<p>This archive includes the research data associated to the paper:</p> <p>Giuliano Casale. Accelerating Performance Inference over Closed Systems by Asymptotic Methods. Proc. ACM Meas. Anal. Comput. Syst., 1(1), 2017. The paper is accepted for presentation at ACM SIGMETRICS 2017.</p> <p>The research data requires MATLAB 2015a or later. Four datasets are included, each corresponding to a section of the paper:<br> - sec5.3.1: Small and medium models without infinite server nodes (Section 5.3.1)<br> - sec5.3.2: Large models without infinite server nodes (Section 5.3.2)<br> - sec5.3.3: Models with infinite server nodes (Section 5.3.3)<br> - sec5.4: Optimization programs (Section 5.4)</p> <p>A description of each dataset is included in the R...
Many machine learning approaches are characterized by information constraints on how they inter-act ...
Many traditional and newly-developed causal inference approaches require imposing strong data assump...
While the amount of data that we are able to collect keeps growing, the use of Machine Learning and ...
Recent years have seen a rapid growth of interest in exploiting monitoring data collected from enter...
Modern technological advances have prompted massive scale data collection in manymodern fields such ...
AbstractExperimental evaluations of speedup learning methods have in the past used non-parametric hy...
This thesis proposes new analysis tools for simulation models in the presence of data. To achieve a ...
Huge data sets containing millions of training examples with a large number of attributes (tall fat ...
This thesis presents multiple fundamental mathematical contributions to Generalized Linear Models (G...
Many traditional and newly-developed causal inference approaches require imposing strong data assump...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Comp...
International audienceMotivated by parallel optimization, we experiment EDA-like adaptation-rules in...
This thesis discusses the application of optimizations to machine learning algorithms. In particular...
(A) Mean performance of RL-Elo on non-terminal pairs throughout training and testing, sorted by symb...
Asynchronous and parallel implementation of standard reinforcement learning (RL) algorithms is a key...
Many machine learning approaches are characterized by information constraints on how they inter-act ...
Many traditional and newly-developed causal inference approaches require imposing strong data assump...
While the amount of data that we are able to collect keeps growing, the use of Machine Learning and ...
Recent years have seen a rapid growth of interest in exploiting monitoring data collected from enter...
Modern technological advances have prompted massive scale data collection in manymodern fields such ...
AbstractExperimental evaluations of speedup learning methods have in the past used non-parametric hy...
This thesis proposes new analysis tools for simulation models in the presence of data. To achieve a ...
Huge data sets containing millions of training examples with a large number of attributes (tall fat ...
This thesis presents multiple fundamental mathematical contributions to Generalized Linear Models (G...
Many traditional and newly-developed causal inference approaches require imposing strong data assump...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Comp...
International audienceMotivated by parallel optimization, we experiment EDA-like adaptation-rules in...
This thesis discusses the application of optimizations to machine learning algorithms. In particular...
(A) Mean performance of RL-Elo on non-terminal pairs throughout training and testing, sorted by symb...
Asynchronous and parallel implementation of standard reinforcement learning (RL) algorithms is a key...
Many machine learning approaches are characterized by information constraints on how they inter-act ...
Many traditional and newly-developed causal inference approaches require imposing strong data assump...
While the amount of data that we are able to collect keeps growing, the use of Machine Learning and ...