Uses[ edit ] The simplest use of brain-in-a-vat scenarios is as an argument for philosophical skepticism  and solipsism.
Upson B17 Abstract Over the last decade, much of the research on discriminative learning has focused on problems like classification and regression, where the prediction is a single univariate variable.
But what if we need to predict complex objects like trees, orderings, or alignments? Such problems arise, for example, when a natural language parser needs to predict the correct parse tree for a given sentence, when one needs Vincent conitzer thesis optimize a multivariate performance measure like the F1-score, or when predicting the alignment between two proteins.
In particular, the tutorial focuses on large-margin approaches to predicting structured outputs, and how the idea of margins can be generalized to complex prediction problems and a large range of loss functions.
While the resulting training problems have exponential size, there are tractable algorithms that allow training in polynomial time. The methods will be illustrated with examples from application problems. Methods, Theory, and Algorithms", advised by Prof.
Katharina Morik at the University of Dortmund.
From there he also received his Diplom in Computer Science in with a thesis on WebWatcher, a browsing assistant for the Web.
His research interests center on a synthesis of theory and system building in the field of machine learning, with a focus on Support Vector Machines and machine learning with text.
He authored the SVM-Light algorithm and software for support vector learning. From to he was a visiting scientist at Carnegie Mellon University with Prof.
Upson Abstract In settings such as auctions and elections, an outcome must be chosen based on the preferences of multiple parties agents.
Typically, each agent initially only knows its own preferences, and will not disclose its true preferences unless it feels that doing so is in its own interest. The challenge is to design the outcome-choosing mechanism in such a way that a good result is obtained nevertheless -- for example, by making it optimal for each agent to reveal its true preferences.
Mechanism design has traditionally been studied by game theorists, economists, and political scientists; but new applications such as combinatorial auctions, job scheduling, and webpage ranking have drawn many computer scientists to the field.
The tutorial is designed for computer scientists with no background in game theory or mechanism design. The basics of "classical" mechanism design, including basic definitions, the revelation principle, Vickrey-Clarke-Groves mechanisms, and impossibility results.
Computational aspects of mechanism design, including whether and how mechanisms' outcomes can be efficiently computed; algorithms for automatically designing the entire mechanism; and limitations of classical results in the face of computationally bounded agents.
Example applications will be provided throughout. Speaker Biography Vincent Conitzer is a Ph. He holds an M. He has published over 30 distinct technical papers on computational issues in game theory, mechanism design, auctions, elections, and other negotiation settings.
He is supported by an IBM Ph. Upson Abstract Inductive Transfer a. In this tutorial we'll show how to perform inductive transfer with supervised learning methods such as neural nets, k-nearest neighbor, SVMs, naive bayes, and bayes nets. We'll also briefly review the history of inductive transfer, discuss what kinds of applications will benefit from inductive transfer, and give a few heuristics for getting the most benefit when using transfer on real problems.
He recieved his Ph. Most of Rich's research is in machine learning and data mining, and applications of these to problems in medical decision making, bioinformatics, and environmental science.Vincent Conitzer is the Sally Dalton Robinson Professor of Computer Science and Professor of Economics at Duke University.
He received PhD () and MS () degrees in Computer Science from Carnegie Mellon University, and a BA () in Applied Mathematics from Harvard University. Economic Foundations of Practical Social Computing by Nisarg Shah Carnegie Mellon University [email protected] Advised by: Ariel D.
Procaccia, Carnegie Mellon University. Other Thesis Committee Members: Maria-Florina Balcan, Carnegie Mellon University. Avrim Blum, Carnegie Mellon University. Vincent Conitzer, Duke University. I would also like to thank other members of my thesis committee, namely, Fernando Ordo´nez,˜ Vincent Conitzer, Bhaskar Krishnamachari and Mathew McCubbins for their helpful feedback and guidance.
A special thanks to Fernando, since he was a tremendous guide to me when I started my research on developing scalable algorithms. List of computer science publications by Vincent Conitzer.
Vincent Conitzer is a Ph.D. candidate at Carnegie Mellon University, advised by Tuomas Sandholm. He holds an M.S. in Computer Science from CMU () and a .
Vincent Conitzer is the Sally Dalton Robinson Professor of Computer Science and Professor of Economics at Duke University. He received PhD () and MS () degrees in Computer Science from Carnegie Mellon University, and a BA () in Applied Mathematics from Harvard University.
His research focuses on computational .