In a simulator-defined MDP, the Markovian dynamics and rewards are provided in the form of a simulator from which samples can be drawn. This paper studies MDP planning algorithms that attempt to minimize the number of simulator calls before terminating and outputting a policy that is approximately optimal with high...
Motivated by a real-world problem, we study a novel setting for budgeted optimization where the goal is to optimize an unknown function f(x) given a budget. In our setting, it is not practical to request samples of f(x) at precise input values due to the formidable cost of experimental setup...
Sequential supervised learning problems involve assigning a class label to each item in a sequence. Examples include part-of-speech tagging and text-to-speech mapping. A very general-purpose strategy for solving such problems is to construct a recurrent sliding window (R.SW) classifier, which maps some window of the input sequence plus some number...
A diagnostic policy species what test to perform next based on the results of previous tests and when to stop and make a diagnosis. Cost-sensitive diagnostic policies perform tradeoffs between (a) the costs of tests and (b) the costs of misdiagnoses. An optimal diagnostic policy minimizes the expected total cost....
What is the relationship between learning and reasoning? Much recent work in machine learning has been criticized for focusing on learning and ignoring reasoning. This paper attempts to describe the various ways in which machine learning research has (and has not) incorporated reasoning. The paper argues that there are important...
This volume contains the papers accepted for the informal workshop on Knowledge Compilation and Speedup Learning held along with the Machine Learning Conference in Aberdeen Scotland. This workshop is a sequel to the first Knowledge Compilation workshop, which was organized by Jim Bennett, Tom Dieterich, and Jack Mostow in Otter...
This paper addresses cost-sensitive classification in the setting where there are costs for measuring each attribute as well as costs for misclassification errors. We show how to formulate this as a Markov Decision Process in which the transition model is learned from the training data. Specifically we assume a set...
The field of machine learning has made major strides over the last 20 years. This document summarizes the major problem formulations that the discipline has studied, then reviews three tasks in cognitive networking and briefly discusses how aspects of those tasks fit these formulations. After this, it discusses challenges for...
Distance-based algorithms are machine learning algorithms that classify queries
by computing distances between these queries and a number of internally stored
exemplars. Exemplars that are closest to the query have the largest in
uence on
the classi cation assigned to the query. Two speci c distance-based algorithms, the
nearest neighbor...
Machine learning applied to computer architecture has rapidly transitioned from a theoretical novelty to being a driving force behind design, control, and simulation in practically all components. These machine-learning-based methodologies are further notable for their scalability to increasingly complex design challenges, which has allowed these methodologies to surpass the prior...