1. 6. Experimental Analysis Visible Boltzmann machine with higher-order potentials: Conditional random field (CRF): Exponential random graph model (ERGM): We ran BCD on Lazega social network data. 5. Tree Structured Blocks BCD (and CL) scales exponentially with block size. Large block sizes (>15) are too computationally expensive in practice. We can use tree structured blocks. Forward-backward sampling can be performed (to obtain a blocked sample), with time complexity linear in block size. Learning with Blocks: Composite Likelihood and Contrastive Divergence Arthur Asuncion 1, Qiang Liu 1, Alexander Ihler, Padhraic Smyth Department of Computer Science, University of California, Irvine 1 Both authors contributed equally. 1. Motivation: Efficient Parameter Estimation Assume an exponential family:. Suppose we have independent observations:. Our task is to perform parameter estimation (for ). Maximum likelihood estimation (MLE) is the standard approach: Likelihood gradient: MLE has nice theoretical properties: Asymptotic consistency and normality, statistical efficiency. Difficulty: The partition function and its gradient are generally intractable for many models. Our approach: Composite likelihood + contrastive divergence. 3. Contrastive Divergence Contrastive divergence (CD) approximates the second term in the likelihood gradient using MCMC (for efficiency reasons): CD-1 corresponds to MPLE [Hyvärinen, 2006]. CD- ∞ (i.e. chain has reached equilibrium) corresponds to MLE. CD-n is an algorithmic variant between CD-1 and CD- ∞. We propose blocked contrastive divergence (BCD). 7. Conclusions Blocked contrastive divergence (which combines CL and CD) is computationally efficient and accurate, especially when there are strong dependencies between blocks of variables. Composite likelihoods allows one to trade off computation for accuracy. Tree structured blocks allow for enhanced efficiency. Come to ICML 2010 to see our paper on CD + particle filtering! 2. Pseudolikelihood and Composite Likelihood Pseudolikelihood (i.e. MPLE) approximates the (log)likelihood by using conditional probabilities: Properties:  Asymptotically consistent  Computationally fast  Not as statistically efficient as MLE  Underestimates dependency structure of the model Composite likelihood (i.e. MCLE) fills gap between MLE & MPLE: Properties:  Asymptotically consistent  Computational cost greater than MPLE and less than MLE (exponential in size of largest subset A c )  Statistical efficiency greater than MPLE and less than MLE  Generally provides more accurate solutions than MPLE 4. Blocked Contrastive Divergence The gradient of the composite likelihood is: where The second term of the gradient can be approximated using a random-scan blocked Gibbs sampler (RSBG): 1. Randomly select a data point i (from empirical data distribution). 2. Randomly select a block c (with probability 1/C). 3. Update by performing one blocked Gibbs step using. Blocked contrastive divergence (BCD) is a stochastic version of MCLE (see paper for derivation). The connection between CD and composite likelihoods allows for cross-fertilization between machine learning and statistics. Example of tree structured blocks on 2D lattice Each dot is a model with random parameters.The performance as a function of the coupling strength. edge 2-star triangle Expectation w.r.t. empirical data distribution Expectation w.r.t. model Partition function is easy to calculate We focus on conditional composite likelihoods MPLE MLE MCLE CD-1 “CD- ∞” BCD (our contribution) Spectrum of Algorithms: Expectation using samples obtained from n th step of Gibbs sampling, initialized at empirical data distribution Network statistics, e.g.: