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08/01/2023
Predictive Modeling through Hyper-Bayesian Optimization
Model selection is an integral problem of model based optimization techn...
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06/01/2023
EMOTE: An Explainable architecture for Modelling the Other Through Empathy
We can usually assume others have goals analogous to our own. This assum...
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03/03/2023
BO-Muse: A human expert and AI teaming framework for accelerated experimental design
In this paper we introduce BO-Muse, a new approach to human-AI teaming f...
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03/03/2023
Neural-BO: A Black-box Optimization Algorithm using Deep Neural Networks
Bayesian Optimization (BO) is an effective approach for global optimizat...
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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...
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11/23/2022
On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation
Sample-efficient offline reinforcement learning (RL) with linear functio...
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09/16/2022
Continual Learning with Dependency Preserving Hypernetworks
Humans learn continually throughout their lifespan by accumulating diver...
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07/25/2022
Black-box Few-shot Knowledge Distillation
Knowledge distillation (KD) is an efficient approach to transfer the kno...
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07/08/2022
Defense Against Multi-target Trojan Attacks
Adversarial attacks on deep learning-based models pose a significant thr...
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05/25/2022
Guiding Visual Question Answering with Attention Priors
The current success of modern visual reasoning systems is arguably attri...
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05/13/2022
Fast Conditional Network Compression Using Bayesian HyperNetworks
We introduce a conditional compression problem and propose a fast framew...
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04/20/2022
Memory-Constrained Policy Optimization
We introduce a new constrained optimization method for policy gradient r...
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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...
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11/27/2021
Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization
Offline policy learning (OPL) leverages existing data collected a priori...
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10/26/2021
Semantic Host-free Trojan Attack
In this paper, we propose a novel host-free Trojan attack with triggers ...
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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...
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08/20/2021
Plug and Play, Model-Based Reinforcement Learning
Sample-efficient generalisation of reinforcement learning approaches hav...
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07/28/2021
Retinal Microvasculature as Biomarker for Diabetes and Cardiovascular Diseases
Purpose: To demonstrate that retinal microvasculature per se is a reliab...
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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...
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07/18/2021
A New Representation of Successor Features for Transfer across Dissimilar Environments
Transfer in reinforcement learning is usually achieved through generalis...
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05/10/2021
Bayesian Optimistic Optimisation with Exponentially Decaying Regret
Bayesian optimisation (BO) is a well-known efficient algorithm for findi...
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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...
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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...
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12/17/2020
High Dimensional Level Set Estimation with Bayesian Neural Network
Level Set Estimation (LSE) is an important problem with applications in ...
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11/19/2020
Logically Consistent Loss for Visual Question Answering
Given an image, a back-ground knowledge, and a set of questions about an...
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09/20/2020
Unsupervised Anomaly Detection on Temporal Multiway Data
Temporal anomaly detection looks for irregularities over space-time. Uns...
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09/08/2020
Sequential Subspace Search for Functional Bayesian Optimization Incorporating Experimenter Intuition
We propose an algorithm for Bayesian functional optimisation - that is, ...
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09/05/2020
Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces
Bayesian optimisation is a popular method for efficient optimisation of ...
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07/24/2020
Distributional Reinforcement Learning with Maximum Mean Discrepancy
Distributional reinforcement learning (RL) has achieved state-of-the-art...
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06/19/2020
Bayesian Optimization with Missing Inputs
Bayesian optimization (BO) is an efficient method for optimizing expensi...
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06/10/2020
Scalable Backdoor Detection in Neural Networks
Recently, it has been shown that deep learning models are vulnerable to ...
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06/08/2020
Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation
In order to improve the performance of Bayesian optimisation, we develop...
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06/02/2020
DeepCoDA: personalized interpretability for compositional health
Interpretability allows the domain-expert to directly evaluate the model...
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05/18/2020
HyperVAE: A Minimum Description Length Variational Hyper-Encoding Network
We propose a framework called HyperVAE for encoding distributions of dis...
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03/27/2020
Incorporating Expert Prior in Bayesian Optimisation via Space Warping
Bayesian optimisation is a well-known sample-efficient method for the op...
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02/26/2020
Incorporating Expert Prior Knowledge into Experimental Design via Posterior Sampling
Scientific experiments are usually expensive due to complex experimental...
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01/19/2020
Distributionally Robust Bayesian Quadrature Optimization
Bayesian quadrature optimization (BQO) maximizes the expectation of an e...
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11/28/2019
Bayesian Optimization for Categorical and Category-Specific Continuous Inputs
Many real-world functions are defined over both categorical and category...
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11/27/2019
Trading Convergence Rate with Computational Budget in High Dimensional Bayesian Optimization
Scaling Bayesian optimisation (BO) to high-dimensional search spaces is ...
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10/29/2019
Bayesian Optimization with Unknown Search Space
Applying Bayesian optimization in problems wherein the search space is u...
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09/10/2019
Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning
Prior access to domain knowledge could significantly improve the perform...
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09/09/2019
Cost-aware Multi-objective Bayesian optimisation
The notion of expense in Bayesian optimisation generally refers to the u...
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07/22/2019
Accelerating Experimental Design by Incorporating Experimenter Hunches
Experimental design is a process of obtaining a product with target prop...
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06/21/2019
Sparse Spectrum Gaussian Process for Bayesian Optimisation
We propose a novel sparse spectrum approximation of Gaussian process (GP...
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02/21/2019
Stable Bayesian Optimisation via Direct Stability Quantification
In this paper we consider the problem of finding stable maxima of expens...
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02/12/2019
Multi-objective Bayesian optimisation with preferences over objectives
We present a Bayesian multi-objective optimisation algorithm that allows...
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02/06/2019
Fast Hyperparameter Tuning using Bayesian Optimization with Directional Derivatives
In this paper we develop a Bayesian optimization based hyperparameter tu...
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11/05/2018
Practical Batch Bayesian Optimization for Less Expensive Functions
Bayesian optimization (BO) and its batch extensions are successful for o...
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09/19/2018
Bayesian functional optimisation with shape prior
Real world experiments are expensive, and thus it is important to reach ...
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05/21/2018