109 research outputs found

    Collaborative Delivery with Energy-Constrained Mobile Robots

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    We consider the problem of collectively delivering some message from a specified source to a designated target location in a graph, using multiple mobile agents. Each agent has a limited energy which constrains the distance it can move. Hence multiple agents need to collaborate to move the message, each agent handing over the message to the next agent to carry it forward. Given the positions of the agents in the graph and their respective budgets, the problem of finding a feasible movement schedule for the agents can be challenging. We consider two variants of the problem: in non-returning delivery, the agents can stop anywhere; whereas in returning delivery, each agent needs to return to its starting location, a variant which has not been studied before. We first provide a polynomial-time algorithm for returning delivery on trees, which is in contrast to the known (weak) NP-hardness of the non-returning version. In addition, we give resource-augmented algorithms for returning delivery in general graphs. Finally, we give tight lower bounds on the required resource augmentation for both variants of the problem. In this sense, our results close the gap left by previous research.Comment: 19 pages. An extended abstract of this paper was published at the 23rd International Colloquium on Structural Information and Communication Complexity 2016, SIROCCO'1

    Doctor of Philosophy

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    dissertationMachine learning is the science of building predictive models from data that automatically improve based on past experience. To learn these models, traditional learning algorithms require labeled data. They also require that the entire dataset fits in the memory of a single machine. Labeled data are available or can be acquired for small and moderately sized datasets but curating large datasets can be prohibitively expensive. Similarly, massive datasets are usually too huge to fit into the memory of a single machine. An alternative is to distribute the dataset over multiple machines. Distributed learning, however, poses new challenges as most existing machine learning techniques are inherently sequential. Additionally, these distributed approaches have to be designed keeping in mind various resource limitations of real-world settings, prime among them being intermachine communication. With the advent of big datasets machine learning algorithms are facing new challenges. Their design is no longer limited to minimizing some loss function but, additionally, needs to consider other resources that are critical when learning at scale. In this thesis, we explore different models and measures for learning with limited resources that have a budget. What budgetary constraints are posed by modern datasets? Can we reuse or combine existing machine learning paradigms to address these challenges at scale? How does the cost metrics change when we shift to distributed models for learning? These are some of the questions that have been investigated in this thesis. The answers to these questions hold the key to addressing some of the challenges faced when learning on massive datasets. In the first part of this thesis, we present three different budgeted scenarios that deal with scarcity of labeled data and limited computational resources. The goal is to leverage transfer information from related domains to learn under budgetary constraints. Our proposed techniques comprise semisupervised transfer, online transfer and active transfer. In the second part of this thesis, we study distributed learning with limited communication. We present initial sampling based results, as well as, propose communication protocols for learning distributed linear classifiers

    Inconsistency, Instability, and Generalization Gap of Deep Neural Network Training

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    As deep neural networks are highly expressive, it is important to find solutions with small generalization gap (the difference between the performance on the training data and unseen data). Focusing on the stochastic nature of training, we first present a theoretical analysis in which the bound of generalization gap depends on what we call inconsistency and instability of model outputs, which can be estimated on unlabeled data. Our empirical study based on this analysis shows that instability and inconsistency are strongly predictive of generalization gap in various settings. In particular, our finding indicates that inconsistency is a more reliable indicator of generalization gap than the sharpness of the loss landscape. Furthermore, we show that algorithmic reduction of inconsistency leads to superior performance. The results also provide a theoretical basis for existing methods such as co-distillation and ensemble
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