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Continuous Time Bayesian Networks
A probabilistic semantics for the language in terms of the generative model a CTBN defines over sequences of events is presented, and an algorithm for approximate inference which takes advantage of the structure within the process is provided.
Fast time series classification using numerosity reduction
While the idea of numerosity reduction for nearest-neighbor classifiers has a long history, it is shown here that it can leverage off an original observation about the relationship between dataset size and DTW constraints to produce an extremely compact dataset with little or no loss in accuracy.
Learning Continuous Time Bayesian Networks
It is shown that CTBNs can provide a better fit to continuous-time processes than DBNs with a fixed time granularity, and can tailor the parameters and dependency structure to the different time granularities of the evolution of different variables.
Automated, highly-accurate, bug assignment using machine learning and tossing graphs
Momentum control for balance
This work demonstrates how momentum is related to the center of mass and center of pressure of the character and derive control rules to change these centers for balance and results in natural balancing motions employing the entire body.
Improving multi-target tracking via social grouping
A general optimization framework that adds social grouping behavior (SGB) to any basic affinity model based upon a simple affinity model is built and shows very promising performance on two publicly available real-world datasets with different tracklet extraction methods.
A Continuation Method for Nash Equilibria in Structured Games
This approach provides the first efficient algorithm for computing exact equilibria in graphical games with arbitrary topology, and the first algorithm to exploit fine-grained structural properties of MAIDs.
An Electronic Market-Maker
An adaptive learning model for market-making under the reinforcement learning framework is presented, achieving multiple objectives including the maximizing of profits and minimization of the bid-ask spread.
Annotating Genes of Known and Unknown Function by Large-Scale Coexpression Analysis1[W][OA]
This study identified the PUF encoding genes from Arabidopsis (Arabidopsis thaliana) using a combination of sequence similarity, domain-based, and empirical approaches to associate the identified PUF genes with regulatory networks and biological processes of known function.
Importance sampling for reinforcement learning with multiple objectives
- C. Shelton
- Computer Science
This thesis considers three complications that arise from applying reinforcement learning to a real-world application, and employs importance sampling (likelihood ratios) to achieve good performance in partially observable Markov decision processes with few data.