Download Roni Khardon - Tufts Computer Science

Roni Khardon
Dept. of Computer Science
Tufts University
Medford, MA 02155, USA
Email: [email protected]
http://www.cs.tufts.edu/˜roni/
Education
Ph.D. in Computer Science, Harvard University, 1996.
Thesis: Learning to be Competent.
Advisor: Prof. Leslie G. Valiant.
M.Sc. in Electrical Engineering, Technion (Israel), 1992.
Thesis: Optimizing Code Grain Size for Data Flow Machines.
Advisor: Dr. Shlomit Pinter.
B.Sc. in Computer Science, Technion (Israel), 1988.
Appointments
Tufts University Professor, September 2012 - Present.
Trinity College Dublin Visiting Professor, January - August 2014.
Tufts University Associate Professor, September 2006 - August 2012.
Tufts University Assistant Professor, September 2000 - August 2006.
University of Edinburgh, UK Lecturer (equivalent to Assistant Professor in the US system), 1997 - 2000
Harvard University Postdoctoral Fellow, 1996 - 1997.
Research Interests Broadly speaking I am interested in Intelligent Agents that can learn from
data, build representations of their world, use such knowledge for reasoning and decision
making, and act in their environment so as to optimize their objectives. At the technical
level, my recent work spans several subareas of AI and Machine Learning, both theoretical
and applied, including knowledge representation and inference, probabilistic planning, learning theory, and approximate inference algorithms and applications of probabilistic graphical
models.
Grants (PI or CO-PI)
PI, NSF grant IIS-1616280 ($447,122), “RI: Small: Stochastic Planning and Probabilistic
Inference for Factored State and Action Spaces”, 2016-2019.
PI, NSF grant IIS-0964457 ($418,228), “RI: Medium: Collaborative Research: Optimizing
Policies for Service Organizations in Complex Structured Domains”, 2010-2016.
Co-PI, NSF IIS-0803409 ($875,824), “III-CXT-Medium: Interdisciplinary Machine Learning
Research and Education”, 2008-2013. (PI: Carla Brodley).
PI, NSF grant IIS-0936687 ($77,808), “EAGER: First Order Decision Diagrams for Relational
Markov Decision Processes”, 2009-2010.
PI, NSF grant IIS-0099446 ($276,489), “Learning and Reasoning with Relational Structures”,
2001-2005.
PI, EPSRC grant GR/M21409 (£53270), “Learning Approximation and Reasoning”, 19982002 (UK).
PI, EPSRC grant GR/N03167 (£1500), “Learning Relational Expressions for Natural Language Applications”, visiting fellowship grant ,1999 (UK).
Research and Other Professional Activities
Program Committee Area Chair and/or Senior Program Committee
SPC: Twenty-Sixth National Conference on Artificial Intelligence (AAAI) 2017.
SPC: International Joint Conference on Artificial Intelligence (IJCAI) 2016.
SPC: Twenty-Sixth National Conference on Artificial Intelligence (AAAI) 2016.
SPC: Twenty-Sixth National Conference on Artificial Intelligence (AAAI) 2014.
SPC: Twenty-Sixth National Conference on Artificial Intelligence (AAAI) 2013.
SPC: Twenty-Fifth National Conference on Artificial Intelligence (AAAI) 2012. Received
Outstanding SPC award.
AC: Twenty-Fourth National Conference on Artificial Intelligence (AAAI) 2011.
SPC: International Joint Conference on Artificial Intelligence (IJCAI) 2011.
AC: Twenty-Fourth National Conference on Artificial Intelligence (AAAI) 2010.
AC: Twenty-Second International Conference on Machine Learning (ICML) 2005.
Program Co-Chair
Twelfth International Conference on Algorithmic Learning Theory (ALT), November 2001.
Organization
Co-Organizer and Workshop Co-Chair for the Learning Track in the 2008 International Planning Competition that is held as part of the International Conference on Automated Planning
and Scheduling (ICAPS) 2008.
Co-Organizer and Program Co-Chair for the Workshop on Logic and Learning, affiliated with
the IEEE symposium on Logic in Computer Science (LICS), June 2001.
Steering Committee
Member of the Steering Committee for the Algorithmic Learning Theory (ALT) conference
series, 2001-2007.
Graduate Students
Yuyang Wang, PhD (August 2013).
Saket Joshi, PhD (August 2010).
Gabriel Wachman, PhD (November, 2009).
Chenggang Wang, PhD (May 2007).
Marta Arias, PhD (May 2004).
External Examiner on Thesis Committee
External examiner of PhD for Tom Walsh, Department of computer Science, Rutgers University, 2010; advisor Michael Littman.
