Канал: AISTATS 2014

Keynote 1: High dimensional Causal Inference -- Peter Bühlman

Keynote 1: High dimensional Causal Inference -- Peter Bühlman

Highlight Talk: Spatiotemporal point process models of conflicts -- Guido Sanguinetti

Highlight Talk: Spatiotemporal point process models of conflicts -- Guido Sanguinetti

LAMORE: A Stable Scalable Approach to Latent Vector Autoregressive Modeling... -- Yubin Park

LAMORE: A Stable Scalable Approach to Latent Vector Autoregressive Modeling... -- Yubin Park

An Analysis of Active Learning with Uniform Feature Noise -- Aaditya Ramdas

An Analysis of Active Learning with Uniform Feature Noise -- Aaditya Ramdas

Towards building a Crowd Sourced Sky Map -- Dustin Lang

Towards building a Crowd Sourced Sky Map -- Dustin Lang

Tutorial 2: Approximate Bayesian Computation (ABC) -- Christian P. Robert

Tutorial 2: Approximate Bayesian Computation (ABC) -- Christian P. Robert

An inclusion optimal algorithm for chain graph structure learning -- Jose Peña

An inclusion optimal algorithm for chain graph structure learning -- Jose Peña

Black Box Variational Interference -- Rajesh Ranganath

Black Box Variational Interference -- Rajesh Ranganath

Keynote 2: Weakly Informative Priors -- Andrew Gelman

Keynote 2: Weakly Informative Priors -- Andrew Gelman

Keynote 3: On the Computational and Statistical Interface and Big Data -- Michael Jordan

Keynote 3: On the Computational and Statistical Interface and Big Data -- Michael Jordan

Highlight Talk: Gaussian Processes for Data Efficient Learning... -- Marc Diesenroth

Highlight Talk: Gaussian Processes for Data Efficient Learning... -- Marc Diesenroth

Notable Paper: Decontamination of Mutually Contaminated Models -- Clayton Scott

Notable Paper: Decontamination of Mutually Contaminated Models -- Clayton Scott

Mixed Graphical Models via Exponential Families -- Pradeep Ravikumar

Mixed Graphical Models via Exponential Families -- Pradeep Ravikumar

Highlight Talk: Representation Learning: A Review and New Perspectives -- Yoshua Bengio

Highlight Talk: Representation Learning: A Review and New Perspectives -- Yoshua Bengio

Efficiently Enforcing Diversity in Multi-Output Structured Prediction -- Abner Guzman-Rivera

Efficiently Enforcing Diversity in Multi-Output Structured Prediction -- Abner Guzman-Rivera

Efficient Algorithms and Error Analysis for the Modified Nystrom Method -- Shusen Wang

Efficient Algorithms and Error Analysis for the Modified Nystrom Method -- Shusen Wang

Notable Paper: Distributed Optimization of Deeply Nested Systems -- Miguel Carreira-Perpinan

Notable Paper: Distributed Optimization of Deeply Nested Systems -- Miguel Carreira-Perpinan

Highlight Talk: Bayesian Monitoring for the Comprehensive Nuclear Test Ban Treaty -- Stuart Russell

Highlight Talk: Bayesian Monitoring for the Comprehensive Nuclear Test Ban Treaty -- Stuart Russell

Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data -- Abhimanu Kumar

Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data -- Abhimanu Kumar

Bayesian Nonparametric Possion Factorization for Recommendation System -- Prem Gopalan

Bayesian Nonparametric Possion Factorization for Recommendation System -- Prem Gopalan

A New Approach to Probabilistic Programming Inference -- Frank Wood

A New Approach to Probabilistic Programming Inference -- Frank Wood

Tutorial 1: Machine Learning and Human Computer Interaction -- Roderick Murray-Smith

Tutorial 1: Machine Learning and Human Computer Interaction -- Roderick Murray-Smith

A Stepwise uncertainty reduction approach to constrained global optimization -- Victor Picheny

A Stepwise uncertainty reduction approach to constrained global optimization -- Victor Picheny

Explicit Link Between Periodic Covariance Functions and State Space Models -- Arno Solin

Explicit Link Between Periodic Covariance Functions and State Space Models -- Arno Solin

Scalable Collaborative Bayesian Preference Learning -- Mohammad Emtiyaz Khan

Scalable Collaborative Bayesian Preference Learning -- Mohammad Emtiyaz Khan

Learning Optimal Bonded Treewidth Bayesian Networks via Maximum Satisfiability -- Matti Järvisalo

Learning Optimal Bonded Treewidth Bayesian Networks via Maximum Satisfiability -- Matti Järvisalo

Selective Sampling with Drift -- Koby Crammer

Selective Sampling with Drift -- Koby Crammer

Tutorial 3: Bayesian Computing with INLA -- Håvard Rue

Tutorial 3: Bayesian Computing with INLA -- Håvard Rue

On Correlation and Budget Constraints in Model-based Bandit Optimization -- Matthew Hoffman

On Correlation and Budget Constraints in Model-based Bandit Optimization -- Matthew Hoffman

Dynamic Resource Allocation for Optimizing Population Diffusion -- Alan Fern

Dynamic Resource Allocation for Optimizing Population Diffusion -- Alan Fern

Non Parametric Estimation of Exchangable Random Graph Model -- Qiuyi Han

Non Parametric Estimation of Exchangable Random Graph Model -- Qiuyi Han