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This paper shows NP-completeness for finding Hamiltonian cycles in induced subgraphs of the dual graphs of semi-regular tessilations. It also shows NP-hardness for a new, wide class of graphs called augmented square grids. This work follows up on prior studies of the complexity of finding Hamiltonian cycles in regular and semi-regular grid graphs.arXiv:1909.13755v1 fatcat:374ku2ki3rdbpkxlnqew36yjv4
Healthcare providers are increasingly using machine learning to predict patient outcomes to make meaningful interventions. However, despite innovations in this area, deep learning models often struggle to match performance of shallow linear models in predicting these outcomes, making it difficult to leverage such techniques in practice. In this work, motivated by the task of clinical prediction from insurance claims, we present a new technique called Reverse Distillation which pretrains deeparXiv:2007.05611v2 fatcat:c5gmxfxcnva47fm5gxvyaqkope
more »... els by using high-performing linear models for initialization. We make use of the longitudinal structure of insurance claims datasets to develop Self Attention with Reverse Distillation, or SARD, an architecture that utilizes a combination of contextual embedding, temporal embedding and self-attention mechanisms and most critically is trained via reverse distillation. SARD outperforms state-of-the-art methods on multiple clinical prediction outcomes, with ablation studies revealing that reverse distillation is a primary driver of these improvements. Code is available at https://github.com/clinicalml/omop-learn.
We consider a generalization of the classical Ski Rental Problem motivated by applications in cloud computing. We develop deterministic and probabilistic online algorithms for rent/buy decision problems with time-varying demand. We show that these algorithms have competitive ratios of 2 and 1.582 respectively. We also further establish the optimality of these algorithms. 233doi:10.1137/14s013032 fatcat:wypjrahiujhc7lwyaj77cat5ce
We consider a variant of the classic Ski Rental online algorithm with applications to machine learning. In our variant, we allow the skier access to a black-box machine-learning algorithm that provides an estimate of the probability that there will be at most a threshold number of ski-days. We derive a class of optimal randomized algorithms to determine the strategy that minimizes the worst-case expected competitive ratio for the skier given a prediction from the machine learning algorithm,andarXiv:1903.00092v2 fatcat:q5mhbndbwvcwrarguo7bbjzlfi
more »... nalyze the performance and robustness of these algorithms.
Kodialam and Lakshman  also modeled intrusion detection as a zero-sum game, albeit between the service provider and the intruder. ... Rohan Chabukswar was with the Department of Electrical and Computer Engineering of Carnegie Mellon University, Pittsburgh, PA, United States when this work was done. firstname.lastname@example.org Bruno Sinopoli ...doi:10.1109/acc.2015.7171934 dblp:conf/amcc/ChabukswarS15 fatcat:yigjwvo57nco3kyd44u7fzxada
Kodialam and Lakshman (2003) also modeled intrusion detection as a zero-sum game, albeit between the service provider and the intruder. ...doi:10.3182/20130925-2-de-4044.00033 fatcat:fe6uqb7e4fe2vmgdxne6tsngqa