External examiner of PhD for Tom Croonenborghs, Department of computer Science, Katholieke
Universiteit Leuven, Belgium, 2009; advisor Hendrik Blockeel.
External examiner of PhD for Scott Sanner, Department of computer Science, University of
Toronto, 2007; advisor Craig Boutilier.
Outside member on PhD thesis committee for Jiang Chen, Computer Science Department,
Yale University, 2006; advisor Dana Angluin.
External reader of PhD for Jerome Maloberti, Laboratoire de Recherche en Informatique,
Universite Paris-Sud, France, 2005; advisor Michele Sebag.
Thesis Committee at Tufts
Bilal Ahmed (Computer Science, 2016; advisor Carla Brodley), Mona Yousofshahi (Computer
Science, 2014; advisor Soha Hassoun), Jingjing Liu (Computer Science, 2014; advisor Carla
Brodley), Byron Wallace (Computer Science, 2012; advisor Carla Brodley), Umaa Rebbapragada (Computer Science, 2010; advisor Carla Brodley), Audrey Girouard (Computer Science,
2010; advisor Robert Jacob), Thomas Oommen (Civil and Environmental Engineering, 2009;
advisor Laurie Baise), Rachel Lomasky (Computer Science, 2009; advisor Carla Brodley),
Leanne Hirshfield (Computer Science, 2009; advisor Robert Jacob), Ning Liu (Biomedical
Engineering, 2009; advisor Sergio Fantini), D. Sculley (Computer Science, 2008; advisor Carla
Brodley).
Editorial Work
Associate Editor for AI Access, a new initiative for open access books in the AI field, 2013-.
Associate Editor for the Artificial Intelligence Journal (AIJ) 2013-.
Associate Editor for the Journal of Artificial Intelligence Research (JAIR) July 2011-.
Associate Editor (action editor) for the Machine Learning Journal (ML), 2006-.
Member of the Editorial Board for the Journal of Artificial Intelligence Research (JAIR)
2010-2011.
Member of the Editorial Board for the Machine Learning Journal during 2003-2006.
Co-editor for a special issue of the journal Theoretical Computer Science including selected
papers from the ALT 2001 conference (with N. Abe); published February 2004.
Co-editor for the Proceedings of the 12th International Conference on Algorithmic Learning
Theory (ALT) 2001. Lecture Notes in Artificial Intelligence (LNAI) 2225, Springer, 2001.
(with N. Abe and T. Zeugmann).
Program Committee Member
AAAI (National Conference on Artificial Intelligence) 2017: senior program committee.
IJCAI (The International Joint Conference on Artificial Intelligence) 2016: senior program
committee.
AAAI (National Conference on Artificial Intelligence) 2016: senior program committee.
ICAPS (International Conference on Automated Planning and Scheduling) 2016.
IKDD CODS (ACM India SIGKDD Conference on Data Sciences) 2016.
UAI (Conference on Uncertainty in Artificial Intelligence) 2015.
ICAPS (International Conference on Automated Planning and Scheduling) 2015.
AAAI (National Conference on Artificial Intelligence) 2015.
StarAI (Statistical Relational AI workshop at UAI) 2015.
AAAI (National Conference on Artificial Intelligence) 2014: senior program committee.
ICML (International Conference on Machine Learning) 2014.
ISAIM (International Symposium on Artificial Intelligence and Mathematics) 2014.
KR (International Conference on Principles of Knowledge Representation and Reasoning)
2014.
ICAPS (International Conference on Automated Planning and Scheduling) 2014.
ECAI (European Conference on Artificial Intelligence) 2014.
StarAI (Statistical Relational AI workshop at AAAI) 2014.
FSEA (Fostering Smart Energy Applications through Advanced Visual Interfaces at AVI)
2014.
BUDA (Workshop on Big Uncertain Data at PODS) 2014.
AAAI (National Conference on Artificial Intelligence) 2013: senior program committee.
ICAPS (International Conference on Automated Planning and Scheduling) 2013.
StarAI (Statistical Relational AI workshop at AAAI) 2013.
AAAI (National Conference on Artificial Intelligence) 2012: senior program committee. Received Outstanding SPC award.
KR (International Conference on Principles of Knowledge Representation and Reasoning)
2012.
ICAPS (International Conference on Automated Planning and Scheduling) 2012.
ISAIM (International Symposium on Artificial Intelligence and Mathematics) 2012.
StarAI (Statistical Relational AI workshop at UAI) 2012.
SRL (Statistical Relational Learning workshop at ICML) 2012.
