102 citations · 430 across the 25 of their papers we have counts for
16 papers · 1 filter
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…
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…
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 …
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…
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…
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…
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…
Open science in machine learning
Joaquin Vanschoren, Mikio L. Braun, Cheng Soon Ong
We present OpenML and mldata, open science platforms that provides easy access to machine learning data, software and results to encourage further study and application. They go be…