1 code implementation • 14 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.
1 code implementation • 11 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.
1 code implementation • 11 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.
no code implementations • 26 Jun 2022 • Mengyan Zhang, Thanh Nguyen-Tang, Fangzhao Wu, Zhenyu He, Xing Xie, Cheng Soon Ong
We consider the problem of personalised news recommendation where each user consumes news in a sequential fashion.
no code implementations • 24 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.
1 code implementation • 27 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).
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.
1 code implementation • 15 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.
2 code implementations • 22 Oct 2020 • Mengyan Zhang, Cheng Soon Ong
We consider a variant of the best arm identification task in stochastic multi-armed bandits.
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.
no code implementations • 31 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.
no code implementations • 18 Jan 2019 • Dawei Chen, Cheng Soon Ong, Aditya Krishna Menon
Playlist recommendation involves producing a set of songs that a user might enjoy.
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.
no code implementations • 8 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.
no code implementations • 5 Jun 2018 • Finnian Lattimore, Cheng Soon Ong
We provide a conceptual map to navigate causal analysis problems.
1 code implementation • 12 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.
no code implementations • 17 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.
no code implementations • 9 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.
no code implementations • 22 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.
1 code implementation • 25 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.
no code implementations • 21 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).
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.
no code implementations • 16 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.
no code implementations • 24 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.