102 citations · 430 across the 25 of their papers we have counts for
25 papers
Uncertainty Quantification of the Virial Black Hole Mass with Conformal Prediction
Suk Yee Yong, Cheng Soon Ong
Precise measurements of the black hole mass are essential to gain insight on the black hole and host galaxy co-evolution. A direct measure of the black hole mass is often restricte…
Squared Neural Families: A New Class of Tractable Density Models
Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic
Flexible models for probability distributions are an essential ingredient in many machine learning tasks. We develop and investigate a new class of probability distributions, which…
Deep equilibrium models as estimators for continuous latent variables
Russell Tsuchida, Cheng Soon Ong
Principal Component Analysis (PCA) and its exponential family extensions have three components: observations, latents and parameters of a linear transformation. We consider a gener…
Two-Stage Neural Contextual Bandits for Personalised News Recommendation
Mengyan Zhang, Thanh Nguyen-Tang, Fangzhao Wu +3
We consider the problem of personalised news recommendation where each user consumes news in a sequential fashion. Existing personalised news recommendation methods focus on exploi…
Gaussian Process Bandits with Aggregated Feedback
Mengyan Zhang, Russell Tsuchida, Cheng Soon Ong
We consider the continuum-armed bandits problem, under a novel setting of recommending the best arms within a fixed budget under aggregated feedback. This is motivated by applicati…
Factorized Fourier Neural Operators
Alasdair Tran, Alexander Mathews, Lexing Xie +1
We propose the Factorized Fourier Neural Operator (F-FNO), a learning-based approach for simulating partial differential equations (PDEs). Starting from a recently proposed Fourier…
Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Series
Alasdair Tran, Alexander Mathews, Cheng Soon Ong +1
We propose a new model for networks of time series that influence each other. Graph structures among time series are found in diverse domains, such as web traffic influenced by hyp…
Quantile Bandits for Best Arms Identification
Mengyan Zhang, Cheng Soon Ong
We consider a variant of the best arm identification task in stochastic multi-armed bandits. Motivated by risk-averse decision-making problems, our goal is to identify a set of …
Cold-start Playlist Recommendation with Multitask Learning
Dawei Chen, Cheng Soon Ong, Aditya Krishna Menon
Playlist recommendation involves producing a set of songs that a user might enjoy. We investigate this problem in three cold-start scenarios: (i) cold playlists, where we recommend…
New Tricks for Estimating Gradients of Expectations
Christian J. Walder, Paul Roussel, Richard Nock +2
We introduce a family of pairwise stochastic gradient estimators for gradients of expectations, which are related to the log-derivative trick, but involve pairwise interactions bet…
A Primer on Causal Analysis
Finnian Lattimore, Cheng Soon Ong
We provide a conceptual map to navigate causal analysis problems. Focusing on the case of discrete random variables, we consider the case of causal effect estimation from observati…
Monge blunts Bayes: Hardness Results for Adversarial Training
Zac Cranko, Aditya Krishna Menon, Richard Nock +3
The last few years have seen a staggering number of empirical studies of the robustness of neural networks in a model of adversarial perturbations of their inputs. Most rely on an…
Radio Galaxy Zoo: Machine learning for radio source host galaxy cross-identification
M. J. Alger, J. K. Banfield, C. S. Ong +6
We consider the problem of determining the host galaxies of radio sources by cross-identification. This has traditionally been done manually, which will be intractable for wide-are…
Radio Galaxy Zoo: ClaRAN - A Deep Learning Classifier for Radio Morphologies
Chen Wu, O. Ivy Wong, Lawrence Rudnick +13
The upcoming next-generation large area radio continuum surveys can expect tens of millions of radio sources, rendering the traditional method for radio morphology classification t…
Provably Fair Representations
Daniel McNamara, Cheng Soon Ong, Robert C. Williamson
Machine learning systems are increasingly used to make decisions about people's lives, such as whether to give someone a loan or whether to interview someone for a job. This has le…
Revisiting revisits in trajectory recommendation
Aditya Krishna Menon, Dawei Chen, Lexing Xie +1
Trajectory recommendation is the problem of recommending a sequence of places in a city for a tourist to visit. It is strongly desirable for the recommended sequence to avoid loops…
PathRec: Visual Analysis of Travel Route Recommendations
Dawei Chen, Dongwoo Kim, Lexing Xie +5
We present an interactive visualisation tool for recommending travel trajectories. This system is based on new machine learning formulations and algorithms for the sequence recomme…
Structured Recommendation
Dawei Chen, Lexing Xie, Aditya Krishna Menon +1
Current recommender systems largely focus on static, unstructured content. In many scenarios, we would like to recommend content that has structure, such as a trajectory of points-…
A Modular Theory of Feature Learning
Daniel McNamara, Cheng Soon Ong, Robert C. Williamson
Learning representations of data, and in particular learning features for a subsequent prediction task, has been a fruitful area of research delivering impressive empirical results…
Hawkes Processes with Stochastic Excitations
Young Lee, Kar Wai Lim, Cheng Soon Ong
We propose an extension to Hawkes processes by treating the levels of self-excitation as a stochastic differential equation. Our new point process allows better approximation in ap…
Probabilistic Knowledge Graph Construction: Compositional and Incremental Approaches
Dongwoo Kim, Lexing Xie, Cheng Soon Ong
Knowledge graph construction consists of two tasks: extracting information from external resources (knowledge population) and inferring missing information through a statistical an…
Learning Points and Routes to Recommend Trajectories
Dawei Chen, Cheng Soon Ong, Lexing Xie
The problem of recommending tours to travellers is an important and broadly studied area. Suggested solutions include various approaches of points-of-interest (POI) recommendation…
A scaled Bregman theorem with applications
Richard Nock, Aditya Krishna Menon, Cheng Soon Ong
Bregman divergences play a central role in the design and analysis of a range of machine learning algorithms. This paper explores the use of Bregman divergences to establish reduct…
Multivariate Spearman's rho for aggregating ranks using copulas
Justin Bedo, Cheng Soon Ong
We study the problem of rank aggregation: given a set of ranked lists, we want to form a consensus ranking. Furthermore, we consider the case of extreme lists: i.e., only the rank…