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Wednesday
, December 4
Harvey's Convention Center Floor, CC
4:00pm •
Registration Desk
Thursday
, December 5
Emerald Bay A
9:30am •
Deep Learning for Computer Vision
1:00pm •
Deep Mathematical Properties of Submodularity with Applications to Machine Learning
3:30pm •
Mechanisms Underlying Visual Object Recognition: Humans vs. Neurons vs. Machines
Emerald Bay B
9:30am •
Causes and Counterfactuals: Concepts, Principles and Tools.
1:00pm •
Approximate Bayesian Computation (ABC)
3:30pm •
Learning to Interact
Harrah's Special Events Center, 2nd Floor
7:00pm •
Data-driven Distributionally Robust Polynomial Optimization
7:00pm •
Bayesian optimization explains human active search
7:00pm •
Generalized Random Utility Models with Multiple Types
7:00pm •
Polar Operators for Structured Sparse Estimation
7:00pm •
Optimal Neural Population Codes for High-dimensional Stimulus Variables
7:00pm •
Accelerating Stochastic Gradient Descent using Predictive Variance Reduction
7:00pm •
Accelerated Mini-Batch Stochastic Dual Coordinate Ascent
7:00pm •
Improved and Generalized Upper Bounds on the Complexity of Policy Iteration
7:00pm •
Online Learning of Nonparametric Mixture Models via Sequential Variational Approximation
7:00pm •
Least Informative Dimensions
7:00pm •
A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks
7:00pm •
Correlated random features for fast semi-supervised learning
7:00pm •
Better Approximation and Faster Algorithm Using the Proximal Average
7:00pm •
Auditing: Active Learning with Outcome-Dependent Query Costs
7:00pm •
A message-passing algorithm for multi-agent trajectory planning
7:00pm •
Blind Calibration in Compressed Sensing using Message Passing Algorithms
7:00pm •
Projecting Ising Model Parameters for Fast Mixing
7:00pm •
Mixed Optimization for Smooth Functions
7:00pm •
On the Linear Convergence of the Proximal Gradient Method for Trace Norm Regularization
7:00pm •
Latent Structured Active Learning
7:00pm •
A Gang of Bandits
7:00pm •
Efficient Algorithm for Privately Releasing Smooth Queries
7:00pm •
Low-Rank Matrix and Tensor Completion via Adaptive Sampling
7:00pm •
Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization Algorithms
7:00pm •
Supervised Sparse Analysis and Synthesis Operators
7:00pm •
Estimating LASSO Risk and Noise Level
7:00pm •
Linear Convergence with Condition Number Independent Access of Full Gradients
7:00pm •
When in Doubt, SWAP: High-Dimensional Sparse Recovery from Correlated Measurements
7:00pm •
Convex Relaxations for Permutation Problems
7:00pm •
Marginals-to-Models Reducibility
7:00pm •
Stochastic Convex Optimization with Multiple Objectives
7:00pm •
Memoized Online Variational Inference for Dirichlet Process Mixture Models
7:00pm •
Learning Prices for Repeated Auctions with Strategic Buyers
7:00pm •
Confidence Intervals and Hypothesis Testing for High-Dimensional Statistical Models
7:00pm •
Bayesian Estimation of Latently-grouped Parameters in Undirected Graphical Models
7:00pm •
Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions
7:00pm •
On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations
7:00pm •
Reflection methods for user-friendly submodular optimization
7:00pm •
Convex Tensor Decomposition via Structured Schatten Norm Regularization
7:00pm •
Learning Chordal Markov Networks by Constraint Satisfaction
7:00pm •
Computing the Stationary Distribution Locally
7:00pm •
Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion
7:00pm •
EDML for Learning Parameters in Directed and Undirected Graphical Models
7:00pm •
Similarity Component Analysis
7:00pm •
Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation
7:00pm •
Manifold-based Similarity Adaptation for Label Propagation
7:00pm •
Learning to Prune in Metric and Non-Metric Spaces
7:00pm •
Online learning in episodic Markovian decision processes by relative entropy policy search
7:00pm •
Pass-efficient unsupervised feature selection
7:00pm •
Streaming Variational Bayes
7:00pm •
Multiscale Dictionary Learning for Estimating Conditional Distributions
7:00pm •
