C. Shelton

发表

Policy Improvement for POMDPs Using Normalized Importance Sampling

Christian R. Shelton, C. Shelton, 2001, UAI.

Learning from Scarce Experience

Leonid Peshkin, Christian R. Shelton, C. Shelton, 2002, ICML.

Geometric Manipulation of Flexible Ligands

Lydia E. Kavraki, Rajeev Motwani, Suresh Venkatasubramanian, 1996, WACG.

Importance sampling for reinforcement learning with multiple objectives

Christian R. Shelton, C. Shelton, 2001 .

Expectation Maximization and Complex Duration Distributions for Continuous Time Bayesian Networks pdf

Daphne Koller, Christian R. Shelton, Uri Nodelman, 2005, UAI.

Continuous Time Bayesian Networks

Daphne Koller, Christian R. Shelton, Uri Nodelman, 2012, UAI.

Momentum control for balance

Victor B. Zordan, Victor Zordan, Christian R. Shelton, 2009, SIGGRAPH 2009.

Cobot: A Social Reinforcement Learning Agent

Peter Stone, Michael Kearns, Christian R. Shelton, 2001, NIPS.

Cobot in LambdaMOO: An Adaptive Social Statistics Agent

Peter Stone, Michael Kearns, Christian R. Shelton, 2006, Autonomous Agents and Multi-Agent Systems.

A social reinforcement learning agent

Peter Stone, Michael Kearns, Christian R. Shelton, 2001, AGENTS '01.

Momentum control for balance

Victor B. Zordan, Christian R. Shelton, Adriano Macchietto, 2009, ACM Trans. Graph..

Predicting conserved protein motifs with Sub-HMMs

Christian R. Shelton, Thomas Girke, Kevin Horan, 2010, BMC Bioinformatics.

Machine Learning, Machine Vision, and the Brain

Tomaso A. Poggio, Christian R. Shelton, T. Poggio, 1999, AI Mag..

Expectation Propagation for Continuous Time Bayesian Networks

Daphne Koller, Christian R. Shelton, Uri Nodelman, 2005, UAI.

An Electronic Market-maker

Christian R. Shelton, Nicholas Tung Chan, C. Shelton, 2001 .

RAPID: randomized pharmacophore identification for drug design

Lydia E. Kavraki, Rajeev Motwani, Suresh Venkatasubramanian, 1997, SCG '97.

Learning Continuous Time Bayesian Networks pdf

Daphne Koller, Christian R. Shelton, Uri Nodelman, 2002, UAI.

Interactive Event Search through Transfer Learning

C. Shelton, A. Roy-Chowdhury, Antony Lam, 2010, ACCV.

RAPID: randomized pharmacophore identification for drug design

Lydia E. Kavraki, Rajeev Motwani, Suresh Venkatasubramanian, 1997, SCG '97.

Learning Continuous-Time Social Network Dynamics pdf

Yu Fan, Christian R. Shelton, C. Shelton, 2009, UAI.

A perturbation scheme for spherical arrangements with application to molecular modeling

Dan Halperin, Christian R. Shelton, C. Shelton, 1997, SCG '97.

Continuous Time Bayesian Networks for Host Level Network Intrusion Detection

Jing Xu, Christian R. Shelton, C. Shelton, 2008, ECML/PKDD.

NIC-CAGE: An open-source software package for predicting optimal control fields in photo-excited chemical systems

Christian R. Shelton, Anshuman Kumar, Bryan M. Wong, 2020, Comput. Phys. Commun..

A new criterion function for exploratory blockmodeling for structural and regular equivalence

Christian R. Shelton, Robert A. Hanneman, Juan I. Casse, 2013, Soc. Networks.

Marked Point Process for Severity of Illness Assessment

Christian R. Shelton, Juan I. Casse, Kazi T. Islam, 2017, MLHC.

