25 citations · 153 across the 58 of their papers we have counts for
63 papers
Robust Nearest Neighbour Retrieval Using Targeted Manifold Manipulation
B. Ghosh, H. Harikumar, S. Rana
Nearest-neighbour retrieval is central to classification and explainable-AI pipelines, but current practice relies on hand-tuning feature layers and distance metrics. We propose Ta…
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…
Composite Concept Extraction through Backdooring
Banibrata Ghosh, Haripriya Harikumar, Khoa D Doan +2
Learning composite concepts, such as \textquotedbl red car\textquotedbl , from individual examples -- like a white car representing the concept of \textquotedbl car\textquotedbl{}…
Novel Kernel Models and Exact Representor Theory for Neural Networks Beyond the Over-Parameterized Regime
Alistair Shilton, Sunil Gupta, Santu Rana +1
This paper presents two models of neural-networks and their training applicable to neural networks of arbitrary width, depth and topology, assuming only finite-energy neural activa…
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…
ECoDe: A Sample-Efficient Method for Co-Design of Robotic Agents
Kishan R. Nagiredla, Buddhika L. Semage, Arun Kumar A. +2
Co-designing autonomous robotic agents involves simultaneously optimizing the controller and physical design of the agent. Its inherent bi-level optimization formulation necessitat…
LaGR-SEQ: Language-Guided Reinforcement Learning with Sample-Efficient Querying
Thommen George Karimpanal, Laknath Buddhika Semage, Santu Rana +4
Large language models (LLMs) have recently demonstrated their impressive ability to provide context-aware responses via text. This ability could potentially be used to predict plau…
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…
EMOTE: An Explainable architecture for Modelling the Other Through Empathy
Manisha Senadeera, Thommen Karimpanal George, Sunil Gupta +2
We can usually assume others have goals analogous to our own. This assumption can also, at times, be applied to multi-agent games - e.g. Agent 1's attraction to green pellets is an…
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…
Gradient Descent in Neural Networks as Sequential Learning in RKBS
Alistair Shilton, Sunil Gupta, Santu Rana +1
The study of Neural Tangent Kernels (NTKs) has provided much needed insight into convergence and generalization properties of neural networks in the over-parametrized (wide) limit…
Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation
Kien Do, Hung Le, Dung Nguyen +5
Data-free Knowledge Distillation (DFKD) has attracted attention recently thanks to its appealing capability of transferring knowledge from a teacher network to a student network wi…
Defense Against Multi-target Trojan Attacks
Haripriya Harikumar, Santu Rana, Kien Do +4
Adversarial attacks on deep learning-based models pose a significant threat to the current AI infrastructure. Among them, Trojan attacks are the hardest to defend against. In this…
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…
Towards Effective and Robust Neural Trojan Defenses via Input Filtering
Kien Do, Haripriya Harikumar, Hung Le +6
Trojan attacks on deep neural networks are both dangerous and surreptitious. Over the past few years, Trojan attacks have advanced from using only a single input-agnostic trigger a…