NECTAR track of ECML/PKDD (European Conference on Machine Learning) 2012.
AAAI (National Conference on Artificial Intelligence) 2011: area chair.
IJCAI (The International Joint Conference on Artificial Intelligence) 2011: senior program
committee.
ICML (International Conference on Machine Learning) 2011.
ILP (International Conference on Inductive Logic Programming) 2011.
ICAPS (International Conference on Automated Planning and Scheduling) 2011.
Planning and Learning Workshop (held with ICAPS) 2011.
Workshop on Generalized Planning (held with AAAI) 2011.
AAAI (National Conference on Artificial Intelligence) 2010: area chair.
ICML (International Conference on Machine Learning) 2010.
ECML/PKDD (European Conference on Machine Learning) 2010.
StarAI (Statistical Relational AI workshop at AAAI) 2010.
ALT (Conference on Algorithmic Learning Theory) 2009.
ICML (International Conference on Machine Learning) 2009.
IJCAI (The International Joint Conference on Artificial Intelligence) 2009.
Learning track of International Planning Competition 2008 (held as part of ICAPS 2008):
co-chair.
ICML (International Conference on Machine Learning) 2008.
Workshop on Algorithms for Large-Scale Information Processing in Knowledge Discovery
(held in conjunction with PAKDD) 2008.
ISAIM (International Symposium on Artificial Intelligence and Mathematics) 2008.
ICML (International Conference on Machine Learning) 2007.
COLT (The International Conference on Learning Theory) 2007.
AAAI (The US National Conference on Artificial Intelligence) 2007.
KDD (The International Conference on Knowledge Discovery and Data Mining) 2006.
AAAI (The US National Conference on Artificial Intelligence) 2006.
ILP (Conference on Inductive Logic Programming) 2006.
LLLL (Workshop on Learning with Logics and Logics for Learning) 2006.
ICML (International Conference on Machine Learning) 2005: area chair.
ECML (European Conference on Machine Learning) 2005.
ILP (Conference on Inductive Logic Programming) 2005.
AAAI (The US National Conference on Artificial Intelligence) 2005.
ICML (International Conference on Machine Learning) 2004.
Workshop on Rich Representations for Reinforcement Learning (held in conjunction with
ICML) 2005.
ILP (Conference on Inductive Logic Programming) 2004.
ECML (European Conference on Machine Learning) 2004.
ILP (Conference on Inductive Logic Programming) 2003.
ECML (European Conference on Machine Learning) 2003.
ILP (Conference on Inductive Logic Programming) 2002.
AAAI (The US National Conference on Artificial Intelligence) 2002.
ALT (Conference on Algorithmic Learning Theory) 2001: co-chair.
Workshop on Logic and Learning (adjoined to LICS 2001): co-chair.
ICML (International Conference on Machine Learning) 2001.
ILP (Conference on Inductive Logic Programming) 2000.
COLT (Conference on Computational Learning Theory) 2000.
ALT (Conference on Algorithmic Learning Theory) 2000.
ICML (International Conference on Machine Learning) 2000.
Workshop on Attribute-Value and Relational Learning: Crossing the Boundaries (adjoined
to ICML 2000).
ILP (Workshop on Inductive Logic Programming) 1999.
AAAI (The US National Conference on Artificial Intelligence) 1996.
Additional Reviews for Conferences
IJCAI (The International Joint Conference on Artificial Intelligence) 2013.
ALT (Conference on Algorithmic Learning Theory) 2012.
RECOMB (Research in Computational Molecular Biology) 2010.
COLT (Conference on Learning Theory) 2009.
STACS (Symposium on Theoretical Aspects of Computing) 2008.
COLT (Conference on Learning Theory) 2008.
ALT (Conference on Algorithmic Learning Theory) 2006.
STOC (Symposium of Theory of Computing) 2002.
NIPS (Neural Information Processing systems) 2001.
MFCS (Mathematical Foundations of Computer Science) 2000.
CC (Conference on Computational Complexity) 1999.
COLT (Conference on Computational Learning Theory) 1998.
ALT (Conference on Algorithmic Learning Theory) 1998.
STOC (Symposium of Theory of Computing) 1996.
Reviews for Journals
Acta Informatica,
Annals of Mathematics and Artificial Intelligence,
Artificial Intelligence,
Data Mining and Knowledge Discovery,
Discrete Applied Mathematics,
Information and Computation,
Information Processing Letters,
Journal of Artificial Intelligence Research,
Journal of Computer and System Sciences,
Journal of Machine Learning Research,
Logical Methods in Computer Science,
Machine Learning,
Theoretical Computer Science.
