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Kee Siong's Homepage
Kee Siong Ng
Adjunct Research Fellow,
Computer Sciences Laboratory,
College of Engineering and Computer Science,
The Australian National University.
keesiong dot ng at nicta dot com dot au

I'm now with the Making Sense of Data (NRL) group in NICTA.

Softwares

Download the latest version of Alkemy. (Escher is now incorported into Alkemy!!)
Download the latest version of Escher!
The Godel programming language can be found here.

I'm currently working on Bach. This is a work-in-progress, but a version of the system is available for experimentation on request.
Here's the literate program An Implementation of Bach.

Datasets and Useful Documents

Papers

Journal papers

Conference/Workshop papers

  • Probabilistic and Logical Beliefs
    J.W. Lloyd, K.S. Ng, In M. Dastani et al (Eds), LADS 2007, LNAI 5118, pp. 19-36, 2008.

  • Reflections on Agent Beliefs
    J.W. Lloyd, K.S. Ng, In M. Baldoni et al (Eds), DALT 2007, LNCS 4897, pp. 122-139, 2007.

  • Learning Modal Theories
    J.W. Lloyd, K.S. Ng, In S. Muggleton, R. Otero and A. Tamaddoni-Nezhad (Eds.): ILP 2006, LNAI 4455, pp. 320-334, 2007.
    - This paper presents a general framework for learning theories in a higher-order multi-modal logic.

  • (Agnostic) PAC Learning Concepts in Higher-order Logic, (A longer preprint.)
    K.S. Ng, In J. Furnkranz, T. Scheffer and M. Spiliopoulou (Eds.): ECML 2006, LNAI 4212, pp. 711-718, 2006.
    - This paper studies the PAC and agnostic PAC learnability of some function classes expressible in higher-order logic.

  • Generalization Behaviour of Alkemic Decision Trees,
    K.S. Ng, In S. Kramer and B. Pfahringer (Eds.): ILP 2005, LNAI 3625, pp. 246-263, 2005.
    - This paper studies the VC dimensions of some common function classes defined on structured data, including sets, multisets, trees, graphs, etc.

  • Predicate Selection for Structural Decision Trees, Additional notes
    K.S. Ng, J.W. Lloyd, In S. Kramer and B. Pfahringer (Eds.): ILP 2005, LNAI 3625, pp. 264-278, 2005.
    - This paper gives efficient algorithms for the problem of picking from a structured search space a predicate that partitions a set of examples well.

  • Personalisation for User Agents
    J.J.Cole, M.Gray, J.W. Lloyd, K.S. Ng, In Proceedings of the 4th International Joint Conference on Autonomous Agents and Multi Agent Systems (AAMAS-05), pp. 603-610, 2005.
    - This paper presents a symbolic machine learning framework for achieving personalisation in intelligent user agents.

  • Symbolic Learning for Adaptive Agents
    J.J. Cole, J.W. Lloyd, K.S. Ng, Proceedings of the Annual Partner Conference, Smart Internet Technology Cooperative Research Centre, pp 139--148, 2003
    - This paper presents an interesting new perspective on relational reinforcement learning.

  • Predictive Toxicology using a Decision-tree Learner
    K.S. Ng, J.W. Lloyd, A.W. Slater, The 2000-1 Predictive Toxicology Challenge Workshop, 5th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD-01), 2001

Theses

Miscellaneous