Solving the multi-way matching problem by permutation synchronization
7:00pm •
When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity
7:00pm •
Online Learning of Dynamic Parameters in Social Networks
7:00pm •
A Graphical Transformation for Belief Propagation: Maximum Weight Matchings and Odd-Sized Cycles
7:00pm •
Approximate Gaussian process inference for the drift function in stochastic differential equations
7:00pm •
On the Sample Complexity of Subspace Learning
7:00pm •
Embed and Project: Discrete Sampling with Universal Hashing
7:00pm •
Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation
7:00pm •
Sensor Selection in High-Dimensional Gaussian Trees with Nuisances
7:00pm •
Buy-in-Bulk Active Learning
7:00pm •
Contrastive Learning Using Spectral Methods
7:00pm •
Speedup Matrix Completion with Side Information: Application to Multi-Label Learning
7:00pm •
Fast Determinantal Point Process Sampling with Application to Clustering
7:00pm •
Aggregating Optimistic Planning Trees for Solving Markov Decision Processes
7:00pm •
Robust learning of low-dimensional dynamics from large neural ensembles
7:00pm •
Scalable Inference for Logistic-Normal Topic Models
7:00pm •
Spectral methods for neural characterization using generalized quadratic models
7:00pm •
Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions
7:00pm •
Relevance Topic Model for Unstructured Social Group Activity Recognition
7:00pm •
Deep content-based music recommendation
7:00pm •
A Stability-based Validation Procedure for Differentially Private Machine Learning
7:00pm •
Cluster Trees on Manifolds
7:00pm •
Bayesian inference for low rank spatiotemporal neural receptive fields
7:00pm •
Adaptive Submodular Maximization in Bandit Setting
7:00pm •
Generalized Method-of-Moments for Rank Aggregation
7:00pm •
(Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings
7:00pm •
Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions
7:00pm •
Σ-Optimality for Active Learning on Gaussian Random Fields
7:00pm •
The Power of Asymmetry in Binary Hashing
7:00pm •
Estimation, Optimization, and Parallelism when Data is Sparse
7:00pm •
Modeling Overlapping Communities with Node Popularities
7:00pm •
An Approximate, Efficient LP Solver for LP Rounding
7:00pm •
Compressive Feature Learning
7:00pm •
Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition
7:00pm •
Efficient Exploration and Value Function Generalization in Deterministic Systems
7:00pm •
Learning and using language via recursive pragmatic reasoning about other agents
7:00pm •
Learning Stochastic Inverses
7:00pm •
Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream
Harvey's Convention Center Floor, CC
7:30am •
Registration Desk
6:30pm •
Opening Remarks
Harvey's: Garden Buffet, Promenade 1+2
10:45am •
Coffee Break
3:00pm •
Coffee Break
Harvey's: Garden Buffet, Promenade 1+2, Top of the Wheel, 12th Floor
8:00am •
Breakfast
Friday
, December 6
Harrah's Special Events Center, 2nd Floor
7:00pm •
Reciprocally Coupled Local Estimators Implement Bayesian Information Integration Distributively
7:00pm •
Provable Subspace Clustering: When LRR meets SSC
7:00pm •
On Decomposing the Proximal Map
7:00pm •
Transportability from Multiple Environments with Limited Experiments
7:00pm •
Causal Inference on Time Series using Restricted Structural Equation Models
7:00pm •
Variance Reduction for Stochastic Gradient Optimization
7:00pm •
A simple example of Dirichlet process mixture inconsistency for the number of components
7:00pm •
Correlations strike back (again): the case of associative memory retrieval
7:00pm •
Learning Hidden Markov Models from Non-sequence Data via Tensor Decomposition
7:00pm •
Dropout Training as Adaptive Regularization
7:00pm •
Learning Stochastic Feedforward Neural Networks
7:00pm •
Inferring neural population dynamics from multiple partial recordings of the same neural circuit
7:00pm •
Multi-Prediction Deep Boltzmann Machines
7:00pm •
Large Scale Distributed Sparse Precision Estimation
7:00pm •
Neural representation of action sequences: how far can a simple snippet-matching model take us?
