47 citations · 55 across the 14 of their papers we have counts for
16 papers
Enabling Secure and Ephemeral AI Workloads in Data Mesh Environments
Chinkit Patel, Kee Siong Ng
Many large enterprises that operate highly governed and complex ICT environments have no efficient and effective way to support their Data and AI teams in rapidly spinning up and t…
Privacy Technologies for Financial Intelligence
Yang Li, Thilina Ranbaduge, Kee Siong Ng
Financial crimes like money laundering and terrorism financing can have significant impacts on society, including loss of trust in the integrity of the financial system, misuse and…
Variational Inference for Scalable 3D Object-centric Learning
Tianyu Wang, Kee Siong Ng, Miaomiao Liu
We tackle the task of scalable unsupervised object-centric representation learning on 3D scenes. Existing approaches to object-centric representation learning show limitations in g…
A Direct Approximation of AIXI Using Logical State Abstractions
Samuel Yang-Zhao, Tianyu Wang, Kee Siong Ng
We propose a practical integration of logical state abstraction with AIXI, a Bayesian optimality notion for reinforcement learning agents, to significantly expand the model class t…
A Tutorial Introduction to Lattice-based Cryptography and Homomorphic Encryption
Yang Li, Kee Siong Ng, Michael Purcell
Why study Lattice-based Cryptography? There are a few ways to answer this question. 1. It is useful to have cryptosystems that are based on a variety of hard computational problems…
Belief Acquisition as Stochastic Filtering
Dawei Chen, John Lloyd, Samuel Yang-Zhao +1
This paper studies how belief acquisition can be accomplished using stochastic filtering. First, a theoretical foundation for empirical beliefs is outlined. Then stochastic filteri…
Private Graph Data Release: A Survey
Yang Li, Michael Purcell, Thierry Rakotoarivelo +3
The application of graph analytics to various domains has yielded tremendous societal and economical benefits in recent years. However, the increasingly widespread adoption of grap…
Spatially Invariant Unsupervised 3D Object-Centric Learning and Scene Decomposition
Tianyu Wang, Miaomiao Liu, Kee Siong Ng
We tackle the problem of object-centric learning on point clouds, which is crucial for high-level relational reasoning and scalable machine intelligence. In particular, we introduc…
Accurate and Efficient Suffix Tree Based Privacy-Preserving String Matching
Sirintra Vaiwsri, Thilina Ranbaduge, Peter Christen +1
The task of calculating similarities between strings held by different organizations without revealing these strings is an increasingly important problem in areas such as health in…
Towards Distributed Privacy-Preserving Prediction
Lingjuan Lyu, Yee Wei Law, Kee Siong Ng +4
In privacy-preserving machine learning, individual parties are reluctant to share their sensitive training data due to privacy concerns. Even the trained model parameters or predic…
Scalable Entity Resolution Using Probabilistic Signatures on Parallel Databases
Yuhang Zhang, Kee Siong Ng, Michael Walker +3
Accurate and efficient entity resolution is an open challenge of particular relevance to intelligence organisations that collect large datasets from disparate sources with differin…
Exploiting Redundancy, Recurrence and Parallelism: How to Link Millions of Addresses with Ten Lines of Code in Ten Minutes
Yuhang Zhang, Tania Churchill, Kee Siong Ng
Accurate and efficient record linkage is an open challenge of particular relevance to Australian Government Agencies, who recognise that so-called wicked social problems are best t…
Probabilities on Sentences in an Expressive Logic
Marcus Hutter, John W. Lloyd, Kee Siong Ng +1
Automated reasoning about uncertain knowledge has many applications. One difficulty when developing such systems is the lack of a completely satisfactory integration of logic and p…
The MADlib Analytics Library or MAD Skills, the SQL
Joe Hellerstein, Christopher Ré, Florian Schoppmann +8
MADlib is a free, open source library of in-database analytic methods. It provides an evolving suite of SQL-based algorithms for machine learning, data mining and statistics that r…
Context Tree Switching
Joel Veness, Kee Siong Ng, Marcus Hutter +1
This paper describes the Context Tree Switching technique, a modification of Context Tree Weighting for the prediction of binary, stationary, n-Markov sources. By modifying Context…
Reinforcement Learning via AIXI Approximation
Joel Veness, Kee Siong Ng, Marcus Hutter +1
This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. This approach is based on a direct approximation of AIXI, a Bayesian…