Advances in data storage, data collection and inference techniques have enabled the creation of huge databases of personal information. Dissemination of information from such databases even if formally anonymised, creates a serious threat to individual privacy through statistical disclosure. One of the key methods developed to limit statistical disclosure risk is k-anonymity. Several methods ha...

Variational inference algorithms provide the most effective framework for largescale training of Bayesian nonparametric models. Stochastic online approaches are promising, but are sensitive to the chosen learning rate and often converge to poor local optima. We present a new algorithm, memoized online variational inference, which scales to very large (yet finite) datasets while avoiding the com...

Sum-product networks (SPNs) are a new class of deep probabilistic models. SPNs can have unbounded treewidth but inference in them is always tractable. An SPN is either a univariate distribution, a product of SPNs over disjoint variables, or a weighted sum of SPNs over the same variables. We propose the first algorithm for learning the structure of SPNs that takes full advantage of their express...

We present a new hybrid algorithm for local search in distributed combinatorial optimization. This method is a mix between classical local search methods in which nodes take decisions based only on local information, and full inference methods that guarantee completeness. In general, classical inference methods are time and space exponential in a parameter of the constraint graph called the ind...

Systematic, high-throughput dissection of causal post-translational regulatory dependencies, on a genome wide basis, is still one of the great challenges of biology. Due to its complexity, however, only a handful of computational algorithms have been developed for this task. Here we present CINDy (Conditional Inference of Network Dynamics), a novel algorithm for the genome-wide, context specifi...

Bayesian single index model is a highly promising dimension reduction tool for an interpretable modeling of the non linear relationship between the response and its predictors. However, existing Bayesian tools in this area suffer from slow mixing of the Markov Chain Monte Carlo (MCMC) computational tool and also lack the ability to deal with missing covariates. To circumvent these practical pro...

We have designed and implemented a type inference algorithm for the full Self language. Thc algorithm can guarantee the safety and disambiguity of message sends, and provide useful information for browsers and optimizing compilers. Self features objects with dynamic inheritance. This construct has until now been considered incompatible with type inference because it allows the inheritance graph...

The interpretation of implications as rules motivates a different left-introduction schema for implication in the sequent calculus, which is conceptually more basic than the implication-left schema proposed by Gentzen. Corresponding to results obtained for systems with higher-level rules, it enjoys the subformula property and cut elimination in a weak form. The introduction schema for implicati...

Dynamic generalized linear mixed models are proposed as a regression tool for nonnormal longitudinal data This framework is an interesting combination of dynamic models by other name state space models and mixed models also known as random e ect models The main feature is that both time and unit speci c parameters are allowed which is especially attractive if a considerable number of units is o...