7:00pm •
Structured Learning via Logistic Regression
7:00pm •
Stochastic blockmodel approximation of a graphon: Theory and consistent estimation
7:00pm •
Recurrent networks of coupled Winner-Take-All oscillators for solving constraint satisfaction problems
7:00pm •
Unsupervised Spectral Learning of Finite State Transducers
7:00pm •
Learning Multi-level Sparse Representations
7:00pm •
Regret based Robust Solutions for Uncertain Markov Decision Processes
7:00pm •
Generalized Denoising Auto-Encoders as Generative Models
7:00pm •
Low-rank matrix reconstruction and clustering via approximate message passing
7:00pm •
Reasoning With Neural Tensor Networks for Knowledge Base Completion
7:00pm •
Zero-Shot Learning Through Cross-Modal Transfer
7:00pm •
Efficient Online Inference for Bayesian Nonparametric Relational Models
7:00pm •
Approximate inference in latent Gaussian-Markov models from continuous time observations
7:00pm •
Wavelets on Graphs via Deep Learning
7:00pm •
A memory frontier for complex synapses
7:00pm •
Learning with Noisy Labels
7:00pm •
Graphical Models for Inference with Missing Data
7:00pm •
Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs
7:00pm •
A Deep Architecture for Matching Short Texts
7:00pm •
Nonparametric Multi-group Membership Model for Dynamic Networks
7:00pm •
Optimistic Concurrency Control for Distributed Unsupervised Learning
7:00pm •
Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering
7:00pm •
Robust Image Denoising with Multi-Column Deep Neural Networks
7:00pm •
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs
7:00pm •
Firing rate predictions in optimal balanced networks
7:00pm •
Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty
7:00pm •
Near-Optimal Entrywise Sampling for Data Matrices
7:00pm •
Bayesian Hierarchical Community Discovery
7:00pm •
Simultaneous Rectification and Alignment via Robust Recovery of Low-rank Tensors
7:00pm •
Noise-Enhanced Associative Memories
7:00pm •
Exact and Stable Recovery of Pairwise Interaction Tensors
7:00pm •
Bayesian entropy estimation for binary spike train data using parametric prior knowledge
7:00pm •
Perfect Associative Learning with Spike-Timing-Dependent Plasticity
7:00pm •
Matrix Completion From any Given Set of Observations
7:00pm •
Stochastic Optimization of PCA with Capped MSG
7:00pm •
Top-Down Regularization of Deep Belief Networks
7:00pm •
Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions
7:00pm •
Machine Teaching for Bayesian Learners in the Exponential Family
7:00pm •
A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data
7:00pm •
Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model
7:00pm •
Demixing odors - fast inference in olfaction
7:00pm •
Distributed Submodular Maximization: Identifying Representative Elements in Massive Data
7:00pm •
Discriminative Transfer Learning with Tree-based Priors
7:00pm •
Predicting Parameters in Deep Learning
7:00pm •
RNADE: The real-valued neural autoregressive density-estimator
7:00pm •
Reconciling 'priors'' & 'priors' without prejudice?
7:00pm •
Compete to Compute
7:00pm •
Discovering Hidden Variables in Noisy-Or Networks using Quartet Tests
7:00pm •
Universal models for binary spike patterns using centered Dirichlet processes
7:00pm •
Integrated Non-Factorized Variational Inference
7:00pm •
Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions
7:00pm •
Flexible sampling of discrete data correlations without the marginal distributions
7:00pm •
Restricting exchangeable nonparametric distributions
7:00pm •
Capacity of strong attractor patterns to model behavioural and cognitive prototypes
7:00pm •
Analyzing Hogwild Parallel Gaussian Gibbs Sampling
7:00pm •
Annealing between distributions by averaging moments
7:00pm •
Translating Embeddings for Modeling Multi-relational Data
7:00pm •
Phase Retrieval using Alternating Minimization
7:00pm •
Real-Time Inference for a Gamma Process Model of Neural Spiking
7:00pm •
Understanding Dropout
7:00pm •
On the Complexity and Approximation of Binary Evidence in Lifted Inference
7:00pm •
On the Expressive Power of Restricted Boltzmann Machines
7:00pm •
Memory Limited, Streaming PCA
7:00pm •
Bayesian inference as iterated random functions with applications to sequential inference in graphical models
7:00pm •
A New Convex Relaxation for Tensor Completion
7:00pm •
Variational Planning for Graph-based MDPs
7:00pm •
Convex Two-Layer Modeling
7:00pm •
Adaptive dropout for training deep neural networks
7:00pm •
Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex
7:00pm •
Distributed Representations of Words and Phrases and their Compositionality
7:00pm •
Recurrent linear models of simultaneously-recorded neural populations
7:00pm •
Scalable Influence Estimation in Continuous-Time Diffusion Networks
7:00pm •
Multisensory Encoding, Decoding, and Identification
7:00pm •
Adaptive Anonymity via $b$-Matching
7:00pm •
Matrix factorization with binary components
7:00pm •
Learning to Pass Expectation Propagation Messages
Harvey's Convention Center Floor, CC
9:00am •
Small, n=me, Data
9:50am •
Scalable Influence Estimation in Continuous-Time Diffusion Networks
10:10am •
Adaptive Anonymity via $b$-Matching
10:14am •
Exact and Stable Recovery of Pairwise Interaction Tensors
10:18am •
Matrix factorization with binary components
10:22am •
On the Complexity and Approximation of Binary Evidence in Lifted Inference
10:26am •
Unsupervised Spectral Learning of Finite State Transducers
11:00am •
On Decomposing the Proximal Map
11:20am •
Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty
11:40am •
Provable Subspace Clustering: When LRR meets SSC
11:44am •
Matrix Completion From any Given Set of Observations
11:48am •
Convex Two-Layer Modeling
11:52am •
Reconciling 'priors'' & 'priors' without prejudice?
