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Large dataflow designs are a result of behavioral specification of modern complex digital systems and/or a result of unfolding and transforming looped and branched programs. Since deep-submicron silicon technology provides large amounts of available resour ...
Optimization is a fundamental tool in modern science. Numerous important tasks in biology, economy, physics and computer science can be cast as optimization problems. Consider the example of machine learning: recent advances have shown that even the most s ...
Expectation propagation (EP) is a widely successful algorithm for variational inference. EP is an iterative algorithm used to approximate complicated distributions, typically to find a Gaussian approximation of posterior distributions. In many applications ...
We consider a repeated sequential game between a learner, who plays first, and an opponent who responds to the chosen action. We seek to design strategies for the learner to successfully interact with the opponent. While most previous approaches consider k ...
The emergence of Big Data has enabled new research perspectives in the discrete choice community. While the techniques to estimate Machine Learning models on a massive amount of data are well established, these have not yet been fully explored for the esti ...
This dissertation discusses algorithmic verification techniques for concurrent component-based systems modeled in the Behavior-Interaction-Priority (BIP) framework with both bounded and unbounded concurrency. BIP is a component framework for mixed software ...
Algebraic data types and pattern matching are key features of functional programming languages. Exhaustivity checking of pattern matching is a safety belt that defends against unmatched exceptions at runtime and boosts type safety. However, the presence of ...
Algebraic data types and pattern matching are key features of functional programming languages. Exhaustivity checking of pattern matching is a safety belt that defends against unmatched exceptions at runtime and boosts type safety. However, the presence of ...
Distributed skyline computation is important for a wide range of domains, from distributed and web-based systems to ISP-network monitoring and distributed databases. The problem is particularly challenging in dynamic distributed settings, where the goal is ...
Rapid advances in data collection and processing capabilities have allowed for the use of increasingly complex models that give rise to nonconvex optimization problems. These formulations, however, can be arbitrarily difficult to solve in general, in the s ...