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Nando Freitas

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7 papers
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7

NeurIPS Conference 2008 Conference Paper

An interior-point stochastic approximation method and an L1-regularized delta rule

  • Peter Carbonetto
  • Mark Schmidt
  • Nando Freitas

The stochastic approximation method is behind the solution to many important, actively-studied problems in machine learning. Despite its far-reaching application, there is almost no work on applying stochastic approximation to learning problems with constraints. The reason for this, we hypothesize, is that no robust, widely-applicable stochastic approximation method exists for handling such problems. We propose that interior-point methods are a natural solution. We establish the stability of a stochastic interior-point approximation method both analytically and empirically, and demonstrate its utility by deriving an on-line learning algorithm that also performs feature selection via L1 regularization.

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NeurIPS Conference 2007 Conference Paper

Active Preference Learning with Discrete Choice Data

  • Brochu Eric
  • Nando Freitas
  • Abhijeet Ghosh

We propose an active learning algorithm that learns a continuous valuation model from discrete preferences. The algorithm automatically decides what items are best presented to an individual in order to find the item that they value highly in as few trials as possible, and exploits quirks of human psychology to minimize time and cognitive burden. To do this, our algorithm maximizes the expected improvement at each query without accurately modelling the entire valuation surface, which would be needlessly expensive. The problem is particularly difficult because the space of choices is infinite. We demonstrate the effectiveness of the new algorithm compared to related active learning methods. We also embed the algorithm within a decision making tool for assisting digital artists in rendering materials. The tool finds the best parameters while minimizing the number of queries.

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NeurIPS Conference 2007 Conference Paper

Bayesian Policy Learning with Trans-Dimensional MCMC

  • Matthew Hoffman
  • Arnaud Doucet
  • Nando Freitas
  • Ajay Jasra

A recently proposed formulation of the stochastic planning and control problem as one of parameter estimation for suitable artificial statistical models has led to the adoption of inference algorithms for this notoriously hard problem. At the algorithmic level, the focus has been on developing Expectation-Maximization (EM) algorithms. In this paper, we begin by making the crucial observation that the stochastic control problem can be reinterpreted as one of trans-dimensional inference. With this new interpretation, we are able to propose a novel reversible jump Markov chain Monte Carlo (MCMC) algorithm that is more efficient than its EM counterparts. Moreover, it enables us to implement full Bayesian policy search, without the need for gradients and with one single Markov chain. The new approach involves sampling directly from a distribution that is proportional to the reward and, consequently, performs better than classic simulations methods in situations where the reward is a rare event.

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NeurIPS Conference 2006 Conference Paper

Conditional mean field

  • Peter Carbonetto
  • Nando Freitas

Despite all the attention paid to variational methods based on sum-product message passing (loopy belief propagation, tree-reweighted sum-product), these methods are still bound to inference on a small set of probabilistic models. Mean field approximations have been applied to a broader set of problems, but the solutions are often poor. We propose a new class of conditionally-specified variational approximations based on mean field theory. While not usable on their own, combined with sequential Monte Carlo they produce guaranteed improvements over conventional mean field. Moreover, experiments on a well-studied problem-- inferring the stable configurations of the Ising spin glass--show that the solutions can be significantly better than those obtained using sum-product-based methods.

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NeurIPS Conference 2005 Conference Paper

Fast Krylov Methods for N-Body Learning

  • Nando Freitas
  • Yang Wang
  • Maryam Mahdaviani
  • Dustin Lang

This paper addresses the issue of numerical computation in machine learning domains based on similarity metrics, such as kernel methods, spectral techniques and Gaussian processes. It presents a general solution strategy based on Krylov subspace iteration and fast N-body learning methods. The experiments show significant gains in computation and storage on datasets arising in image segmentation, object detection and dimensionality reduction. The paper also presents theoretical bounds on the stability of these methods.

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NeurIPS Conference 2004 Conference Paper

Beat Tracking the Graphical Model Way

  • Dustin Lang
  • Nando Freitas

We present a graphical model for beat tracking in recorded music. Using a probabilistic graphical model allows us to incorporate local information and global smoothness constraints in a principled manner. We evaluate our model on a set of varied and difficult examples, and achieve impres- sive results. By using a fast dual-tree algorithm for graphical model in- ference, our system runs in less time than the duration of the music being processed.

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