11:56am •
Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model
12:00pm •
Structured Learning via Logistic Regression
12:04pm •
Graphical Models for Inference with Missing Data
2:00pm •
Memory Reactivation in Awake and Sleep States
2:50pm •
Correlations strike back (again): the case of associative memory retrieval
3:10pm •
A memory frontier for complex synapses
3:30pm •
Bayesian entropy estimation for binary spike train data using parametric prior knowledge
3:34pm •
Inferring neural population dynamics from multiple partial recordings of the same neural circuit
3:38pm •
Noise-Enhanced Associative Memories
3:42pm •
Demixing odors - fast inference in olfaction
3:46pm •
Recurrent linear models of simultaneously-recorded neural populations
4:20pm •
Understanding Dropout
4:40pm •
Annealing between distributions by averaging moments
5:00pm •
A simple example of Dirichlet process mixture inconsistency for the number of components
5:20pm •
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs
5:40pm •
Dropout Training as Adaptive Regularization
5:44pm •
Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex
5:48pm •
Restricting exchangeable nonparametric distributions
5:52pm •
Approximate inference in latent Gaussian-Markov models from continuous time observations
5:56pm •
Bayesian inference as iterated random functions with applications to sequential inference in graphical models
Harvey's: Garden Buffet, Promenade 1+2
10:30am •
Coffee Break
3:50pm •
Coffee Break
Harvey's: Garden Buffet, Promenade 1+2, Top of the Wheel, 12th Floor
7:30am •
Breakfast
Harveys Convention Center Floor, CC
7:30am •
Registration Desk
Tahoe A+B, Harrah’s Special Events Center 2nd Floor
7:00pm •
Accelerating Deep Neural Networks on Mobile Processor with Embedded Programmable Logic
7:00pm •
Controlling Robot Dynamics With Spiking Neurons
Tahoe A, Harrah’s Special Events Center 2nd Floor
7:00pm •
A Mobile Development Platform for Adaptive Machine Learning and Neuromorphic Computing in Robotics
7:00pm •
Codewebs: a Pedagogical Search Engine for Code Submissions to a MOOC
Tahoe B, Harrah’s Special Events Center 2nd Floor
7:00pm •
The Three-Weight Algorithm: Enhancing ADMM for Large-Scale Distributed Optimization
7:00pm •
NCS: A Novel CPU/GPU Simulation Environment for Large-Scale Biologically-Realistic Neural Modeling
Tahoe C, Harrah’s Special Events Center 2nd Floor
7:00pm •
Di-BOSS™: Digital Building Operating System Solution
Saturday
, December 7
Harrah's Special Events Center, 2nd Floor
7:00pm •
The Randomized Dependence Coefficient
7:00pm •
Latent Maximum Margin Clustering
7:00pm •
PAC-Bayes-Empirical-Bernstein Inequality
7:00pm •
More data speeds up training time in learning halfspaces over sparse vectors
7:00pm •
Density estimation from unweighted k-nearest neighbor graphs: a roadmap
7:00pm •
Summary Statistics for Partitionings and Feature Allocations
7:00pm •
One-shot learning and big data with n=2
7:00pm •
Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression
7:00pm •
Online Variational Approximations to non-Exponential Family Change Point Models: With Application to Radar Tracking
7:00pm •
Using multiple samples to learn mixture models
7:00pm •
New Subsampling Algorithms for Fast Least Squares Regression
7:00pm •
Faster Ridge Regression via the Subsampled Randomized Hadamard Transform
7:00pm •
Online Robust PCA via Stochastic Optimization
7:00pm •
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture
7:00pm •
Rapid Distance-Based Outlier Detection via Sampling
7:00pm •
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima
7:00pm •
q-OCSVM: A q-Quantile Estimator for High-Dimensional Distributions
7:00pm •
Dirty Statistical Models
7:00pm •
Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent
7:00pm •
Prior-free and prior-dependent regret bounds for Thompson Sampling
7:00pm •
Which Space Partitioning Tree to Use for Search?