Application of Machine Learning Models for Tracking Participant Skills in Cognitive Training

Aaron R. Seitz, Susanne M. Jaeggi, C. Shelton, 2020, Frontiers in Psychology.

Annotating Genes of Known and Unknown Function by Large-Scale Coexpression Analysis1[W][OA]

C. Shelton, K. Horan, T. Girke, 2008, Plant Physiology.

Modeling Stock Order Flows and Learning Market-Making from Data

T. Poggio, C. Shelton, A. Kim, 2002 .

Memo No . 167 December 1998 Three-Dimensional Correspondence

C. Shelton, 1998 .

Three-dimensional Correspondence Three-dimensional Correspondence Three-dimensional Correspondence

C. Shelton, 1996 .

Learning in brains and machines.

T. Poggio, C. Shelton, 2000, Spatial vision.

First Light Adaptive Optics Images from the Keck II Telescope: A New Era of High Angular Resolution Imagery

D. S. Acton, Jong R. An, C. Shelton, 2000 .

Fast time series classification using numerosity reduction

Li Wei, Eamonn J. Keogh, Chotirat Ann Ratanamahatana, 2006, ICML.

Neural Stochastic Differential Equations with Bayesian Jumps for Marked Temporal Point Process

C. Shelton, K. Islam, 2020 .

Gene function prediction based on sequence or expression data

C. Shelton, K. Horan, T. Girke, 2011 .

Kernel-Based Feature Extraction and Speech Technology Applications

János Csirik, C. Shelton, J. Csirik, 2007 .

of Flexible Ligands

C. Shelton, J. Latombe, D. Halperin, 1996 .

Model performance on a more “ natural ” object class

C. Shelton, 2007 .

Lossless compression via two-level logic minimization: a case study using Chess endgame data

C. Shelton, Dave Gomboc, 2020 .

Deep Neyman-Scott Processes

C. Shelton, Chengkuan Hong, 2021, AISTATS.

Automated , Highly-accurate Bug Triaging Using Machine Learning

C. Shelton, Iulian Neamtiu, P. Bhattacharya, 2010 .

Harnessing deep neural networks to solve inverse problems in quantum dynamics: machine-learned predictions of time-dependent optimal control fields.

Bryan M. Wong, C. Shelton, Xian Wang, 2020, Physical chemistry chemical physics : PCCP.

Applying modality and equivalence concepts to pattern finding in social process-produced data

Christian R. Shelton, Robert A. Hanneman, C. Shelton, 2010, Social Network Analysis and Mining.

Alternating Optimization Algorithm for Blockmodeling Two-way Two-mode Data with Unknown Numbers of Clusters

Christian R. Shelton, Robert A. Hanneman, Juan I. Casse, 2011 .

Balancing Multiple Sources of Reward in Reinforcement Learning

Christian R. Shelton, C. Shelton, 2000, NIPS.

Investigating Generative Factors of Score Matrices

Christian R. Shelton, Titus Winters, Tom Payne, 2007, AIED.

Catenary Support Vector Machines

Christian R. Shelton, Kin Fai Kan, C. Shelton, 2008, ECML/PKDD.

Annotating Genes of Known and Unknown Function by Large-Scale Co-Expression Analysis

C. Shelton, K. Horan, T. Girke, 2008 .

A perturbation scheme for spherical arrangements with application to molecular modeling

Dan Halperin, Christian R. Shelton, C. Shelton, 1997, SCG '97.

Dimensionality reduction algorithms with applications to collaborative data and images

Christian R. Shelton, Guobiao Mei, C. Shelton, 2008 .

Explaining Contrasting Categories

Aaron R. Seitz, Michael Pazzani, Amir Feghahati, 2018, IUI Workshops.

Reinforcement Learning with Multiple Objectives

Christian R. Shelton, C. Shelton, 2001 .