Theory and Practice of Logic Programming
Grant Reviews
Grant reviews and panel participation for NSF (National Science Foundation) 2002, 2003,
2008, 2009, 2011, 2013.
Grant reviews for the Research Foundation Flanders (FWO), 2014.
Grant reviews for the Netherlands Organisation for Scientific Research (NWO), 2012.
Grant reviews for NASA (National Aeronautics and Space Administration) 2011.
Grant reviews for ISF (Israel Science Foundation) 2001, 2003, 2010.
Grant reviews for EPSRC (Engineering and Physical Sciences Research Council, UK) 1999.
Colloquia and Other Talks
How to Take Advantage of Structure When Solving Large Relational MDPs, IBM Research,
Dublin, May 2014.
GFODDs - a representation and calculus for structured functions over possible worlds, Dagstuhl
Seminar 14201 “Horn formulas, directed hypergraphs, lattices and closure systems: related
formalisms and applications”, May 2014.
GFODDs - a representation and calculus for structured functions over possible worlds, Spring
workshop on Mining and Learning (SMiLe), Ostend, Belgium, March 2014.
First Order Decision Diagrams for Relational Markov Decision Processes, Trinity College
Dublin, February 2014.
Generalization Bounds for Online Learning with Pairwise Loss Functions, Trinity College
Dublin, November 2013.
Probabilistic Machine Learning with Applications to Science and Engineering, Trinity College
Dublin, September 2013.
Generalization Bounds for Online Learning with Pairwise Loss Functions, Technion, Electrical
Engineering, Machine Learning Group, December 2012.
First Order Decision Diagrams for Relational Markov Decision Processes, University of Illinois
at Urbana-Champaign, March 2011.
Classifying Stars: Machine Learning for Astrophysics, IBM Research Labs, Haifa, Israel,
December 2010.
First Order Decision Diagrams for Relational Markov Decision Processes, University of Massachusetts, Amherst, Planning Seminar, November 2010.
Stochastic Planning and Lifted Inference: Workshop on Statistical Relational AI (held with
AAAI), 2010.
First Order Decision Diagrams for Relational Markov Decision Processes, Rutgers University,
April 2010.
From Raw Measurements to Clean Catalogs: Automatic Filtering and Classification of Variable Stars in the MACHO Survey. Workshop on Applications of Machine Learning and AI
to Astrophysics and Cosmology, July 2009, Pasadena (as part of IJCAI 2009).
First Order Decision Diagrams for Relational Markov Decision Processes, Probabilistic Logic
Learning Summit, Leuven, Belgium, July 2009.
First Order Decision Diagrams for Relational Markov Decision Processes, Meeting of the
COST Action on Algorithmic Decision Theory, Cork, Ireland, April 2009.
Learning to Classify Graphs and Hypergraphs, Boston University, November 2008.
Learning to Classify Graphs and Hypergraphs, University of Toronto, Canada, December
2007.
Learning to Act in Relational MDPs, Workshop on Rich Representations for Reinforcement
Learning, Bonn, Germany, August 2005.
From Complexity Results to Efficient Systems, Workshop on Learning with Logics and Logics
for Learning, Kitakyushu, Japan, June 2005.
Kernels for Logic Learning: potential and overfitting. University of Kyoto, Japan, June 2005.
Kernels for Logic Learning: potential and overfitting. National Institute of Informatics,
Tokyo, Japan, June 2005.
Learning and Logic: Theoretical Foundations and Efficient Systems, University of Massachusetts at Amherst, February 2005.
Learning to Act in Relational MDPs, Dagstuhl seminar “Probabilistic, Logical and Relational
Learning - Towards a Synthesis”, February 2005.
Learning and Logic: Theoretical Foundations and Efficient Systems, University of Freiburg
(Germany), January 2005.
Learning and Logic: Theoretical Foundations and Efficient Systems, Technion (Israel), December 2004.
Panel member (presentation and discussion) in Workshop on Relational Reinforcement Learning, held as part of the International Conference on Machine Learning, July 2004.
Machine Learning and Logic, Department of Mathematics, Tufts University, February 2003.
Learning and Logic: Theory and Implementation, University of Illinois at Chicago, November
2002.
Recent Progress in Learning Logic Programs with Queries, 17th Workshop on Machine Intelligence, July 2000.
1998-2000 I gave several talks at Informatics in Edinburgh, in the: Computer Science Colloquium, Cognitive Science Seminar, Mathematical Reasoning Group, LFCS Lab Lunch.
Learning First Order Horn Expressions, York University, UK, 1998.
Learning Horn Expressions, University of Illinois at Urbana Champaign, USA, 1998.