7:00pm •
B-test: A Non-parametric, Low Variance Kernel Two-sample Test
7:00pm •
Online PCA for Contaminated Data
7:00pm •
Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n)
7:00pm •
Robust Data-Driven Dynamic Programming
7:00pm •
A Comparative Framework for Preconditioned Lasso Algorithms
7:00pm •
A Latent Source Model for Nonparametric Time Series Classification
7:00pm •
Efficient Optimization for Sparse Gaussian Process Regression
7:00pm •
A Kernel Test for Three-Variable Interactions
7:00pm •
Designed Measurements for Vector Count Data
7:00pm •
Robust Transfer Principal Component Analysis with Rank Constraints
7:00pm •
Probabilistic Principal Geodesic Analysis
7:00pm •
More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server
7:00pm •
Statistical Active Learning Algorithms
7:00pm •
Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits
7:00pm •
Unsupervised Structure Learning of Stochastic And-Or Grammars
7:00pm •
Multiclass Total Variation Clustering
7:00pm •
Approximate Inference in Continuous Determinantal Processes
7:00pm •
Thompson Sampling for 1-Dimensional Exponential Family Bandits
7:00pm •
It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals
7:00pm •
Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses
7:00pm •
Inverse Density as an Inverse Problem: the Fredholm Equation Approach
7:00pm •
From Bandits to Experts: A Tale of Domination and Independence
7:00pm •
Predictive PAC Learning and Process Decompositions
7:00pm •
Bayesian Mixture Modelling and Inference based Thompson Sampling in Monte-Carlo Tree Search
7:00pm •
Solving inverse problem of Markov chain with partial observations
7:00pm •
Locally Adaptive Bayesian Multivariate Time Series
7:00pm •
Gaussian Process Conditional Copulas with Applications to Financial Time Series
7:00pm •
Regression-tree Tuning in a Streaming Setting
7:00pm •
On Flat versus Hierarchical Classification in Large-Scale Taxonomies
7:00pm •
Robust Bloom Filters for Large MultiLabel Classification Tasks
7:00pm •
Scoring Workers in Crowdsourcing: How Many Control Questions are Enough?
7:00pm •
Learning Multiple Models via Regularized Weighting
7:00pm •
Distributed k-means and k-median clustering on general communication topologies
7:00pm •
Multi-Task Bayesian Optimization
7:00pm •
Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space
7:00pm •
Adaptive Market Making via Online Learning
7:00pm •
Spike train entropy-rate estimation using hierarchical Dirichlet process priors
7:00pm •
Small-Variance Asymptotics for Hidden Markov Models
7:00pm •
What do row and column marginals reveal about your dataset?
7:00pm •
Two-Target Algorithms for Infinite-Armed Bandits with Bernoulli Rewards
7:00pm •
Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis
7:00pm •
Sequential Transfer in Multi-armed Bandit with Finite Set of Models
7:00pm •
Message Passing Inference with Chemical Reaction Networks
7:00pm •
Eluder Dimension and the Sample Complexity of Optimistic Exploration
7:00pm •
Stochastic Majorization-Minimization Algorithms for Large-Scale Optimization
7:00pm •
Information-theoretic lower bounds for distributed statistical estimation with communication constraints
7:00pm •
How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal
7:00pm •
Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion
7:00pm •
The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited
7:00pm •
Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints
7:00pm •
Synthesizing Robust Plans under Incomplete Domain Models
7:00pm •
One-shot learning by inverting a compositional causal process
7:00pm •
Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators.
7:00pm •
Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis
7:00pm •
Sign Cauchy Projections and Chi-Square Kernel
7:00pm •
k-Prototype Learning for 3D Rigid Structures
7:00pm •
Multilinear Dynamical Systems for Tensor Time Series
7:00pm •
Fantope Projection and Selection: A near-optimal convex relaxation of sparse PCA
7:00pm •
Learning Kernels Using Local Rademacher Complexity
7:00pm •
Optimizing Instructional Policies
7:00pm •
Linear decision rule as aspiration for simple decision heuristics
7:00pm •
On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation
7:00pm •
Moment-based Uniform Deviation Bounds for $k$-means and Friends
7:00pm •
Sketching Structured Matrices for Faster Nonlinear Regression
7:00pm •
Adaptivity to Local Smoothness and Dimension in Kernel Regression
7:00pm •
Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodel
7:00pm •
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC
7:00pm •
The Fast Convergence of Incremental PCA
7:00pm •
Robust Low Rank Kernel Embeddings of Multivariate Distributions
7:00pm •
Lasso Screening Rules via Dual Polytope Projection
Harvey's Convention Center Floor, CC
7:30am •
Registration Desk
9:00am •
The Online Revolution: Learning without Limits
9:50am •
Optimizing Instructional Policies
10:10am •
Linear decision rule as aspiration for simple decision heuristics
10:14am •
Scoring Workers in Crowdsourcing: How Many Control Questions are Enough?
