69 citations · 111 across the 27 of their papers we have counts for
11 papers · 1 filter
Shared Latent Structures Enable Unified Backdoor Detection and Mitigation in LLMs
Omar Mahmoud, Aly M. Kassem, Thommen George Karimpanal +4
Backdoor attacks in large language models (LLMs) are often treated as isolated trigger-response failures, motivating defenses tailored to specific triggers or behaviors. We show th…
ASPECT:Analogical Semantic Policy Execution via Language Conditioned Transfer
Ajsal Shereef Palattuparambil, Thommen George Karimpanal, Santu Rana
Reinforcement Learning (RL) agents often struggle to generalize knowledge to new tasks, even those structurally similar to ones they have mastered. Although recent approaches have…
MAGIK: Mapping to Analogous Goals via Imagination-enabled Knowledge Transfer
Ajsal Shereef Palattuparambil, Thommen George Karimpanal, Santu Rana
Humans excel at analogical reasoning - applying knowledge from one task to a related one with minimal relearning. In contrast, reinforcement learning (RL) agents typically require…
Beyond the Known: Decision Making with Counterfactual Reasoning Decision Transformer
Minh Hoang Nguyen, Linh Le Pham Van, Thommen George Karimpanal +2
Decision Transformers (DT) play a crucial role in modern reinforcement learning, leveraging offline datasets to achieve impressive results across various domains. However, DT requi…
Dynamic Policy Fusion for User Alignment Without Re-Interaction
Ajsal Shereef Palattuparambil, Thommen George Karimpanal, Santu Rana
Deep reinforcement learning (RL) policies, although optimal in terms of task rewards, may not align with the personal preferences of human users. To ensure this alignment, a naive…
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…
Neuro-evolutionary Frameworks for Generalized Learning Agents
Thommen George Karimpanal
The recent successes of deep learning and deep reinforcement learning have firmly established their statuses as state-of-the-art artificial learning techniques. However, longstandi…
Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning
Thommen George Karimpanal, Santu Rana, Sunil Gupta +2
Prior access to domain knowledge could significantly improve the performance of a reinforcement learning agent. In particular, it could help agents avoid potentially catastrophic e…
Self-Organizing Maps for Storage and Transfer of Knowledge in Reinforcement Learning
Thommen George Karimpanal, Roland Bouffanais
The idea of reusing or transferring information from previously learned tasks (source tasks) for the learning of new tasks (target tasks) has the potential to significantly improve…
Identification and Off-Policy Learning of Multiple Objectives Using Adaptive Clustering
Thommen George Karimpanal, Erik Wilhelm
In this work, we present a methodology that enables an agent to make efficient use of its exploratory actions by autonomously identifying possible objectives in its environment and…
Experience Replay Using Transition Sequences
Thommen George Karimpanal, Roland Bouffanais
Experience replay is one of the most commonly used approaches to improve the sample efficiency of reinforcement learning algorithms. In this work, we propose an approach to select…