Automated measurement of quasar redshift with a Gaussian process

R. Garnett, C. Shelton, S. Bird, 2020, Monthly Notices of the Royal Astronomical Society.

Importance Sampling for Continuous Time Bayesian Networks

Jing Xu, Yu Fan, Christian R. Shelton, 2010, J. Mach. Learn. Res..

Continuous Time Bayesian Network Reasoning and Learning Engine

Jing Xu, Yu Fan, Joon Lee, 2010, J. Mach. Learn. Res..

The Journal of Systems and Software 85 (2012) 2275–2292 Contents lists available at SciVerse ScienceDirect The Journal of Systems and Software

Iulian Neamtiu, Christian R. Shelton, Pamela Bhattacharya, 2022 .

Chess Endgame Compression via Logic Minimization

C. Shelton, Dave Gomboc, 2021, ACG.

Intrusion Detection using Continuous Time Bayesian Networks

Jing Xu, Christian R. Shelton, C. Shelton, 2010, J. Artif. Intell. Res..

Morphable Surface Models

Christian R. Shelton, C. Shelton, 2000, International Journal of Computer Vision.

Identification of destabilizing attacks in power systems

Hamed Mohsenian Rad, Mike Izbicki, Sajjad Amini, 2017, 2017 American Control Conference (ACC).

Simultaneous learning of motion and sensor model parameters for mobile robots

Christian R. Shelton, Teddy N. Yap, T. N. Yap, 2008, 2008 IEEE International Conference on Robotics and Automation.

Hawkes Process Inference With Missing Data

Zhen Qin, Christian R. Shelton, Chandini Shetty, 2018, AAAI.

Faster cover trees

Mike Izbicki, Christian R. Shelton, C. Shelton, 2015, ICML.

Unsupervised image embedding using nonparametric statistics

Christian R. Shelton, Guobiao Mei, C. Shelton, 2008, 2008 19th International Conference on Pattern Recognition.

Improving multi-target tracking via social grouping

Zhen Qin, Christian R. Shelton, C. Shelton, 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

MF-Box: Multi-fidelity and multi-scale emulation for the matter power spectrum

C. Shelton, M. Ho, M. Fernandez, 2023, 2306.03144.

Re-ranking using compression-based distance measure for Content-based Commercial Product Image Retrieval

Honggang Zhang, Jun Guo, Zhen Qin, 2012, 2012 19th IEEE International Conference on Image Processing.

A Continuation Method for Nash Equilibria in Structured Games

Daphne Koller, Christian R. Shelton, Ben Blum, 2003, IJCAI.

Convolutional Deep Exponential Families pdf

Christian R. Shelton, Chengkuan Hong, C. Shelton, 2021, ArXiv.

Cobot in LambdaMOO: An Adaptive Social Statistics Agent

Satinder Singh, P. Stone, C. Shelton, 2006, Autonomous Agents and Multi-Agent Systems.

Annotating historical archives of images

Eamonn J. Keogh, Christian R. Shelton, Xiaoyue Wang, 2008, Int. J. Digit. Libr. Syst..

Geometric Manipulation of F Lexible Ligands ?

C. Shelton, J. Latombe, P. Finn, 1996 .

Solving Structured Continuous-Time Markov Decision Processes

Christian R. Shelton, Kin Fai Kan, C. Shelton, 2008, ISAIM.

Event Detection in Continuous Video: An Inference in Point Process Approach

Christian R. Shelton, Zhen Qin, C. Shelton, 2017, IEEE Transactions on Image Processing.

Auxiliary Gibbs Sampling for Inference in Piecewise-Constant Conditional Intensity Models

Zhen Qin, Christian R. Shelton, C. Shelton, 2015, UAI.

Tutorial on Structured Continuous-Time Markov Processes

Gianfranco Ciardo, Christian R. Shelton, C. Shelton, 2014, J. Artif. Intell. Res..

Reinforcement Learning with Partially Known World Dynamics

Christian R. Shelton, C. Shelton, 2002, UAI.