10:18am •
Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits
10:22am •
Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis
10:26am •
Lasso Screening Rules via Dual Polytope Projection
11:00am •
A Kernel Test for Three-Variable Interactions
11:20am •
More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server
11:40am •
Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space
11:44am •
Learning Kernels Using Local Rademacher Complexity
11:48am •
Inverse Density as an Inverse Problem: the Fredholm Equation Approach
11:52am •
Regression-tree Tuning in a Streaming Setting
11:56am •
Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n)
2:00pm •
Belief Propagation Algorithms: From Matching Problems to Network Discovery in Cancer Genomics
2:50pm •
Message Passing Inference with Chemical Reaction Networks
3:10pm •
Information-theoretic lower bounds for distributed statistical estimation with communication constraints
3:30pm •
PAC-Bayes-Empirical-Bernstein Inequality
3:34pm •
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima
3:38pm •
More data speeds up training time in learning halfspaces over sparse vectors
3:42pm •
Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses
3:46pm •
On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation
4:20pm •
From Bandits to Experts: A Tale of Domination and Independence
4:40pm •
Eluder Dimension and the Sample Complexity of Optimistic Exploration
5:00pm •
Adaptive Market Making via Online Learning
5:20pm •
Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints
5:40pm •
How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal
5:44pm •
Small-Variance Asymptotics for Hidden Markov Models
5:48pm •
The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited
5:52pm •
Using multiple samples to learn mixture models
5:56pm •
Approximate Inference in Continuous Determinantal Processes
Harvey's: Garden Buffet, Promenade 1+2
10:30am •
Coffee Break
3:50pm •
Coffee Break
Harvey's: Garden Buffet, Promenade 1+2, Top of the Wheel, 12th Floor
7:30am •
Breakfast
Tahoe A+B, Harrah’s Special Events Center 2nd Floor
7:00pm •
Distributed Representations of Words and Phrases and their Compositionality
7:00pm •
Making Smooth Topical Connections on Touch Devices
Tahoe A, Harrah’s Special Events Center 2nd Floor
7:00pm •
Topic Modeling for Robots
7:00pm •
Deep Content-Based Music Recommendation
Tahoe B, Harrah’s Special Events Center 2nd Floor
7:00pm •
Easy Text Classification with Machine Learning
7:00pm •
Cross-Lingual Technologies: Text to Logic Mapping, Search and Classification over 100 Languages
Tahoe C
7:00pm •
Demos of Deep Learning Technologies at Baidu IDL
Sunday
, December 8
Harrah's Special Events Center, 2nd Floor
2:00pm •
Documents as multiple overlapping windows into grids of counts
2:00pm •
Transfer Learning in a Transductive Setting
2:00pm •
Point Based Value Iteration with Optimal Belief Compression for Dec-POMDPs
2:00pm •
Modeling Clutter Perception using Parametric Proto-object Partitioning
2:00pm •
Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching
2:00pm •
Deep Fisher Networks for Large-Scale Image Classification
2:00pm •
Sparse Additive Text Models with Low Rank Background
2:00pm •
Training and Analysing Deep Recurrent Neural Networks
2:00pm •
Variational Policy Search via Trajectory Optimization
2:00pm •
Scalable kernels for graphs with continuous attributes
2:00pm •
Decision Jungles: Compact and Rich Models for Classification
2:00pm •
What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach
2:00pm •
Actor-Critic Algorithms for Risk-Sensitive MDPs
2:00pm •
On model selection consistency of penalized M-estimators: a geometric theory
2:00pm •
Understanding variable importances in forests of randomized trees
2:00pm •
Non-Linear Domain Adaptation with Boosting
2:00pm •
Mid-level Visual Element Discovery as Discriminative Mode Seeking
2:00pm •
Higher Order Priors for Joint Intrinsic Image, Objects, and Attributes Estimation
2:00pm •
Learning Trajectory Preferences for Manipulators via Iterative Improvement
2:00pm •
On Algorithms for Sparse Multi-factor NMF
2:00pm •
Parallel Sampling of DP Mixture Models using Sub-Cluster Splits
2:00pm •
Conditional Random Fields via Univariate Exponential Families
2:00pm •
Reinforcement Learning in Robust Markov Decision Processes
2:00pm •
Learning Feature Selection Dependencies in Multi-task Learning
2:00pm •
Beyond Pairwise: Provably Fast Algorithms for Approximate $k$-Way Similarity Search
