization: From Sets to Paths 210 C #69
Fast and Guaranteed Tensor Decomposition via Sketching 210 C #47
Revenue Optimization against Strategic Buyers 210 C #91
On the Optimality of Classifier Chain for Multi-label Classification 210 C #65
Parallel Recursive Best-First AND/OR Search for Exact MAP Inference in Graphical Models 210 C #36
From random walks to distances on unweighted graphs 210 C #74
Parallelizing MCMC with Random Partition Trees 210 C #44
Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach 210 C #79
Predtron: A Family of Online Algorithms for General Prediction Problems 210 C #64
Natural Neural Networks 210 C #9
Discriminative Robust Transformation Learning 210 C #31
Space-Time Local Embeddings 210 C #27
Neural Adaptive Sequential Monte Carlo 210 C #17
Improved Iteration Complexity Bounds of Cyclic Block Coordinate Descent for Convex Problems 210 C #93
End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture 210 C #23