Search Results for author: Cheng Soon Ong

Found 24 papers, 10 papers with code

Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families

1 code implementation14 Feb 2024 Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic

Maximum likelihood and maximum a posteriori estimates in a reparameterisation of the final layer of the intensity function can be obtained by solving a (strongly) convex optimisation problem using projected gradient descent.

Gaussian Processes Point Processes

Uncertainty Quantification of the Virial Black Hole Mass with Conformal Prediction

1 code implementation11 Jul 2023 Suk Yee Yong, Cheng Soon Ong

In contrast to baseline approaches for prediction interval estimation, we show that the CQR method provides prediction intervals that adjust to the black hole mass and its related properties.

Conformal Prediction Prediction Intervals +2

Deep equilibrium models as estimators for continuous latent variables

1 code implementation11 Nov 2022 Russell Tsuchida, Cheng Soon Ong

We consider a generalised setting where the canonical parameters of the exponential family are a nonlinear transformation of the latents.

Gaussian Process Bandits with Aggregated Feedback

no code implementations24 Dec 2021 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.

Factorized Fourier Neural Operators

2 code implementations27 Nov 2021 Alasdair Tran, Alexander Mathews, Lexing Xie, Cheng Soon Ong

We propose the Factorized Fourier Neural Operator (F-FNO), a learning-based approach for simulating partial differential equations (PDEs).

Declarative nets that are equilibrium models

no code implementations ICLR 2022 Russell Tsuchida, Suk Yee Yong, Mohammad Ali Armin, Lars Petersson, Cheng Soon Ong

We show that using a kernelised generalised linear model (kGLM) as an inner problem in a DDN yields a large class of commonly used DEQ architectures with a closed-form expression for the hidden layer parameters in terms of the kernel.

Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Series

1 code implementation15 Feb 2021 Alasdair Tran, Alexander Mathews, Cheng Soon Ong, Lexing Xie

We introduce Radflow, a novel model that embodies three key ideas: a recurrent neural network to obtain node embeddings that depend on time, the aggregation of the flow of influence from neighboring nodes with multi-head attention, and the multi-layer decomposition of time series.

Imputation Time Series +1

Quantile Bandits for Best Arms Identification

2 code implementations22 Oct 2020 Mengyan Zhang, Cheng Soon Ong

We consider a variant of the best arm identification task in stochastic multi-armed bandits.

Decision Making Multi-Armed Bandits

Disentangled behavioural representations

1 code implementation NeurIPS 2019 Amir Dezfouli, Hassan Ashtiani, Omar Ghattas, Richard Nock, Peter Dayan, Cheng Soon Ong

Individual characteristics in human decision-making are often quantified by fitting a parametric cognitive model to subjects' behavior and then studying differences between them in the associated parameter space.

Decision Making

New Tricks for Estimating Gradients of Expectations

no code implementations31 Jan 2019 Christian J. Walder, Paul Roussel, Richard Nock, Cheng Soon Ong, Masashi Sugiyama

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 between samples.

Representation Learning of Compositional Data

2 code implementations NeurIPS 2018 Marta Avalos, Richard Nock, Cheng Soon Ong, Julien Rouar, Ke Sun

Our approach combines the benefits of the log-ratio transformation from compositional data analysis and exponential family PCA.

Representation Learning

Monge blunts Bayes: Hardness Results for Adversarial Training

no code implementations8 Jun 2018 Zac Cranko, Aditya Krishna Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian Walder

A key feature of our result is that it holds for all proper losses, and for a popular subset of these, the optimisation of this central measure appears to be independent of the loss.

A Primer on Causal Analysis

no code implementations5 Jun 2018 Finnian Lattimore, Cheng Soon Ong

We provide a conceptual map to navigate causal analysis problems.


Provably Fair Representations

1 code implementation12 Oct 2017 Daniel McNamara, Cheng Soon Ong, Robert C. Williamson

These provable properties can be used in a governance model involving a data producer, a data user and a data regulator, where there is a separation of concerns between fairness and target task utility to ensure transparency and prevent perverse incentives.

BIG-bench Machine Learning Fairness

Revisiting revisits in trajectory recommendation

no code implementations17 Aug 2017 Aditya Krishna Menon, Dawei Chen, Lexing Xie, Cheng Soon Ong

Trajectory recommendation is the problem of recommending a sequence of places in a city for a tourist to visit.

A Modular Theory of Feature Learning

no code implementations9 Nov 2016 Daniel McNamara, Cheng Soon Ong, Robert C. Williamson

We propose the idea of a risk gap induced by representation learning for a given prediction context, which measures the difference in the risk of some learner using the learned features as compared to the original inputs.

Representation Learning

Hawkes Processes with Stochastic Excitations

no code implementations22 Sep 2016 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.

Learning Points and Routes to Recommend Trajectories

1 code implementation25 Aug 2016 Dawei Chen, Cheng Soon Ong, Lexing Xie

Information about POIs are used to learn a POI ranking model that accounts for the start and end points of tours.

Probabilistic Knowledge Graph Construction: Compositional and Incremental Approaches

no code implementations21 Aug 2016 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 analysis on the extracted information (knowledge completion).

graph construction

A scaled Bregman theorem with applications

no code implementations NeurIPS 2016 Richard Nock, Aditya Krishna Menon, Cheng Soon Ong

Experiments on each of these domains validate the analyses and suggest that the scaled Bregman theorem might be a worthy addition to the popular handful of Bregman divergence properties that have been pervasive in machine learning.

BIG-bench Machine Learning Clustering

Multivariate Spearman's rho for aggregating ranks using copulas

no code implementations16 Oct 2014 Justin Bedo, Cheng Soon Ong

Our main contribution is the derivation of a non-parametric estimator for rank aggregation based on multivariate extensions of Spearman's \rho, which measures correlation between a set of ranked lists.


Open science in machine learning

no code implementations24 Feb 2014 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.

BIG-bench Machine Learning

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