Learning Logic Programs, Warwick University, UK, 1998.
Learning to be Competent, University of Helsinki, Finland, 1996.
Learning to be Competent, in AAAI Fall Symposium on Learning Complex Behaviors in
Adaptive Intelligent Systems, 1996.
Industrial Experience
VLSI designer, Motorola Semiconductors, Design center, Israel, 1988-1990. Worked in circuit
and system design of the MC68302, a VLSI chip in CMOS technology. Developed several
peripherals for the 68000 processor such as an interrupt controller, and special circuits such
as I/O drivers, and a clock generator.
Publications
Edited Volumes
[E2] N. Abe and R. Khardon (Guest Editors). Special issue of the journal Theoretical Computer Science, Volume 313, Issue 2, Pages 173-312, February 2004. Volume includes
complete and revised versions of selected papers from the ALT 2001 conference.
[E1] N. Abe, R. Khardon, and T. Zeugmann (Editors). Proceedings of the 12th International
Conference on Algorithmic Learning Theory (ALT) 2001. Lecture Notes in Artificial
Intelligence (LNAI) 2225, Springer, 2001.
Refereed Journal Articles
[J30] B. J. Hescott and R. Khardon, The Complexity of Reasoning with FODD and GFODD.
Artificial Intelligence, Volume 229, pages 1-32, 2015.
[J29] G. Garriga, R. Khardon and L. De Raedt.
Mining Closed Patterns in Relational,
Graph and Network Data. Annals of Mathematics and Artificial Intelligence (AMAI)
Volume 69, Issue 4, pp 315-342, 2013.
[J28] Y. Wang, R. Khardon, and P. Protopapas. Nonparametric Bayesian Estimation of
Periodic Functions. The Astrophysical Journal (ApJ), Vol 756, Number 1, pages 67-78,
2012.
[J27] D. Kim, P. Protopapas, M. Trichas, M. Rowan-Robinson, R. Khardon, C. Alcock and Y.
Byun. A Refined QSO Selection Method Using Diagnostics Tests: 663 QSO Candidates
in the LMC. The Astrophysical Journal (ApJ), Volume 747, Number 2, pages 107-117,
2012.
[J26] S. Joshi, K. Kersting, and R. Khardon. Decision-Theoretic Planning with Generalized
First Order Decision Diagrams. Artificial Intelligence, Volume 175, pages 2198-2222,
2011.
[J25] S. Joshi and R. Khardon. Probabilistic Relational Planning with First Order Decision
Diagrams. Journal of Artificial Intelligence Research, Volume 41, Pages 231-266, 2011.
[J24] D.W. Kim, P. Protopapas, Y.I. Byun, C. Alcock, R. Khardon and Trichas, M. Quasistellar Object Selection Algorithm Using Time Variability and Machine Learning: Selection of 1620 Quasi-stellar Object Candidates from MACHO Large Magellanic Cloud
Database The Astrophysical Journal (ApJ), Volume 735, Number 2, Pages 68-81, 2011.
[J23] A. Fern, P. Tadepalli and R .Khardon. The first learning track of the international
planning competition. Machine Learning, volume 84, Number 1, Pages 81-107, 2011.
[J22] C. Wang, S. Joshi and R. Khardon. First Order Decision Diagrams for Relational
MDPs. Journal of Artificial Intelligence Research, Volume 31, pages 431-472, 2008.
[J21] M. Arias and R. Khardon and J. Maloberti. Learning Horn Expressions with LogAn-H.
Journal of Machine Learning Research, Volume 8, pages 549–587, 2007.
[J20] R. Khardon and G. Wachman. Noise Tolerant Variants of the Perceptron Algorithm.
Journal of Machine Learning Research, volume 8, pages 227–248, 2007.
[J19] M. Arias and A. Feigelson and R. Khardon and R. Servedio. Polynomial Certificates
for Propositional Classes. Information and Computation, Volume 204, Issue 5, Pages
816-834, 2006.
[J18] M. Arias and R. Khardon. Complexity Parameters for First Order Classes. Machine
Learning, Volume 64, pages 121-144, 2006.
[J17] M. Arias and R. Khardon. The Subsumption Lattice and Query Learning.
of Computer and Systems Science, Volume 72, Issue 1, Pages 1-204, 2006.
Journal
[J16] R. Khardon and R. Servedio. Maximum Margin Algorithms with Boolean Kernels.
Journal of Machine Learning Research, Volume 6, pages 1405–1429, 2005.
[J15] R. Khardon, D, Roth and R. Servedio.
Kernels for On-Line Learning Algorithms.
Volume 24, pages 341-356, 2005.