2:00pm •
Learning a Deep Compact Image Representation for Visual Tracking
2:00pm •
Distributed Exploration in Multi-Armed Bandits
2:00pm •
The Pareto Regret Frontier
2:00pm •
Direct 0-1 Loss Minimization and Margin Maximization with Boosting
2:00pm •
Speeding up Permutation Testing in Neuroimaging
2:00pm •
Learning Adaptive Value of Information for Structured Prediction
2:00pm •
Robust Spatial Filtering with Beta Divergence
2:00pm •
High-Dimensional Gaussian Process Bandits
2:00pm •
First-order Decomposition Trees
2:00pm •
Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent
2:00pm •
Lexical and Hierarchical Topic Regression
2:00pm •
Online Learning with Switching Costs and Other Adaptive Adversaries
2:00pm •
Tracking Time-varying Graphical Structure
2:00pm •
Factorized Asymptotic Bayesian Inference for Latent Feature Models
2:00pm •
Online Learning with Costly Features and Labels
2:00pm •
A Novel Two-Step Method for Cross Language Representation Learning
2:00pm •
Reshaping Visual Datasets for Domain Adaptation
2:00pm •
Parametric Task Learning
2:00pm •
Adaptive Step-Size for Policy Gradient Methods
2:00pm •
Reservoir Boosting : Between Online and Offline Ensemble Learning
2:00pm •
Optimistic policy iteration and natural actor-critic: A unifying view and a non-optimality result
2:00pm •
Mapping paradigm ontologies to and from the brain
2:00pm •
On Poisson Graphical Models
2:00pm •
Extracting regions of interest from biological images with convolutional sparse block coding
2:00pm •
Approximate Dynamic Programming Finally Performs Well in the Game of Tetris
2:00pm •
Third-Order Edge Statistics: Contour Continuation, Curvature, and Cortical Connections
2:00pm •
DESPOT: Online POMDP Planning with Regularization
2:00pm •
Dimension-Free Exponentiated Gradient
2:00pm •
Learning Gaussian Graphical Models with Observed or Latent FVSs
2:00pm •
Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies
2:00pm •
Generalizing Analytic Shrinkage for Arbitrary Covariance Structures
2:00pm •
Heterogeneous-Neighborhood-based Multi-Task Local Learning Algorithms
2:00pm •
Action from Still Image Dataset and Inverse Optimal Control to Learn Task Specific Visual Scanpaths
2:00pm •
Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation
2:00pm •
Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic
2:00pm •
Convergence of Monte Carlo Tree Search in Simultaneous Move Games
2:00pm •
DeViSE: A Deep Visual-Semantic Embedding Model
2:00pm •
Reward Mapping for Transfer in Long-Lived Agents
2:00pm •
Estimating the Unseen: Improved Estimators for Entropy and other Properties
2:00pm •
Learning word embeddings efficiently with noise-contrastive estimation
2:00pm •
Sparse Inverse Covariance Estimation with Calibration
2:00pm •
Sinkhorn Distances: Lightspeed Computation of Optimal Transport
2:00pm •
Projected Natural Actor-Critic
2:00pm •
Learning the Local Statistics of Optical Flow
2:00pm •
Estimation Bias in Multi-Armed Bandit Algorithms for Search Advertising
2:00pm •
Action is in the Eye of the Beholder: Eye-gaze Driven Model for Spatio-Temporal Action Localization
2:00pm •
A* Lasso for Learning a Sparse Bayesian Network Structure for Continuous Variables
2:00pm •
Symbolic Opportunistic Policy Iteration for Factored-Action MDPs
2:00pm •
Deep Neural Networks for Object Detection
2:00pm •
Geometric optimisation on positive definite matrices for elliptically contoured distributions
2:00pm •
Forgetful Bayes and myopic planning: Human learning and decision-making in a bandit setting
2:00pm •
Probabilistic Movement Primitives
2:00pm •
Policy Shaping: Integrating Human Feedback with Reinforcement Learning
2:00pm •
Minimax Optimal Algorithms for Unconstrained Linear Optimization
2:00pm •
A multi-agent control framework for co-adaptation in brain-computer interfaces
2:00pm •
Learning from Limited Demonstrations
2:00pm •
Fast Template Evaluation with Vector Quantization
2:00pm •
Context-sensitive active sensing in humans
2:00pm •
(More) Efficient Reinforcement Learning via Posterior Sampling
2:00pm •
Bellman Error Based Feature Generation using Random Projections on Sparse Spaces
2:00pm •
Learning invariant representations and applications to face verification
2:00pm •
Optimization, Learning, and Games with Predictable Sequences
2:00pm •
Analyzing the Harmonic Structure in Graph-Based Learning
2:00pm •
BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables
