Svetha Venkatesh

research

∙ 12/03/2021

Episodic Policy Gradient Training

We introduce a novel training procedure for policy gradient methods wher...

Hung Le, et al. ∙

research

∙ 11/27/2021

Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization

Offline policy learning (OPL) leverages existing data collected a priori...

Thanh Nguyen-Tang, et al. ∙

research

∙ 11/03/2021

Model-Based Episodic Memory Induces Dynamic Hybrid Controls

Episodic control enables sample efficiency in reinforcement learning by ...

Hung Le, et al. ∙

research

∙ 10/26/2021

Semantic Host-free Trojan Attack

In this paper, we propose a novel host-free Trojan attack with triggers ...

Haripriya Harikumar, et al. ∙

research

∙ 10/13/2021

A Field Guide to Scientific XAI: Transparent and Interpretable Deep Learning for Bioinformatics Research

Deep learning has become popular because of its potential to achieve hig...

Thomas P. Quinn, et al. ∙

research

∙ 08/20/2021

Plug and Play, Model-Based Reinforcement Learning

Sample-efficient generalisation of reinforcement learning approaches hav...

Majid Abdolshah, et al. ∙

research

∙ 07/24/2021

Clustering by Maximizing Mutual Information Across Views

We propose a novel framework for image clustering that incorporates join...

Kien Do, et al. ∙

research

∙ 07/24/2021

Combining Online Learning and Offline Learning for Contextual Bandits with Deficient Support

We address policy learning with logged data in contextual bandits. Curre...

Hung Tran-The, et al. ∙

research

∙ 07/18/2021

A New Representation of Successor Features for Transfer across Dissimilar Environments

Transfer in reinforcement learning is usually achieved through generalis...

Majid Abdolshah, et al. ∙

research

∙ 05/20/2021

A Spatio-temporal Attention-based Model for Infant Movement Assessment from Videos

The absence or abnormality of fidgety movements of joints or limbs is st...

Binh Nguyen-Thai, et al. ∙

research

∙ 05/10/2021

Bayesian Optimistic Optimisation with Exponentially Decaying Regret

Bayesian optimisation (BO) is a well-known efficient algorithm for findi...

Hung Tran-The, et al. ∙

research

∙ 04/11/2021

ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms

Machine learning models are being used extensively in many important are...

Huong Ha, et al. ∙

research

∙ 03/11/2021

On Finite-Sample Analysis of Offline Reinforcement Learning with Deep ReLU Networks

This paper studies the statistical theory of offline reinforcement learn...

Thanh Nguyen-Tang, et al. ∙

research

∙ 03/03/2021

Learning Asynchronous and Sparse Human-Object Interaction in Videos

Human activities can be learned from video. With effective modeling it i...

Romero Morais, et al. ∙

research

∙ 12/17/2020

High Dimensional Level Set Estimation with Bayesian Neural Network

Level Set Estimation (LSE) is an important problem with applications in ...

Huong Ha, et al. ∙

research

∙ 12/03/2020

Semi-Supervised Learning with Variational Bayesian Inference and Maximum Uncertainty Regularization

We propose two generic methods for improving semi-supervised learning (S...

Kien Do, et al. ∙

research

∙ 11/19/2020

Logically Consistent Loss for Visual Question Answering

Given an image, a back-ground knowledge, and a set of questions about an...

Anh Cat Le Ngo, et al. ∙

research

∙ 10/18/2020

Hierarchical Conditional Relation Networks for Multimodal Video Question Answering

Video QA challenges modelers in multiple fronts. Modeling video necessit...

Thao Minh Le, et al. ∙

research

∙ 09/24/2020

Neurocoder: Learning General-Purpose Computation Using Stored Neural Programs

Artificial Neural Networks are uniquely adroit at machine learning by pr...

Hung Le, et al. ∙

research

∙ 09/16/2020

Theory of Mind with Guilt Aversion Facilitates Cooperative Reinforcement Learning

Guilt aversion induces experience of a utility loss in people if they be...

Dung Nguyen, et al. ∙

research

∙ 09/08/2020

Sequential Subspace Search for Functional Bayesian Optimization Incorporating Experimenter Intuition

We propose an algorithm for Bayesian functional optimisation - that is, ...

Alistair Shilton, et al. ∙

research

∙ 09/05/2020

Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces

Bayesian optimisation is a popular method for efficient optimisation of ...

Hung Tran-The, et al. ∙

research

∙ 08/20/2020

Learning to Abstract and Predict Human Actions

Human activities are naturally structured as hierarchies unrolled over t...

Romero Morais, et al. ∙

research

∙ 07/24/2020

Distributional Reinforcement Learning with Maximum Mean Discrepancy

Distributional reinforcement learning (RL) has achieved state-of-the-art...