Efficiency versus Convergence of Boolean
Journal of Artificial Intelligence Research,
[J14] D. Gunopulos, R. Khardon , H. Mannila, S. Saluja, H. Toivonen, and R. S. Sharma.
Discovering All Most Specific Sentences.
ACM Transactions on Database Systems,
Volume 28, Number 2, June 2003, pages 140-174.
[J13] M. Arias and R. Khardon.
Learning Closed Horn expressions.
Computation, Vol 178, 2002, pages 214-240.
Information and
[J12] R. Khardon. Learning function free Horn expressions. Machine Learning Vol 37, No
3, 1999, pages 241-275.
[J11] R. Khardon. Learning action strategies for planning domains. Artificial Intelligence,
Vol 113, 1999, pages 125-148.
[J10] R. Khardon, H. Mannila, and D. Roth.
Reasoning with examples: Propositional
formulae and database dependencies. Acta Informatica Vol 36, 1999, pages 267-286.
[J9] R. Khardon and D. Roth. Learning to reason with a restricted view. Machine Learning
Vol 35, No 2, 1999, pages 95-117.
[J8] R. Khardon.
57-90.
Learning to take actions.
Machine Learning Vol 35, No 1, 1999, pages
[J7] H. Aizenstein, A. Blum, R. Khardon, A. Kushilevitz, L. Pitt, and D. Roth. On learning
read-k satisfy-j DNF. SIAM Journal of Computing Vol 27, No 6, 1998, pages 1505-1530.
[J6] R. Khardon and D. Roth.
1997, pages 697-725.
Learning to reason.
Journal of the ACM, Vol 44, No 5,
[J5] R. Khardon and D. Roth. Defaults and relevance in model based reasoning. Artificial
Intelligence, Vol 97, No 1-2, 1997, pages 169-193.
[J4] R. Khardon and D. Roth. Reasoning with models. Artificial Intelligence 87(1-2):187–
213, 1996.
[J3] R. Khardon and S. Pinter. Partitioning and scheduling to counteract overhead. Parallel Computing, 22(1996):555–593.
[J2] R. Khardon. Translating between Horn representations and their characteristic models.
Journal of Artificial Intelligence Research 3(1995):349–372.
[J1] R. Khardon.
On using the Fourier transform to learn disjoint DNF. Information
Processing Letters, 49(5):219–222, March 1994.
Refereed Conference Articles
[C44] H. Cui and R. Khardon. Online Symbolic Gradient-Based Optimization for Factored
Action MDPs. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2016.
[C43] R. Sheth and R. Khardon. A Fixed-Point Operator for Inference in Variational Bayesian
Latent Gaussian Models. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2016.
[C42] A. Raghavan, R. Khardon, P. Tadepalli, and A. Fern. Memory-Efficient Symbolic
Online Planning for Factored MDPs. Proceedings of the International Conference on
Uncertainty in Artificial Intelligence (UAI), 2015.
[C41] R. Sheth, Y. Wang and R. Khardon. Sparse Variational Inference for Generalized Gaussian Process Models. Proceedings of the International Conference on Machine Learning
(ICML), 2015.
[C40] M. Issakkimuthu, A. Fern, R. Khardon, P. Tadepalli, and S. Xue. Hindsight Optimization for Probabilistic Planning with Factored Actions. Proceedings of the International
Conference on Automated Planning and Scheduling (ICAPS), 2015.
[C39] H. Cui, R. Khardon, A. Fern, and P. Tadepalli. Factored MCTS for Large Scale
Stochastic Planning. Proceedings of the AAAI Conference on Artificial Intelligence
(AAAI), 2015.
[C38] B. J. Hescott and R. Khardon, The Complexity of Reasoning with FODD and GFODD.
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2014. A preliminary version of [J30].
[C37] A. Raghavan, R. Khardon, A. Fern, and P. Tadepalli. Symbolic Opportunistic Policy
Iteration for Factored-Action MDPs. Proceedings of the International Conference on
Neural Information Processing Systems (NIPS), 2013.
[C36] S. Joshi, R. Khardon, P. Tadepalli, A. Raghavan, and A. Fern. Solving Relational
MDPs with Exogenous Events and Additive Rewards. Proceedings of the European
Conference on Machine Learning (ECML/PKDD), 2013.
[C35] Y. Wang and R. Khardon. Sparse Gaussian Processes for Multi-task Learning. Proceedings of the European Conference on Machine Learning (ECML/PKDD), 2012.
[C34] Y. Wang, R. Khardon, D. Pechyony and R. Jones. Generalization Bounds for Online
Learning Algorithms with Pairwise Loss Functions. Proceedings of the Annual Conference on Learning Theory (COLT) 2012.
