Advances in Neural Information Processing Systems 19

Name: Advances in Neural Information Processing Systems 19 | Proceedings of the 2006 Conference
Brand: MIT Press
Price: 197.09 EUR
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Proceedings of the 2006 Conference

Bernhard Scholkopf John Platt Thomas Hofmann(Herausgeber*in)

MIT Press

1. Auflage

Erschienen am 11. September 2017

1672 Seiten

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Inhalt

Intro
Contents
Preface
Donors
NIPS Foundation
Committees
Reviewers
An Application of Reinforcement Learning to Aerobatic Helicopter Flight
Tighter PAC-Bayes Bounds
Online Classification for Complex Problems Using Simultaneous Projections
Learning on Graph with Laplacian Regularization
Multi-Task Feature Learning
Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning
Efficient Methods for Privacy Preserving Face Detection
Active learning for misspecified generalized linear models
Subordinate class recognition using relational object models
Unified Inference for Variational Bayesian Linear Gaussian State-Space Models
A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems
Sample complexity of policy search with known dynamics
AdaBoost is Consistent
A selective attention multi-chip system with dynamic synapses and spiking neurons
Temporal and Cross-Subject Probabilistic Models for fMRI Prediction Tasks
Convergence of Laplacian Eigenmaps
Analysis of Representations for Domain Adaptation
An Approach to Bounded Rationality
Greedy Layer-Wise Training of Deep Networks
Dirichlet-Enhanced Spam Filtering based on Biased Samples
Detecting Humans via Their Pose
Similarity by Composition
Denoising and Dimension Reduction in Feature Space
Learning to Rank with Nonsmooth Cost Functions
Conditional mean field
Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation
Branch and Bound for Semi-Supervised Support Vector Machines
Automated Hierarchy Discovery for Planning in Partially Observable Environments
Max-margin classification of incomplete data
Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model
Implicit Online Learning with Kernels
Context dependent amplification of both rate and event-correlation in a VLSI network of spiking neurons
Bayesian Ensemble Learning
Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions
Map-Reduce for Machine Learning on Multicore
Relational Learning with Gaussian Processes
Recursive Attribute Factoring
On Transductive Regression
Balanced Graph Matching
Learning from Multiple Sources
Kernels on Structured Objects Through Nested Histograms
Differential Entropic Clustering of Multivariate Gaussians
Support Vector Machines on a Budget
A Theory of Retinal Population Coding
Learning to Traverse Image Manifolds
Using Combinatorial Optimization within Max-Product Belief Propagation
Optimal Single-Class Classification Strategies
A Small World Threshold for Economic Network Formation
PG-means: learning the number of clusters in data
Clustering Under Prior Knowledge with Application to Image Segmentation
Multi-dynamic Bayesian Networks
Image Retrieval and Classification Using Local Distance Functions
Multiple Instance Learning for Computer Aided Diagnosis
Distributed Inference in Dynamical Systems
iLSTD: Eligibility Traces and Convergence Analysis
A PAC-Bayes Risk Bound for General Loss Functions
Bayesian Policy Gradient Algorithms
Data Integration for Classification Problems Employing Gaussian Process Priors
Approximate inference using planar graph decomposition
Near-Uniform Sampling of Combinatorial Spaces Using XOR Constraints
No-regret Algorithms for Online Convex Programs
Large Margin Multi-channel Analog-to-Digital Conversion with Applications to Neural Prosthesis
Approximate Correspondences in High Dimensions
A Kernel Method for the Two-Sample-Problem
Learning Nonparametric Models for Probabilistic Imitation
Training Conditional Random Fields for Maximum Labelwise Accuracy
Adaptive Spatial Filters with predefined Region of Interest for EEG based Brain-Computer-Interfaces
Graph-Based Visual Saliency
Stratification Learning: Detecting Mixed Density and Dimensionality in High Dimensional Point Clouds
Manifold Denoising
TrueSkill: A Bayesian Skill Rating System
Prediction on a Graph with a Perceptron
Geometric entropy minimization (GEM) for anomaly detection and localization
Single Channel Speech Separation Using Factorial Dynamics
Correcting Sample Selection Bias by Unlabeled Data
Sparse Representation for Signal Classification
In-N etwork PCA and Anomaly Detection
Learning Time-Intensity Proxles of Human Activity using Non-Parametric Bayesian Models
Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm
Adaptor Grammars:A Framework for Specifying Compositional Nonparametric Bayesian Models
A Humanlike Predictor of Facial Attractiveness
Clustering appearance and shape by learning jigsaws
A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems
An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models
Combining casual and similarity-based reasoning
A Nonparametric Approach to Bottom-Up Visual Saliency
Hierarchical Dirichlet Processes with Random Effects
An Information Theoretic Framework for Eukaryotic Gradient Sensing
Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons
Predicting spike times from subthreshold dynamics of a neuron
Gaussian and Wishart Hyperkernels
Causal inference in sensorimotor integration
Multiple timescales and uncertainty in motor adaptation
Reducing Calibration Time For Brain-Computer Interfaces: A Clustering Approach
Accelerated Variational Dirichlet Process Mixtures
PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier
Inducing Metric Violations in Human Similarity Judgements
Modelling transcriptional regulation using Gaussian processes
Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields
Efficient sparse coding algorithms
A Bayesian Approach to Diffusion Models of Decision-Making and Response Time
Efficient Structure Learning of Markov Networks using L1-Regularization
Aggregating Classification Accuracy across Time: Application to Single Trial EEG
