NIPS2012おもしろそうな論文メモ

おもしろそうなタイトルの論文をメモ。あくまでメモ。

  • Adaptive Stratified Sampling for Monte-Carlo integration of Differentiable functions
    • A. Carpentier, R. Munos
  • Ancestor Sampling for Particle Gibbs
    • F. Lindsten, M. Jordan, T. Schön
  • A new metric on the manifold of kernel matrices with application to matrix geometric means
    • S. Sra
  • Assessing Blinding in Clinical Trials
    • O. Arandjelovic
  • Bayesian Nonparametric Modeling of Suicide Attempts
    • F. Ruiz, I. Valera, C. Blanco, F. Perez-Cruz
  • Causal discovery with scale-mixture model for spatiotemporal variance dependencies
    • Z. Chen, K. Zhang, L. CHAN
  • Deep Learning of invariant features via tracked video sequences
    • W. Zou, A. Ng, S. Zhu, K. Yu
  • Density-Difference Estimation
    • M. Sugiyama, T. Kanamori, T. Suzuki, M. Plessis, S. Liu, I. Takeuchi
  • Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data
    • M. Hughes, E. Fox, E. Sudderth
  • Efficient Monte Carlo Counterfactual Regret Minimization in Games with Many Player Actions
    • R. Gibson, M. Lanctot, N. Burch, D. Szafron
  • Emergence of Object-Selective Features in Unsupervised Feature Learning
    • A. Coates, A. Karpathy, A. Ng
  • Ensemble weighted kernel estimators for multivariate entropy estimation
    • K. Sricharan, A. Hero
  • Entangled Monte Carlo
    • S. Jun, L. Wang, A. Bouchard-Côté
  • Expectation Propagation in Gaussian Process Dynamical Systems
    • M. Deisenroth, S. Mohamed
  • Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression
    • M. Khan, S. Mohamed, K. Murphy
  • High-Order Multi-Task Feature Learning to Identify Longitudinal Phenotypic Markers for Alzheimer Disease Progression Prediction
    • H. Wang, F. Nie, H. Huang, J. Yan, S. Kim, S. Risacher, A. Saykin, L. Shen
  • High Dimensional Semiparametric Scale-invariant Principal Component Analysis
    • F. Han, H. Liu
  • How Prior Probability Influences Decision Making: A Unifying Probabilistic Model
    • Y. Huang, A. Friesen, T. Hanks, M. Shadlen, R. Rao
  • Human memory search as a random walk in a semantic network
    • J. Abbott, J. Austerweil, T. Griffiths
  • Identifiability and Unmixing of Latent Parse Trees
    • P. Liang, S. Kakade, D. Hsu
  • Kernel Hyperalignment
    • A. Lorbert, P. Ramadge
  • Kernel Latent SVM for Visual Recognition
    • W. Yang, Y. Wang, A. Vahdat, G. Mori
  • Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning
    • M. Der, L. Saul
  • Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs
    • A. Anandkumar, R. Valluvan
  • Learning from Distributions via Support Measure Machines
    • K. Muandet, K. Fukumizu, F. Dinuzzo, B. Schölkopf
  • Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data
    • A. Glazer, M. Lindenbaoum, S. Markovitch
  • Learning Mixtures of Tree Graphical Models
    • A. Anandkumar, D. Hsu, F. Huang, S. Kakade
  • Learning Networks of Heterogeneous Influence
    • N. DU, L. Song, A. Smola, M. Yuan
  • Learning Probability Measures with respect to Optimal Transport Metrics
    • G. Canas, L. Rosasco
  • Learning to Discover Social Circles in Ego Networks
    • J. McAuley, J. Leskovec
  • Link Prediction in Graphs with Autoregressive Features
    • E. Richard, S. Gaiffas, N. Vayatis
  • Max-Margin Structured Output Regression for Spatio-Temporal Action Localization
    • D. Tran, J. Yuan
  • MCMC for continuous-time discrete-state systems
    • V. Rao, Y. Teh
  • Meta-Gaussian Information Bottleneck
    • M. Rey, V. Roth
  • Monte Carlo Methods for Maximum Margin Supervised Topic Models
    • Q. Jiang, J. Zhu, M. Sun, E. Xing
  • Multiple Operator-valued Kernel Learning
    • H. Kadri, A. Rakotomamonjy, F. Bach, p. preux
  • Multiplicative Forests for Continuous-Time Processes
    • J. Weiss, S. Natarajan, D. Page
  • Multiresolution Gaussian Processes
    • E. Fox, D. Dunson
  • Non-parametric Approximate Dynamic Programming via the Kernel Method
    • N. Bhat, C. Moallemi, V. Farias
  • No voodoo here! Learning discrete graphical models via inverse covariance estimation
    • P. Loh, M. Wainwright
  • Nystr{ö}m Method vs Random Fourier Features: A Theoretical and Empirical Comparison
    • T. Yang, Y. Li, M. Mahdavi, R. Jin, Z. Zhou
  • On Lifting the Gibbs Sampling Algorithm
    • D. Venugopal, V. Gogate
  • Online L1-Dictionary Learning with Application to Novel Document Detection
    • S. Kasiviswanathan, H. Wang, A. Banerjee, P. Melville
  • On the connections between saliency and tracking
    • V. Mahadevan, N. Vasconcelos
  • Optimal kernel choice for large-scale two-sample tests
    • A. Gretton, B. Sriperumbudur, D. Sejdinovic, H. Strathmann, S. Balakrishnan, M. Pontil, K. Fukumizu
  • Patient Risk Stratification for Hospital-Associated C. Diff as a Time-Series Classification Task
    • J. Wiens, J. Guttag, E. Horvitz
  • Phoneme Classification using Constrained Variational Gaussian Process Dynamical System
    • H. Park, J. Kim, S. Park, S. Yun, C. Yoo
  • Pointwise Tracking the Optimal Regression Function
    • Y. Wiener, R. El-Yaniv
  • Priors for Diversity in Generative Latent Variable Models
  • Probabilistic Event Cascades for Alzheimer's disease
    • J. Huang, D. Alexander
  • Putting Bayes to sleep
    • W. Koolen, D. Adamskiy, M. Warmuth
  • Recognizing Activities by Attribute Dynamics
    • W. Li, N. Vasconcelos
  • Reducing statistical time-series problems to binary classification
    • D. Ryabko, J. Mary
  • Scalable Inference of Overlapping Communities
    • P. Gopalan, D. Mimno, S. Gerrish, M. Freedman, D. Blei
  • Semantic Kernel Forests from Multiple Taxonomies
    • S. Hwang, K. Grauman, F. Sha
  • Sparse Approximate Manifolds for Differential Geometric MCMC
    • B. Calderhead, M. Sustik
  • Spectral learning of linear dynamics from generalised-linear observations with application to neural population data
    • L. Buesing, J. Macke, M. Sahani
  • Symmetric Correspondence Topic Models for Multilingual Text Analysis
    • K. Fukumasu, K. Eguchi, E. Xing
  • The representer theorem for Hilbert spaces: a necessary and sufficient condition
    • F. Dinuzzo, B. Schölkopf
  • The variational hierarchical EM algorithm for clustering hidden Markov models.
    • E. Coviello, A. Chan, G. Lanckriet
  • Training sparse natural image models with a fast Gibbs sampler of an extended state space
    • L. Theis, J. Sohl-Dickstein, M. Bethge