[C33] B. Ahmed, I. Mendoza-Sanchez, R. Khardon, L. Abriola, E. Miller. A Mixture of Experts Based Discretization Approach for Characterizing Subsurface Contaminant Source
Zones. IEEE Statistical Signal Processing Workshop (SSP) 2012.
[C32] A. Raghavan, S. Joshi, P. Tadepalli, A. Fern, and R. Khardon. Planning in Factored
Action Spaces with Symbolic Dynamic Programming. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2012.
[C31] S. Joshi, P. Schermerhorn, R. Khardon, and M. Scheutz.
Abstract Planning for
Reactive Robots. IEEE International Conference on Robotics and Automation (ICRA),
2012.
[C30] Y. Wang, R. Khardon, and P. Protopapas. Shift-invariant Grouped Multi-task Learning for Gaussian Processes.
Proceedings of the European Conference on Machine
Learning (ECML/PKDD), 2010.
[C29] D. Preston and C. Brodley and R. Khardon and D. Sulla-Menashe and M. Friedl.
Redefining Class Definitions using Constraint-Based Clustering. Proceedings of the
ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD), 2010.
[C28] C. Wang and R. Khardon. Relational Partially Observable MDPs. Proceedings of
the AAAI Conference on Artificial Intelligence (AAAI), 2010.
[C27] S. Joshi, K. Kersting, and R. Khardon.
Self-Taught Decision Theoretic Planning
with First Order Decision Diagrams. Proceedings of the International Conference on
Automated Planning and Scheduling (ICAPS), 2010.
[C26] G. Wachman, R. Khardon, P. Protopapas and C. Alcock. Kernels for Time Series
Arising in Astronomy. Proceedings of the European Conference on Machine Learning
(ECML/PKDD), 2009.
[C25] S. Joshi, K. Kersting, and R. Khardon. Generalized First Order Decision Diagrams
for First Order Markov Decision Processes.
Proceedings of the International Joint
Conference on Artificial Intelligence (IJCAI), 2009. A preliminary version of [J26].
[C24] S. Joshi and R. Khardon. Stochastic Planning with First Order Decision Diagrams.
Proceedings of the International Conference on Automated Planning and Scheduling
(ICAPS), 2008. A preliminary version of [J25].
[C23] C. Wang and R. Khardon. Policy Iteration for Relational MDPs. Proceedings of the
International Conference on Uncertainty in Artificial Intelligence (UAI), 2007.
[C22] G. Wachman and R. Khardon.
Learning from Interpretations: A Rooted Kernel
for Ordered Hypergraphs. Proceedings of the International Conference on Machine
Learning (ICML), 2007.
[C21] C. Wang, S. Joshi and R. Khardon. First Order Decision Diagrams for Relational
MDPs.
Proceedings of the International Joint Conference on Artificial Intelligence
(IJCAI), 2007, pages 1095-1100. A preliminary version of [J22].
[C20] G. Garriga, R. Khardon and L. De Raedt. On Mining Closed Sets in Multi-Relational
Data.
Proceedings of the International Joint Conference on Artificial Intelligence
(IJCAI), 2007, pages 804-809. A preliminary version of [J29].
[C19] M. Arias and R. Khardon. The Subsumption Lattice and Query Learning. Proceedings of the Conference on Algorithmic Learning Theory (ALT), 2004, pages 410-424. A
preliminary version of [J17].
[C18] M. Arias and R. Khardon. Bottom-Up ILP using Large Refinement Steps. Proceedings of the Conference on Inductive Logic Programming (ILP), 2004, pages 26-42.
Material included in Journal article [J21]
[C17] M. Arias and R. Khardon and R. Servedio. Polynomial Certificates for Propositional
Classes. Proceedings of the Conference on Computational Learning Theory (COLT),
2003, pages 537-551. A preliminary version of [J19].
[C16] R. Khardon and R. Servedio. Maximum Margin Algorithms with Boolean Kernels.
Proceedings of the Conference on Computational Learning Theory (COLT), 2003, pages
87-101. A preliminary version of [J16].
[C15] M. Arias and R. Khardon. Complexity Parameters for First Order Classes. Proceedings of the Conference on Inductive Logic Programming (ILP), 2003, pages 22-37.
A preliminary version of [J18].
[C14] R. Khardon, D, Roth and R. Servedio.
Efficiency versus Convergence of Boolean
Kernels for On-Line Learning Algorithms. Proceedings of the International Conference
on Neural Information Processing Systems (NIPS), 2001, pages 423-430. A preliminary
version of [J15].