Thanh Tang Nguyen, et al. ∙

research

∙ 06/19/2020

Bayesian Optimization with Missing Inputs

Bayesian optimization (BO) is an efficient method for optimizing expensi...

Phuc Luong, et al. ∙

research

∙ 06/10/2020

Scalable Backdoor Detection in Neural Networks

Recently, it has been shown that deep learning models are vulnerable to ...

Haripriya Harikumar, et al. ∙

research

∙ 06/08/2020

Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation

In order to improve the performance of Bayesian optimisation, we develop...

Julian Berk, et al. ∙

research

∙ 06/02/2020

DeepCoDA: personalized interpretability for compositional health

Interpretability allows the domain-expert to directly evaluate the model...

Thomas P. Quinn, et al. ∙

research

∙ 05/18/2020

HyperVAE: A Minimum Description Length Variational Hyper-Encoding Network

We propose a framework called HyperVAE for encoding distributions of dis...

Phuoc Nguyen, et al. ∙

research

∙ 04/30/2020

Dynamic Language Binding in Relational Visual Reasoning

We present Language-binding Object Graph Network, the first neural reaso...

Thao Minh Le, et al. ∙

research

∙ 03/27/2020

Incorporating Expert Prior in Bayesian Optimisation via Space Warping

Bayesian optimisation is a well-known sample-efficient method for the op...

Anil Ramachandran, et al. ∙

research

∙ 02/26/2020

Incorporating Expert Prior Knowledge into Experimental Design via Posterior Sampling

Scientific experiments are usually expensive due to complex experimental...

Cheng Li, et al. ∙

research

∙ 02/25/2020

Hierarchical Conditional Relation Networks for Video Question Answering

Video question answering (VideoQA) is challenging as it requires modelin...

Thao Minh Le, et al. ∙

research

∙ 02/10/2020

Self-Assttentive Associative Memory

Heretofore, neural networks with external memory are restricted to singl...

Hung Le, et al. ∙

research

∙ 02/10/2020

Self-Attentive Associative Memory

Heretofore, neural networks with external memory are restricted to singl...

Hung Le, et al. ∙

research

∙ 01/19/2020

Distributionally Robust Bayesian Quadrature Optimization

Bayesian quadrature optimization (BQO) maximizes the expectation of an e...

Thanh Tang Nguyen, et al. ∙

research

∙ 11/28/2019

Bayesian Optimization for Categorical and Category-Specific Continuous Inputs

Many real-world functions are defined over both categorical and category...

Dang Nguyen, et al. ∙

research

∙ 11/27/2019

Trading Convergence Rate with Computational Budget in High Dimensional Bayesian Optimization

Scaling Bayesian optimisation (BO) to high-dimensional search spaces is ...

Hung Tran-The, et al. ∙

research

∙ 10/29/2019

Bayesian Optimization with Unknown Search Space

Applying Bayesian optimization in problems wherein the search space is u...

Huong Ha, et al. ∙

research

∙ 09/10/2019

Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning

Prior access to domain knowledge could significantly improve the perform...

Thommen George Karimpanal, et al. ∙

research

∙ 09/09/2019

Cost-aware Multi-objective Bayesian optimisation

The notion of expense in Bayesian optimisation generally refers to the u...

Majid Abdolshah, et al. ∙

research

∙ 07/22/2019

Accelerating Experimental Design by Incorporating Experimenter Hunches

Experimental design is a process of obtaining a product with target prop...

Cheng Li, et al. ∙

research

∙ 07/10/2019

Learning to Reason with Relational Video Representation for Question Answering

How does machine learn to reason about the content of a video in answeri...

Thao Minh Le, et al. ∙

research

∙ 06/21/2019

Sparse Spectrum Gaussian Process for Bayesian Optimisation

We propose a novel sparse spectrum approximation of Gaussian process (GP...

Ang Yang, et al. ∙

research

∙ 05/25/2019

Neural Stored-program Memory

Neural networks powered with external memory simulate computer behaviors...

Hung Le, et al. ∙

research

∙ 04/04/2019

Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection

Deep autoencoder has been extensively used for anomaly detection. Traini...

Dong Gong, et al. ∙

research

∙ 03/08/2019

Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos

Appearance features have been widely used in video anomaly detection eve...

Romero Morais, et al. ∙

research

∙ 02/21/2019

Stable Bayesian Optimisation via Direct Stability Quantification

In this paper we consider the problem of finding stable maxima of expens...

Alistair Shilton, et al. ∙

research

∙ 02/12/2019

Multi-objective Bayesian optimisation with preferences over objectives

We present a Bayesian multi-objective optimisation algorithm that allows...

Majid Abdolshah, et al. ∙

research

∙ 02/11/2019

Improving Generalization and Stability of Generative Adversarial Networks

Generative Adversarial Networks (GANs) are one of the most popular tools...

Hoang Thanh-Tung, et al. ∙

Svetha Venkatesh

Featured Co-authors