S
Stephen Giguere
Researcher at University of Massachusetts Amherst
Publications - 24
Citations - 657
Stephen Giguere is an academic researcher from University of Massachusetts Amherst. The author has contributed to research in topics: Reinforcement learning & Spectroscopy. The author has an hindex of 11, co-authored 21 publications receiving 499 citations. Previous affiliations of Stephen Giguere include University of Texas at Austin & Worcester Polytechnic Institute.
Papers
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Journal ArticleDOI
Preventing undesirable behavior of intelligent machines.
TL;DR: A general framework for algorithm design is introduced in which the burden of avoiding undesirable behavior is shifted from the user to the designer of the algorithm, and this framework simplifies the problem of specifying and regulating undesirable behavior.
Proceedings ArticleDOI
Attribit: content creation with semantic attributes
TL;DR: Experiments suggest this interface is an effective alternative for novices performing tasks with high-level design goals, enabling rapid, in-situ exploration of candidate designs.
Book ChapterDOI
Contextual slip and prediction of student performance after use of an intelligent tutor
Ryan S. Baker,Albert T. Corbett,Sujith M. Gowda,Angela Z. Wagner,Benjamin A. MacLaren,Linda R. Kauffman,Aaron P. Mitchell,Stephen Giguere +7 more
TL;DR: This paper compares the Contextual-Guess-and-Slip variant on Bayesian knowledge tracing to classical four-parameter Bayesian Knowledge Tracing and the Individual Difference Weights variant of Bayesianknowledge Tracing, investigating how well each model variant predicts post-test performance.
Posted Content
Proximal Reinforcement Learning: A New Theory of Sequential Decision Making in Primal-Dual Spaces.
Sridhar Mahadevan,Bo Liu,Philip S. Thomas,Will Dabney,Stephen Giguere,Nicholas Jacek,Ian Gemp,Ji Liu +7 more
TL;DR: A new vision of reinforcement learning is set forth, one that yields mathematically rigorous solutions to longstanding important questions that have remained unresolved, and proximal operator theory enables the systematic development of operator splitting methods that show how to safely and reliably decompose complex products of gradients.
Journal ArticleDOI
Matrix Effects in Quantitative Analysis of Laser-Induced Breakdown Spectroscopy (LIBS) of Rock Powders Doped with Cr, Mn, Ni, Zn, and Co
K. H. Lepore,Caleb I. Fassett,E. A. Breves,Sarah Byrne,Stephen Giguere,T. Boucher,J. Michael Rhodes,Michael J. Vollinger,Chloe H Anderson,Richard W. Murray,M. Darby Dyar +10 more
TL;DR: The results showed the superiority of using normalization for predictions of minor elements when the predicted sample and those in the training set had matrices with similar SiO2 contents, and normalization also mitigates differences in spectra arising from laser/sample coupling effects and the use of different energy densities.