Svetha Venkatesh

research

∙ 02/01/2023

Gradient Descent in Neural Networks as Sequential Learning in RKBS

The study of Neural Tangent Kernels (NTKs) has provided much needed insi...

Alistair Shilton, et al. ∙

research

∙ 01/17/2023

Memory-Augmented Theory of Mind Network

Social reasoning necessitates the capacity of theory of mind (ToM), the ...

Dung Nguyen, et al. ∙

research

∙ 11/23/2022

On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation

Sample-efficient offline reinforcement learning (RL) with linear functio...

Thanh Nguyen-Tang, et al. ∙

research

∙ 09/21/2022

Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation

Data-free Knowledge Distillation (DFKD) has attracted attention recently...

Kien Do, et al. ∙

research

∙ 07/25/2022

Black-box Few-shot Knowledge Distillation

Knowledge distillation (KD) is an efficient approach to transfer the kno...

Dang Nguyen, et al. ∙

research

∙ 05/25/2022

Guiding Visual Question Answering with Attention Priors

The current success of modern visual reasoning systems is arguably attri...

Thao Minh Le, et al. ∙

research

∙ 05/13/2022

Fast Conditional Network Compression Using Bayesian HyperNetworks

We introduce a conditional compression problem and propose a fast framew...

Phuoc Nguyen, et al. ∙

research

∙ 04/21/2022

Persistent-Transient Duality in Human Behavior Modeling

We propose to model the persistent-transient duality in human behavior u...

Hung Tran, et al. ∙

research

∙ 04/20/2022

Memory-Constrained Policy Optimization

We introduce a new constrained optimization method for policy gradient r...

Hung Le, et al. ∙

research

∙ 04/17/2022

Learning Theory of Mind via Dynamic Traits Attribution

Machine learning of Theory of Mind (ToM) is essential to build social ag...

Dung Nguyen, et al. ∙

research

∙ 03/15/2022

Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization

The expected improvement (EI) algorithm is one of the most popular strat...

Hung Tran-The, et al. ∙

research

∙ 02/24/2022

Towards Effective and Robust Neural Trojan Defenses via Input Filtering

Trojan attacks on deep neural networks are both dangerous and surreptiti...

Kien Do, et al. ∙

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. ∙

Svetha Venkatesh

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