Uncertainty,phase and oscillatory hippocampal recall
Blind Motion Deblurring Using Image Statistics
Speakers optimize information density through syntactic reduction
Real-time adaptive information-theoretic optimization of neurophysiology experiments
Ordinal Regression by Extended Binary Classification
Conditional Random Sampling: A Sketch-based Sampling Technique for Sparse Data
Generalized Regularized Least-Squares Learning with Predefined Features in a Hilbert Space
Learnability and the Doubling Dimension
Emergence of conjunctive visual features by quadratic independent component analysis
Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure
Analysis of Contour Motions
Attribute-efficient learning of decision lists and linear threshold functions under unconcentrated distributions
Dynamic Foreground/Background Extraction from Images and Videos using Random Patches
Effects of Stress and Genotype on Meta-parameter Dynamics in Reinforcement Learning
Statistical Modeling of Images with Fields of Gaussian Scale Mixtures
An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments
Isotonic Conditional Random Fields and Local Sentiment Flow
Part-based Probabilistic Point Matching using Equivalence Constraints
Modeling Dyadic Data with Binary Latent Factors
Fast Discriminative Visual Codebooks using Randomized Clustering Forests
Context Effects in Category Learning: An Investigation of Four Probabilistic Models
Multi-Robot Negotiation: Approximating the Set of Subgame Perfect Equilibria in General-Sum Stochastic Games
N on-rigid point set registration: Coherent Point Drift
Fundamental Limitations of Spectral Clustering
On the Relation Between Low Density Separation, Spectral Clustering and Graph Cuts
A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments
Temporal dynamics of information content carried by neurons in the primary visual cortex
Blind source separation for over-determined delayed mixtures
The Neurodynamics of Belief Propagation on Binary Markov Random Fields
Handling Advertisements of Unknown Quality in Search Advertising
Bayesian Model Scoring in Markov Random Fields
Game theoretic algorithms for Protein-DNA binding
Bayesian Image Super-resolution, Continued
Parameter Expanded Variational Bayesian Methods
Inferring Network Structure from Co-Occurrences
Unsupervised Regression with Applications to Nonlinear System Identification
Stability of K-Means Clustering
Learning to parse images of articulated bodies
Efficient Learning of Sparse Representations with an Energy-Based Model
Learning to be Bayesian without Supervision
Boosting Structured Prediction for Imitation Learning
Large Scale Hidden Semi-Markov SVMs
Natural Actor-Critic for Road Traffic Optimisation
Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees
Learning annotated hierarchies from relational data
Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds
Neurophysiological Evidence of Cooperative Mechanisms for Stereo Computation
Robotic Grasping of Novel Objects
Theory and Dynamics of Perceptual Bistability
Fast Iterative Kernel PCA
Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods
Information Bottleneck for Non Co-Occurrence Data
Large Margin Hidden Markov Models for Automatic Speech Recognition
Nonlinear physically-based models for decoding motor-cortical population activity
Convex Repeated Games and Fenchel Duality
Recursive ICA
Chained Boosting
A recipe for optimizing a time-histogram Hideaki Shimazaki
Mutagenetic tree Fisher kernel improves prediction of HIV drug resistance from viral genotype
Hidden Markov Dirichlet Process: Modeling Genetic Recombination in Open Ancestral Space
Learning Dense 3D Correspondence
An Oracle Inequality for Clipped Regularized Risk Minimizers
Learning Structural Equation Models for fMRI
Mixture Regression for Covariate Shift
Modeling Human Motion Using Binary Latent Variables
A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation
Towards a general independent subspace analysis
Linearly-solvable Markov decision problems
Logistic Regression for Single Trial EEG Classification
Large Margin Component Analysis
Learning Motion Style Synthesis from Perceptual Observations
Large-Scale Sparsified Manifold Regularization
Scalable Discriminative Learning for Natural Language Parsing and Translation
Generalized Maximum Margin Clustering and Unsupervised Kernel Learning
A Complexity-Distortion Approach to Joint Pattern Alignment
Online Clustering of Moving Hyperplanes
Comparative Gene Prediction using Conditional Random Fields
Fast Computation of Graph Kernels
Temporal Coding using the Response Properties of Spiking Neurons
High-Dimensional Graphical Model Selection Using l1 -Regularized Logistic Regression
Attentional Processing on a Spike-Based VLSI Neural Network
Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension
Graph Laplacian Regularization for Large-Scale Semidefinite Programming
A Switched Gaussian Process for Estimating Disparity and Segmentation in Binocular Stereo
Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization
Particle Filtering for Nonparametric Bayesian Matrix Factorization
A Scalable Machine Learning Approach to Go
A Local Learning Approach for Clustering
The Robustness-Performance Tradeoff in Markov Decision Processes
Optimal Change-Detection and Spiking Neurons
Stochastic Relational Models for Discriminative Link Prediction
Nonnegative Sparse PCA
Doubly Stochastic Normalization for Spectral Clustering
Simplifying Mixture Models through Function Approximation
Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms
MLLE: Modified Locally Linear Embedding Using Multiple Weights
Learning with Hypergraphs:Clustering, Classification, and Embedding
Multi-Instance Multi-Label Learning with Application to Scene Classification
Unsupervised Learning of a Probabilistic Grammar for Object Detection and Parsing
A Probabilistic Algorithm Integrating Source Localization and Noise Suppression of MEG and EEG Data
Subject Index
Author Index

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