[C13] M. Arias and R. Khardon. A new algorithm for learning range restricted Horn expressions. Proceedings of the International Conference on Inductive Logic Programming
(ILP), 2000, pages 21-39. A preliminary version of [J13].
[C12] R. Khardon. Learning Horn expressions with LogAn-H. Proceedings of the International Conference on Machine Learning (ICML), 2000, pages 471-478. Material included
in Journal article [J21]
[C11] R. Khardon, D. Roth and L. Valiant.
Relational learning for NLP using linear
threshold elements. In Proceedings of the International Joint Conference on Artificial
Intelligence (IJCAI), 1999, pages 911-919.
[C10] R. Khardon. Learning range restricted Horn expressions. In Proceedings of the
European Conference on Computational Learning Theory (EuroCOLT), 1999, LNAI
1572, pages 111-125.
[C9] R. Khardon.
Learning first-order universal Horn expressions.
In Proceedings of
the Conference on Computational Learning Theory (COLT), 1998, pages 154-165. A
preliminary version of [J12].
[C8] D. Gunopulos, R. Khardon, H. Mannila, and H. Toivonen. Data mining, hypergraph
transversals, and machine learning. In Proceedings of the Symposium on Principles of
Database Systems (PODS), 1997, pages 209-216. A preliminary version of [J14].
[C7] R. Khardon. Learning to take actions. In Proceedings of the National Conference on
Artificial Intelligence (AAAI), 1996, pages 787–792. A preliminary version of [J8].
[C6] R. Khardon and D. Roth. Learning to reason with a restricted view. In Proceedings
of the Conference on Computational Learning Theory (COLT), 1995, pages 301–310. A
preliminary version of [J9].
[C5] R. Khardon and D. Roth. Defaults Reasoning with Models. In Proceedings of the
International Joint Conference on Artificial Intelligence (IJCAI), 1995, pages 319-325.
A preliminary version of [J5].
[C4] A. Blum, R. Khardon, A. Kushilevitz, L. Pitt, and D. Roth. On learning read-k satisfyj DNF. In Proceedings of the Conference on Computational Learning Theory (COLT),
1994, pages 110–117. A preliminary version of [J7].
[C3] R. Khardon and D. Roth.
Learning to reason.
In Proceedings of the National
Conference on Artificial Intelligence (AAAI), 1994, pages 682–687. A preliminary version
of [J6].
[C2] R. Khardon and D. Roth. Reasoning with models. In Proceedings of the National
Conference on Artificial Intelligence (AAAI), 1994, pages 1148–1153. A preliminary
version of [J4].
[C1] R. Khardon and S. Pinter. Choosing the right grains for data flow machines. In Proceedings of the International Conference on Parallel Processing (ICPP), 1991, pages
I672–I673. A preliminary version of [J3].
Other Conferences and Workshops and/or Invited Papers and/or Editorials
[O9] S. Joshi, R. Khardon, P. Tadepalli, A. Raghavan, and A. Fern. Relational Markov
Decision Processes: Promise and Prospects. Workshop on Statistical Relational AI
(StarAI), in AAAI 2013.
[O8] B. Ahmed, I. Mendoza-Sanchez, R. Khardon, L. Abriola, and E. Miller. A DiscriminativeGenerative Approach to the Characterization of Subsurface Contaminant Source Zones.
IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2012.
[O7] R. Khardon. Stochastic Planning and Lifted Inference, Workshop on Statistical Relational AI (StarAI), in AAAI 2010.
[O6] C. Wang, S. Joshi and R. Khardon. First Order Decision Diagrams for Relational MDPs.
Workshop on Statistical relational learning (SRL) in ICML 2006.
[O5] N. Abe, and R. Khardon. Foreword to Special Issue of Theoretical Computer Science,
313(2): 173-174 (2004).
[O4] N. Abe, R. Khardon, and T. Zeugmann. Editors’ Introduction. In Proceedings of the
12th International Conference on Algorithmic Learning Theory (ALT) 2001, pages 1-7.
Lecture Notes in Artificial Intelligence (LNAI) 2225, Springer, 2001.
[O3] M. Arias and R. Khardon. Learning Closed Horn expressions. Workshop on Logic
and Learning (held as part of the Symposium on Logic in Computer Science), 2001. A
preliminary version of [J13].
[O2] R. Khardon. Recent Progress in Learning Logic Programs with Queries. 17th Workshop
on Machine Intelligence, 2000.
[O1] R. Khardon. Learning to be Competent. AAAI Fall Symposium on Learning Complex
Behaviors in Adaptive Intelligent Systems, 1996.