Sensory inference under conditions of uncertainty is a major problem in both machine learning and computational neuroscience. An important but poorly understood aspect of sensory processing is the role of active sensing. Here, we present a Bayes-optimal inference and control framework for active sensing, C-DAC (Context-Dependent Active Controller). Unlike previously proposed algorithms that opt...

Background In consumer lending, portfolio managers typically have access to a scorecard used to forecast default probability for each applicant. Scorecards are built using historical data on loan accounts and their respective performance data. The inputs into the scorecard include financial, demographic and other personal information about each applicant. The output of the scorecard is a real-v...

In this paper we investigate the use of MPCinspired neural network policies for sequential decision making. We introduce an extension to the DAGGER algorithm for training such policies and show how they have improved training performance and generalization capabilities. We take advantage of this extension to show scalable and efficient training of complex planning policy architectures in contin...

As the amount of data collected from individuals increases, there are more opportunities to use it to offer personalized experiences (e.g., using electronic health records to offer personalized treatments). We advocate applying techniques from batch reinforcement learning to predict the range of effectiveness that policies might have for individuals. We identify three sources of uncertainty and...

Title of Dissertation / Thesis: PERFORMANCE AND ANALYSIS OF SPOT TRUCK-LOAD PROCUREMENT MARKETS USING SEQUENTIAL AUCTIONS Miguel Andres Figliozzi, Ph.D., 2004 Dissertation / Thesis Directed By: Professor Hani Mahmassani, Civil and Environmental Engineering Department Competition in a transportation marketplace is studied under different supply/demand conditions, auction formats, and carriers’ b...

In the field of sequential decision making and reinforcement learning, it has been observed that good policies for most problems exhibit a significant amount of structure. In practice, this implies that when a learning agent discovers an action is better than any other in a given state, this action actually happens to also dominate in a certain neighbourhood around that state. This paper presen...

Sequential decision making problems often require an agent to act in an environment where data is noisy or not fully observed. The agent will have to learn how different actions relate to different rewards, and must therefore balance the need to explore and exploit in an effective strategy. In this report, sequential decision making problems are considered through extensions of the multi-armed ...

This chapter discusses decision making under uncertainty. More specifically, it offers an overview of efficient Bayesian and distribution-free algorithms for making near-optimal sequential decisions under uncertainty about the environment. Due to the uncertainty, such algorithms must not only learn from their interaction with the environment but also perform as well as possible while learning i...

Possibilistic decision theory is a natural one to consider when information about uncertainty cannot be quantified in probabilistic way. Different qualitative criteria based on possibility theory have been proposed, the definition of which requires a finite ordinal, non compensatory, scale for evaluating both utility and plausibility. In presence of heterogeneous information, i.e. when the know...