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Indicates the beginning of a session

 Wed Dec 04, 2013

Time Harvey's Convention Center Floor, CC
04:00 PM (Placeholders)
Registration Desk

 Thu Dec 05, 2013

Time Harvey's Convention Center Floor, CC Harrah's Special Events Center, 2nd Floor Emerald Bay B Emerald Bay A Harvey's: Garden Buffet, Promenade 1+2, Top of the Wheel, 12th Floor
07:30 AM (Placeholders)
Registration Desk
08:00 AM (Placeholders)
Breakfast
09:30 AM (Tutorials)
Tutorial Session B
Causes and Counterfactuals: Concepts, Principles and Tools.
Tutorial Session A
Deep Learning for Computer Vision
01:00 PM (Tutorials)
Tutorial Session B
Approximate Bayesian Computation (ABC)
Tutorial Session A
Deep Mathematical Properties of Submodularity with Applications to Machine Learning
03:30 PM (Tutorials)
Tutorial Session B
Learning to Interact
Tutorial Session A
Mechanisms Underlying Visual Object Recognition: Humans vs. Neurons vs. Machines
06:30 PM (Talks)
Opening Remarks
07:00 PM (Posters)
Poster Session

 Fri Dec 06, 2013

Time Harveys Convention Center Floor, CC Tahoe B, Harrah’s Special Events Center 2nd Floor Tahoe A, Harrah’s Special Events Center 2nd Floor Tahoe C, Harrah’s Special Events Center 2nd Floor Tahoe A+B, Harrah’s Special Events Center 2nd Floor Harvey's Convention Center Floor, CC Harrah's Special Events Center, 2nd Floor Harvey's: Garden Buffet, Promenade 1+2, Top of the Wheel, 12th Floor
07:30 AM (Placeholders)
Registration Desk
Breakfast
09:00 AM (Invited Talks)
Oral Session 1
Small, n=me, Data
09:50 AM (Orals)
Scalable Influence Estimation in Continuous-Time Diffusion Networks
10:10 AM (Spotlights)
Spotlight Session 1
Adaptive Anonymity via $b$-Matching
10:14 AM (Spotlights)
Exact and Stable Recovery of Pairwise Interaction Tensors
10:18 AM (Spotlights)
Matrix factorization with binary components
10:22 AM (Spotlights)
On the Complexity and Approximation of Binary Evidence in Lifted Inference
10:26 AM (Spotlights)
Unsupervised Spectral Learning of Finite State Transducers
11:00 AM (Orals)
On Decomposing the Proximal Map
11:20 AM (Orals)
Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty
11:40 AM (Spotlights)
Spotlight Session 2
Provable Subspace Clustering: When LRR meets SSC
11:44 AM (Spotlights)
Matrix Completion From any Given Set of Observations
11:48 AM (Spotlights)
Convex Two-Layer Modeling
11:52 AM (Spotlights)
Reconciling "priors'' & "priors" without prejudice?
11:56 AM (Spotlights)
Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model
12:00 PM (Spotlights)
Structured Learning via Logistic Regression
12:04 PM (Spotlights)
Graphical Models for Inference with Missing Data
02:00 PM (Invited Talks)
Oral Session 3
Memory Reactivation in Awake and Sleep States
02:50 PM (Orals)
Correlations strike back (again): the case of associative memory retrieval
03:10 PM (Orals)
A memory frontier for complex synapses
03:30 PM (Spotlights)
Spotlight Session 3
Bayesian entropy estimation for binary spike train data using parametric prior knowledge
03:34 PM (Spotlights)
Inferring neural population dynamics from multiple partial recordings of the same neural circuit
03:38 PM (Spotlights)
Noise-Enhanced Associative Memories
03:42 PM (Spotlights)
Demixing odors - fast inference in olfaction
03:46 PM (Spotlights)
Recurrent linear models of simultaneously-recorded neural populations
04:20 PM (Orals)
Understanding Dropout
04:40 PM (Orals)
Annealing between distributions by averaging moments
05:00 PM (Orals)
A simple example of Dirichlet process mixture inconsistency for the number of components
05:20 PM (Orals)
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs
05:40 PM (Spotlights)
Spotlight Session 4
Dropout Training as Adaptive Regularization
05:44 PM (Spotlights)
Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex
05:48 PM (Spotlights)
Restricting exchangeable nonparametric distributions
05:52 PM (Spotlights)
Approximate inference in latent Gaussian-Markov models from continuous time observations
05:56 PM (Spotlights)
Bayesian inference as iterated random functions with applications to sequential inference in graphical models
07:00 PM (Demonstrations, Posters)
The Three-Weight Algorithm: Enhancing ADMM for Large-Scale Distributed Optimization
NCS: A Novel CPU/GPU Simulation Environment for Large-Scale Biologically-Realistic Neural Modeling
A Mobile Development Platform for Adaptive Machine Learning and Neuromorphic Computing in Robotics
Codewebs: a Pedagogical Search Engine for Code Submissions to a MOOC
Di-BOSS™: Digital Building Operating System Solution
Accelerating Deep Neural Networks on Mobile Processor with Embedded Programmable Logic
Controlling Robot Dynamics With Spiking Neurons
Poster Session

