Ruslan Salakhutdinov

Learning with the Weighted Trace-norm under Arbitrary Sampling Distributions
Rina Foygel, Ohad Shamir, Nathan Srebro, and Ruslan Salakhutdinov
ArXiv Report, 2011

Domain Adaptation: Overfitting and Small Sample Statistics
Dean Foster, Sham Kakade, and Ruslan Salakhutdinov
ArXiv Report [ pdf], 2011

One-shot learning of simple visual concepts
Brenden Lake , Ruslan Salakhutdinov, Jason Gross, and Josh Tenenbaum.
To appear in Proceedings of the 33rd Annual Conference of the Cognitive Science Society, 2011

Learning to Share Visual Appearance for Multiclass Object Detection
Ruslan Salakhutdinov, Antonio Torralba , and Josh Tenenbaum.
To appear in Computer Vision and Pattern Recognition (CVPR) 2011

Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm.
Ruslan Salakhutdinov and Nathan Srebro.
Neural Information Processing Systems 24, 2011
[bibtex] [ pdf]
Earlier version: [arXiv:1002.2780v1], [ps.gz][ pdf]

Practical Large-Scale Optimization for Max-Norm Regularization.
Jason Lee, Benjamin Recht, Ruslan Salakhutdinov, Nathan Srebro, and Joel A. Tropp
Neural Information Processing Systems 24, 2011
[bibtex] [ pdf]

Discovering Binary Codes for Documents by Learning Deep Generative Models.
Geoffrey Hinton and Ruslan Salakhutdinov.
To appear in Topics in Cognitive Science, 2010
[bibtex] [ pdf]

One-Shot Learning with a Hierarchical Nonparametric Bayesian Model.
Ruslan Salakhutdinov, Josh Tenenbaum, and Antonio Torralba.
MIT Technical Report MIT-CSAIL-TR-2010-052, 2010, [ pdf]

An Efficient Learning Procedure for Deep Boltzmann Machines.
Ruslan Salakhutdinov and Geoffrey Hinton.
MIT Technical Report MIT-CSAIL-TR-2010-037, 2010
[bibtex] [ pdf] [ Code]

Learning in Deep Boltzmann Machines using Adaptive MCMC.
Ruslan Salakhutdinov.
In 27th International Conference on Machine Learning (ICML-2010)
[bibtex] [ps.gz], [ pdf]

Efficient Learning of Deep Boltzmann Machines.
Ruslan Salakhutdinov and Hugo Larochelle.
AI and Statistics, 2010
[bibtex] [ps.gz][ pdf]

Learning in Markov Random Fields using Tempered Transitions.
Ruslan Salakhutdinov.
Neural Information Processing Systems 23, 2010
[bibtex] [ps.gz][ pdf]

Replicated Softmax: an Undirected Topic Model.
Ruslan Salakhutdinov and Geoffrey Hinton.
Neural Information Processing Systems 23, 2010
[bibtex] [ps.gz][pdf]

Modelling Relational Data using Bayesian Clustered Tensor Factorization.
Ilya Sutskever, Ruslan Salakhutdinov, and Josh Tenenbaum.
Neural Information Processing Systems 23, 2010
[bibtex] [pdf]

Learning Deep Generative Models.
Ruslan Salakhutdinov
PhD Thesis, Sep 2009
Dept. of Computer Science, University of Toronto
[bibtex] [ps.gz][pdf]

Semantic Hashing.
Ruslan Salakhutdinov and Geoffrey Hinton
International Journal of Approximate Reasoning, 2009
[bibtex] [pdf]
Earlier verision appeared in: SIGIR workshop on Information Retrieval and applications of Graphical Models (2007)
[bibtex] [ps.gz, pdf]

Learning Nonlinear Dynamic Models.
John Langford, Ruslan Salakhutdinov and Tong Zhang.
Proceedings of the 26th International Conference on Machine Learning (ICML), 2009.
[bibtex] [ps.gz][ pdf]

Evaluation Methods for Topic Models.
Hanna M. Wallach, Iain Murray, Ruslan Salakhutdinov and David Mimno.
Proceedings of the 26th International Conference on Machine Learning (ICML), 2009.
[bibtex] [ pdf]

Deep Boltzmann Machines
Ruslan Salakhutdinov and Geoffrey Hinton
12th International Conference on Artificial Intelligence and Statistics (2009).
[bibtex] [ps.gz][ pdf]

Evaluating probabilities under high-dimensional latent variable models.
Iain Murray and Ruslan Salakhutdinov
Neural Information Processing Systems 22 (NIPS 2009)
[bibtex] [ pdf], Jan 2009

Learning and Evaluating Boltzmann Machines
Ruslan Salakhutdinov
Technical Report UTML TR 2008-002, Dept. of Computer Science, University of Toronto
[bibtex] [ps.gz][ pdf]
This paper introduces a new Boltzmann machine learning algorithm that combines variational techniques and MCMC.