2:00pm •
Optimal integration of visual speed across different spatiotemporal frequency channels
Harvey's Convention Center Floor, CC
7:30am •
Registration Desk
9:00am •
Neural Reinforcement Learning
9:50am •
Actor-Critic Algorithms for Risk-Sensitive MDPs
10:10am •
Learning from Limited Demonstrations
10:14am •
Distributed Exploration in Multi-Armed Bandits
10:18am •
Dimension-Free Exponentiated Gradient
10:22am •
Generalizing Analytic Shrinkage for Arbitrary Covariance Structures
10:26am •
Robust Spatial Filtering with Beta Divergence
10:50am •
New Methods for the Analysis of Genome Variation Data
11:40am •
BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables
12:00pm •
Speeding up Permutation Testing in Neuroimaging
12:04pm •
Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching
12:08pm •
Deep Fisher Networks for Large-Scale Image Classification
12:12pm •
Sinkhorn Distances: Lightspeed Computation of Optimal Transport
12:16pm •
Understanding variable importances in forests of randomized trees
Harvey's: Garden Buffet, Promenade 1+2
10:30am •
Coffee Break
Monday
, December 9
Harrah's Fallen+Marla
7:00am •
Extreme Classification: Multi-Class & Multi-Label Learning with Millions of Categories
Harrah's Glenbrook+Emerald
7:30am •
NIPS 2013 Workshop on Causality: Large-scale Experiment Design and Inference of Causal Mechanisms
Harrah's Sand Harbor I
7:30am •
Discrete Optimization in Machine Learning: Connecting Theory and Practice
Harrah's Sand Harbor II
7:30am •
Deep Learning
Harrah's Sand Harbor III
7:30am •
Output Representation Learning
Harrah's Tahoe A+B
7:30am •
Crowdsourcing: Theory, Algorithms and Applications
Harrah's Tahoe C
7:30am •
OPT2013: Optimization for Machine Learning
Harrah's Tahoe D
7:30am •
Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games.
Harvey's Convention Center Floor, CC
6:30am •
Registration Desk
Harvey's Emerald Bay 1
7:30am •
Advances in Machine Learning for Sensorimotor Control
Harvey's Emerald Bay 2
7:30am •
Perturbations, Optimization, and Statistics
Harvey's Emerald Bay 3
7:30am •
Frontiers of Network Analysis: Methods, Models, and Applications
Harvey's Emerald Bay 4
7:30am •
What Difference Does Personalization Make?
Harvey's Emerald Bay 5
7:30am •
Randomized Methods for Machine Learning
Harvey's Emerald Bay 6
7:30am •
High-dimensional Statistical Inference in the Brain
Harvey's Emerald Bay A
7:30am •
Probabilistic Models for Big Data
Harvey's Emerald Bay B
7:30am •
Big Learning : Advances in Algorithms and Data Management
Harvey's Sierra
7:30am •
MLINI-13: Machine Learning and Interpretation in Neuroimaging (Day 1)
Harvey's Tallac
7:30am •
Large Scale Matrix Analysis and Inference
Harvey's Zephyr
7:30am •
Modern Nonparametric Methods in Machine Learning
Harvey's: Garden Buffet, Promenade 1+2, Harrahs SEC Foyer
6:30am •
Breakfast
Tuesday
, December 10
Harrah's Fallen+Marla
7:30am •
New Directions in Transfer and Multi-Task: Learning Across Domains and Tasks
Harrah's Glenbrook+Emerald
7:30am •
Machine Learning for Sustainability
Harrah's Sand Harbor
7:00pm •
Farewell Reception
Harrah's Tahoe A
7:30am •
Learning Faster From Easy Data
Harrah's Tahoe B
7:30am •
Workshop on Spectral Learning
Harrah's Tahoe C
7:30am •
Neural Information Processing Scaled for Bioacoustics : NIPS4B
Harrah's Tahoe D
7:30am •
Data Driven Education
Harvey's Convention Center Floor, CC
6:30am •
Registration Desk
Harvey's Emerald Bay 1
7:30am •
Constructive Machine Learning
Harvey's Emerald Bay 2
7:30am •
Knowledge Extraction from Text (KET)
Harvey's Emerald Bay 3
7:30am •
Resource-Efficient Machine Learning
Harvey's Emerald Bay 4
7:30am •
Acquiring and Analyzing the Activity of Large Neural Ensembles
Harvey's Emerald Bay 5
7:30am •
Machine Learning Open Source Software: Towards Open Workflows
Harvey's Emerald Bay 6
7:30am •
Greedy Algorithms, Frank-Wolfe and Friends - A modern perspective
Harvey's Emerald Bay A
7:30am •
Bayesian Optimization in Theory and Practice
Harvey's Emerald Bay B
7:30am •
Topic Models: Computation, Application, and Evaluation
Harvey's Sierra
7:30am •
MLINI-13: Machine Learning and Interpretation in Neuroimaging (Day 2)
Harvey's Tallac
7:30am •
Machine Learning for Clinical Data Analysis and Healthcare
Harvey's Zephyr
7:30am •
Machine Learning in Computational Biology
Harvey's: Garden Buffet, Promenade 1+2, Harrahs SEC Foyer
6:30am •
Breakfast
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