Optimizing expensive to evaluate black-box functions over an input space consisting of all permutations of d objects is an important problem with many real-world applications. For example, placement of functional blocks in hardware design to optimize …

We consider the problem of optimizing combinatorial spaces (e.g., sequences, trees, and graphs) using expensive black-box function evaluations. For example, optimizing molecules for drug design using physical lab experiments. Bayesian optimization …

We consider the problem of optimizing expensive black-box functions over discrete spaces (e.g., sets, sequences, graphs). The key challenge is to select a sequence of combinatorial structures to evaluate, in order to identify high-performing …

We consider the problem of multi-objective (MO) blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto set of solutions while minimizing the number of function evaluations. For example, in …

Efficiency of power management system (PMS) is one of the key performance metrics for highly integrated system on chips (SoCs). Towards the goal of improving power efficiency of SoCs, we make two key technical contributions in this paper. First, we …

We study the novel problem of blackbox optimization of multiple objectives via multi-fidelity function evaluations that vary in the amount of resources consumed and their accuracy. The overall goal is to approximate the true Pareto set of solutions …

We consider the problem of multi-objective (MO) blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto-set of solutions by minimizing the number of function evaluations. For example, in hardware …

In a structured prediction problem, one needs to learn a predictor that, given a structured input, produces a structured object, such as a sequence, tree, or clustering output. Prototypical structured prediction tasks include part-of-speech tagging …

In a structured prediction problem, we need to learn a predictor that can produce a structured output given a structured input (eg, part-of-speech tagging). The key learning and inference challenge is due to the exponential size of the structured …

To avoid rewriting software code for new computer architectures and to take advantage of the extreme heterogeneous processing, communication and storage technologies, there is an urgent need for determining the right amount and type of specialization …

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