 Sat Dec 07, 2013

Time Tahoe B, Harrah’s Special Events Center 2nd Floor Tahoe A, Harrah’s Special Events Center 2nd Floor Tahoe A+B, Harrah’s Special Events Center 2nd Floor Tahoe C Harvey's Convention Center Floor, CC Harrah's Special Events Center, 2nd Floor Harvey's: Garden Buffet, Promenade 1+2, Top of the Wheel, 12th Floor
07:30 AM (Placeholders)
Registration Desk
Breakfast
09:00 AM (Invited Talks)
Oral Session 5
The Online Revolution: Learning without Limits
09:50 AM (Orals)
Optimizing Instructional Policies
10:10 AM (Spotlights)
Spotlight Session 5
Linear decision rule as aspiration for simple decision heuristics
10:14 AM (Spotlights)
Scoring Workers in Crowdsourcing: How Many Control Questions are Enough?
10:18 AM (Spotlights)
Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits
10:22 AM (Spotlights)
Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis
10:26 AM (Spotlights)
Lasso Screening Rules via Dual Polytope Projection
11:00 AM (Orals)
A Kernel Test for Three-Variable Interactions
11:20 AM (Orals)
More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server
11:40 AM (Spotlights)
Spotlight Session 6
Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space
11:44 AM (Spotlights)
Learning Kernels Using Local Rademacher Complexity
11:48 AM (Spotlights)
Inverse Density as an Inverse Problem: the Fredholm Equation Approach
11:52 AM (Spotlights)
Regression-tree Tuning in a Streaming Setting
11:56 AM (Spotlights)
Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n)
02:00 PM (Invited Talks)
Oral Session 7
Belief Propagation Algorithms: From Matching Problems to Network Discovery in Cancer Genomics
02:50 PM (Orals)
Message Passing Inference with Chemical Reaction Networks
03:10 PM (Orals)
Information-theoretic lower bounds for distributed statistical estimation with communication constraints
03:30 PM (Spotlights)
Spotlight Session 7
PAC-Bayes-Empirical-Bernstein Inequality
03:34 PM (Spotlights)
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima
03:38 PM (Spotlights)
More data speeds up training time in learning halfspaces over sparse vectors
03:42 PM (Spotlights)
Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses
03:46 PM (Spotlights)
On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation
04:20 PM (Orals)
Oral Session 8
From Bandits to Experts: A Tale of Domination and Independence
04:40 PM (Orals)
Eluder Dimension and the Sample Complexity of Optimistic Exploration
05:00 PM (Orals)
Adaptive Market Making via Online Learning
05:20 PM (Orals)
Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints
05:40 PM (Spotlights)
Spotlight Session 8
How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal
05:44 PM (Spotlights)
Small-Variance Asymptotics for Hidden Markov Models
05:48 PM (Spotlights)
The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited
05:52 PM (Spotlights)
Using multiple samples to learn mixture models
05:56 PM (Spotlights)
Approximate Inference in Continuous Determinantal Processes
07:00 PM (Demonstrations, Posters)
Easy Text Classification with Machine Learning
Cross-Lingual Technologies: Text to Logic Mapping, Search and Classification over 100 Languages
Topic Modeling for Robots
Deep Content-Based Music Recommendation
Distributed Representations of Words and Phrases and their Compositionality
Making Smooth Topical Connections on Touch Devices
Demos of Deep Learning Technologies at Baidu IDL
Poster Session

