25 citations · 153 across the 58 of their papers we have counts for
35 papers · 1 filter
TRUST: Test-time Resource Utilization for Superior Trustworthiness
Haripriya Harikumar, Santu Rana
Standard uncertainty estimation techniques, such as dropout, often struggle to clearly distinguish reliable predictions from unreliable ones. We attribute this limitation to noisy…
Efficient Symmetry-Aware Materials Generation via Hierarchical Generative Flow Networks
Tri Minh Nguyen, Sherif Abdulkader Tawfik, Truyen Tran +3
Discovering new solid-state materials requires rapidly exploring the vast space of crystal structures and locating stable regions. Generating stable materials with desired properti…
Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties
Arun Kumar A, Alistair Shilton, Sunil Gupta +3
Experimental (design) optimization is a key driver in designing and discovering new products and processes. Bayesian Optimization (BO) is an effective tool for optimizing expensive…
Revisiting the Dataset Bias Problem from a Statistical Perspective
Kien Do, Dung Nguyen, Hung Le +6
In this paper, we study the "dataset bias" problem from a statistical standpoint, and identify the main cause of the problem as the strong correlation between a class attribute u a…
Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection
Tuan Hoang, Santu Rana, Sunil Gupta +1
Recent data-privacy laws have sparked interest in machine unlearning, which involves removing the effect of specific training samples from a learnt model as if they were never pres…
Predictive Modeling through Hyper-Bayesian Optimization
Manisha Senadeera, Santu Rana, Sunil Gupta +1
Model selection is an integral problem of model based optimization techniques such as Bayesian optimization (BO). Current approaches often treat model selection as an estimation pr…
A Data-Driven Defense against Edge-case Model Poisoning Attacks on Federated Learning
Kiran Purohit, Soumi Das, Sourangshu Bhattacharya +1
Federated Learning systems are increasingly subjected to a multitude of model poisoning attacks from clients. Among these, edge-case attacks that target a small fraction of the inp…
BO-Muse: A human expert and AI teaming framework for accelerated experimental design
Sunil Gupta, Alistair Shilton, Arun Kumar A +7
In this paper we introduce BO-Muse, a new approach to human-AI teaming for the optimization of expensive black-box functions. Inspired by the intrinsic difficulty of extracting exp…
Controlled Diversity with Preference : Towards Learning a Diverse Set of Desired Skills
Maxence Hussonnois, Thommen George Karimpanal, Santu Rana
Autonomously learning diverse behaviors without an extrinsic reward signal has been a problem of interest in reinforcement learning. However, the nature of learning in such mechani…
Zero-shot Sim2Real Adaptation Across Environments
Buddhika Laknath Semage, Thommen George Karimpanal, Santu Rana +1
Simulation based learning often provides a cost-efficient recourse to reinforcement learning applications in robotics. However, simulators are generally incapable of accurately rep…
Fast Conditional Network Compression Using Bayesian HyperNetworks
Phuoc Nguyen, Truyen Tran, Ky Le +6
We introduce a conditional compression problem and propose a fast framework for tackling it. The problem is how to quickly compress a pretrained large neural network into optimal s…
Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization
Hung Tran-The, Sunil Gupta, Santu Rana +1
The expected improvement (EI) algorithm is one of the most popular strategies for optimization under uncertainty due to its simplicity and efficiency. Despite its popularity, the t…
Uncertainty Aware System Identification with Universal Policies
Buddhika Laknath Semage, Thommen George Karimpanal, Santu Rana +1
Sim2real transfer is primarily concerned with transferring policies trained in simulation to potentially noisy real world environments. A common problem associated with sim2real tr…
Fast Model-based Policy Search for Universal Policy Networks
Buddhika Laknath Semage, Thommen George Karimpanal, Santu Rana +1
Adapting an agent's behaviour to new environments has been one of the primary focus areas of physics based reinforcement learning. Although recent approaches such as universal poli…
Balanced Q-learning: Combining the Influence of Optimistic and Pessimistic Targets
Thommen George Karimpanal, Hung Le, Majid Abdolshah +4
The optimistic nature of the Q-learning target leads to an overestimation bias, which is an inherent problem associated with standard learning. Such a bias fails to account for…
Plug and Play, Model-Based Reinforcement Learning
Majid Abdolshah, Hung Le, Thommen Karimpanal George +3
Sample-efficient generalisation of reinforcement learning approaches have always been a challenge, especially, for complex scenes with many components. In this work, we introduce P…
A New Representation of Successor Features for Transfer across Dissimilar Environments
Majid Abdolshah, Hung Le, Thommen Karimpanal George +3
Transfer in reinforcement learning is usually achieved through generalisation across tasks. Whilst many studies have investigated transferring knowledge when the reward function ch…
Bayesian Optimistic Optimisation with Exponentially Decaying Regret
Hung Tran-The, Sunil Gupta, Santu Rana +1
Bayesian optimisation (BO) is a well-known efficient algorithm for finding the global optimum of expensive, black-box functions. The current practical BO algorithms have regret bou…
ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms
Huong Ha, Sunil Gupta, Santu Rana +1
Machine learning models are being used extensively in many important areas, but there is no guarantee a model will always perform well or as its developers intended. Understanding…
Intuitive Physics Guided Exploration for Sample Efficient Sim2real Transfer
Buddhika Laknath Semage, Thommen George Karimpanal, Santu Rana +1
Physics-based reinforcement learning tasks can benefit from simplified physics simulators as they potentially allow near-optimal policies to be learned in simulation. However, such…
Unsupervised Anomaly Detection on Temporal Multiway Data
Duc Nguyen, Phuoc Nguyen, Kien Do +3
Temporal anomaly detection looks for irregularities over space-time. Unsupervised temporal models employed thus far typically work on sequences of feature vectors, and much less on…
Sequential Subspace Search for Functional Bayesian Optimization Incorporating Experimenter Intuition
Alistair Shilton, Sunil Gupta, Santu Rana +1
We propose an algorithm for Bayesian functional optimisation - that is, finding the function to optimise a process - guided by experimenter beliefs and intuitions regarding the exp…
Bayesian Optimization with Missing Inputs
Phuc Luong, Dang Nguyen, Sunil Gupta +2
Bayesian optimization (BO) is an efficient method for optimizing expensive black-box functions. In real-world applications, BO often faces a major problem of missing values in inpu…
DeepCoDA: personalized interpretability for compositional health data
Thomas P. Quinn, Dang Nguyen, Santu Rana +2
Interpretability allows the domain-expert to directly evaluate the model's relevance and reliability, a practice that offers assurance and builds trust. In the healthcare setting,…