On the Quantitative Analysis of Deep Belief Networks.
Ruslan Salakhutdinov and Iain Murray
In 25th International Conference on Machine Learning (ICML-2008)
[bibtex] [ps.gz],[ pdf], [code]

Bayesian Probabilistic Matrix Factorization using MCMC.
Ruslan Salakhutdinov and Andriy Mnih
In 25th International Conference on Machine Learning (ICML-2008)
[bibtex] [ps.gz],[ pdf]

Probabilistic Matrix Factorization.
Ruslan Salakhutdinov and Andriy Mnih
Neural Information Processing Systems 21 (NIPS 2008)
[bibtex] [ps.gz][pdf], Jan 2008
(accepted for an oral presentation)

Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes.
Ruslan Salakhutdinov and Geoffrey Hinton
Neural Information Processing Systems 21 (NIPS 2008)
[bibtex] [ps.gz][pdf], Jan 2008

Restricted Boltzmann Machines for Collaborative Filtering.
Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey Hinton
ICML 2007
[bibtex] [ps.gz][pdf]

Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure.
Ruslan Salakhutdinov and Geoffrey Hinton
AI and Statistics 2007
[bibtex] [ps.gz][ pdf]

Reducing the Dimensionality of Data with Neural Networks.
Geoffrey E. Hinton and Ruslan R. Salakhutdinov
Science, 28 July 2006:
Vol. 313. no. 5786, pp. 504 - 507
[bibtex] [pdf][ Science Online]
Supporting Online Material [pdf, Science Online]
Matlab Code is available here
Figures are available in eps format: [fig1, fig2, fig3, fig4]
and in jpeg format: [fig1, fig2, fig3, fig4]

Simultaneous Localization and Surveying with Multiple Agents.
Sam Roweis & Ruslan Salakhutdinov (2005)
In R. Murray-Smith, R. Shorten (eds), Switching and Learning in Feedback Systems (Springer LNCS vol 3355, 2005). pp. 313--332
[bibtex] [pdf]

Neighbourhood Component Analysis
Jacob Goldberger, Sam Roweis, Geoff Hinton, Ruslan Salakhutdinov
Neural Information Processing Systems 17 (NIPS'04).
[bibtex] [pdf]

Semi-Supervised Mixture-of-Experts Classification
Grigoris Karakoulas & Ruslan Salakhutdinov
The Fourth IEEE International Conference on Data Mining (ICDM 04)
[bibtex]

On the Convergence of Bound Optimization Algorithms
Ruslan Salakhutdinov & Sam T. Roweis & Zoubin Ghahramani (2003).
Uncertainty in Artificial Intelligence (UAI-2003). pp 509-516
[bibtex] [ps.gz] [pdf]

Optimization with EM and Expectation-Conjugate-Gradient
Ruslan Salakhutdinov & Sam T. Roweis & Zoubin Ghahramani (2003).
International Conference on Machine Learning (ICML-2003). pp 672-679
[bibtex] [ps.gz] [pdf]

Adaptive Overrelaxed Bound Optimization Methods.
Ruslan Salakhutdinov & Sam T. Roweis (2003).
International Conference on Machine Learning (ICML-2003). pp 664-671
[bibtex] [ps.gz] [pdf]

Also check out demos on Adaptive vs Standard EM for Mixture of Factor Analyzers here and Mixture of Gaussians here

Technical Reports/Unpublished Manuscripts

Notes on the KL-divergence between a Markov chain and its equilibrium distribution
Iain Murray and Ruslan Salakhutdinov (2008)
[pdf]

Relationship between gradient and EM steps in latent variable models.
Ruslan Salakhutdinov & Sam T. Roweis & Zoubin Ghahramani (2002).
Unpublished Report. [draft version (sep.02)-->ps.gz(32K) pdf(70K)]

Expectation Conjugate-Gradient: An Alternative to EM
Ruslan Salakhutdinov & Sam T. Roweis & Zoubin Ghahramani (2003).
[draft version (june.02)-->ps.gz(186K) pdf(640K)]