 Sun Dec 08, 2013

Time Harvey's Convention Center Floor, CC Harrah's Special Events Center, 2nd Floor
07:30 AM (Placeholders)
Registration Desk
09:00 AM (Invited Talks)
Oral Session 9
Neural Reinforcement Learning
09:50 AM (Orals)
Actor-Critic Algorithms for Risk-Sensitive MDPs
10:10 AM (Spotlights)
Spotlight Session 9
Learning from Limited Demonstrations
10:14 AM (Spotlights)
Distributed Exploration in Multi-Armed Bandits
10:18 AM (Spotlights)
Dimension-Free Exponentiated Gradient
10:22 AM (Spotlights)
Generalizing Analytic Shrinkage for Arbitrary Covariance Structures
10:26 AM (Spotlights)
Robust Spatial Filtering with Beta Divergence
10:50 AM (Invited Talks)
Oral Session 10
New Methods for the Analysis of Genome Variation Data
11:40 AM (Orals)
BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables
12:00 PM (Spotlights)
Spotlight Session 10
Speeding up Permutation Testing in Neuroimaging
12:04 PM (Spotlights)
Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching
12:08 PM (Spotlights)
Deep Fisher Networks for Large-Scale Image Classification
12:12 PM (Spotlights)
Sinkhorn Distances: Lightspeed Computation of Optimal Transport
12:16 PM (Spotlights)
Understanding variable importances in forests of randomized trees
02:00 PM (Posters)
Poster Session

 Mon Dec 09, 2013

Time Harvey's Convention Center Floor, CC Harvey's: Garden Buffet, Promenade 1+2, Harrahs SEC Foyer Harrah's Fallen+Marla Harvey's Sierra Harrah's Tahoe A+B Harvey's Zephyr Harrah's Sand Harbor II Harvey's Emerald Bay 3 Harrah's Sand Harbor I Harrah's Sand Harbor III Harvey's Emerald Bay 2 Harvey's Emerald Bay B Harvey's Emerald Bay 6 Harvey's Emerald Bay 5 Harvey's Tallac Harvey's Emerald Bay 1 Harvey's Emerald Bay 4 Harrah's Tahoe D Harrah's Glenbrook+Emerald Harvey's Emerald Bay A Harrah's Tahoe C
06:30 AM (Placeholders)
Registration Desk
Breakfast
07:00 AM (Workshops)
Extreme Classification: Multi-Class & Multi-Label Learning with Millions of Categories
07:30 AM (Workshops)
MLINI-13: Machine Learning and Interpretation in Neuroimaging (Day 1)
Crowdsourcing: Theory, Algorithms and Applications
Modern Nonparametric Methods in Machine Learning
Deep Learning
Frontiers of Network Analysis: Methods, Models, and Applications
Discrete Optimization in Machine Learning: Connecting Theory and Practice
Output Representation Learning
Perturbations, Optimization, and Statistics
Big Learning : Advances in Algorithms and Data Management
High-dimensional Statistical Inference in the Brain
Randomized Methods for Machine Learning
Large Scale Matrix Analysis and Inference
Advances in Machine Learning for Sensorimotor Control
What Difference Does Personalization Make?
Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games.
NIPS 2013 Workshop on Causality: Large-scale Experiment Design and Inference of Causal Mechanisms
Probabilistic Models for Big Data
OPT2013: Optimization for Machine Learning

 Tue Dec 10, 2013

Time Harvey's Convention Center Floor, CC Harvey's: Garden Buffet, Promenade 1+2, Harrahs SEC Foyer Harrah's Sand Harbor Harrah's Fallen+Marla Harvey's Sierra Harvey's Zephyr Harvey's Emerald Bay 3 Harvey's Emerald Bay 2 Harvey's Emerald Bay B Harvey's Emerald Bay 6 Harvey's Emerald Bay 5 Harvey's Tallac Harvey's Emerald Bay 1 Harvey's Emerald Bay 4 Harrah's Tahoe D Harrah's Glenbrook+Emerald Harvey's Emerald Bay A Harrah's Tahoe C Harrah's Tahoe B Harrah's Tahoe A
06:30 AM (Placeholders)
Registration Desk
Breakfast
07:30 AM (Workshops)
New Directions in Transfer and Multi-Task: Learning Across Domains and Tasks
MLINI-13: Machine Learning and Interpretation in Neuroimaging (Day 2)
Machine Learning in Computational Biology
Resource-Efficient Machine Learning
Knowledge Extraction from Text (KET)
Topic Models: Computation, Application, and Evaluation
Greedy Algorithms, Frank-Wolfe and Friends - A modern perspective
Machine Learning Open Source Software: Towards Open Workflows
Machine Learning for Clinical Data Analysis and Healthcare
Constructive Machine Learning
Acquiring and Analyzing the Activity of Large Neural Ensembles
Data Driven Education
Machine Learning for Sustainability
Bayesian Optimization in Theory and Practice
Neural Information Processing Scaled for Bioacoustics : NIPS4B
Workshop on Spectral Learning
Learning Faster From Easy Data
07:00 PM (Placeholders)
Farewell Reception