5 citations · 5 across the 4 of their papers we have counts for
5 papers
Contextual MetaML: Syntax and Full Abstraction
Haoxuan Yin, Andrzej S. Murawski, C. -H. Luke Ong
MetaML-style metaprogramming languages allow programmers to construct, manipulate and run code. In the presence of higher-order references for code, ensuring type safety is challen…
Conformal Prediction Meets Long-tail Classification
Shuqi Liu, Jianguo Huang, Luke Ong
Conformal Prediction (CP) is a popular method for uncertainty quantification that converts a pretrained model's point prediction into a prediction set, with the set size reflecting…
The Singapore Consensus on Global AI Safety Research Priorities
Yoshua Bengio, Tegan Maharaj, Luke Ong +84
Rapidly improving AI capabilities and autonomy hold significant promise of transformation, but are also driving vigorous debate on how to ensure that AI is safe, i.e., trustworthy,…
Guaranteed Bounds on Posterior Distributions of Discrete Probabilistic Programs with Loops
Fabian Zaiser, Andrzej S. Murawski, C. -H. Luke Ong
We study the problem of bounding the posterior distribution of discrete probabilistic programs with unbounded support, loops, and conditioning. Loops pose the main difficulty in th…
Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support
Tim Reichelt, Luke Ong, Tom Rainforth
The posterior in probabilistic programs with stochastic support decomposes as a weighted sum of the local posterior distributions associated with each possible program path. We sho…