10 citations · 24 across the 29 of their papers we have counts for
6 papers · 1 filter
Human-Allied Relational Reinforcement Learning
Fateme Golivand Darvishvand, Hikaru Shindo, Sahil Sidheekh +2
Reinforcement learning (RL) has experienced a second wind in the past decade. While incredibly successful in images and videos, these systems still operate within the realm of prop…
IndiCASA: A Dataset and Bias Evaluation Framework in LLMs Using Contrastive Embedding Similarity in the Indian Context
Santhosh G S, Akshay Govind S, Gokul S Krishnan +2
Large Language Models (LLMs) have gained significant traction across critical domains owing to their impressive contextual understanding and generative capabilities. However, their…
mFARM: Towards Multi-Faceted Fairness Assessment based on HARMs in Clinical Decision Support
Shreyash Adappanavar, Krithi Shailya, Gokul S Krishnan +2
The deployment of Large Language Models (LLMs) in high-stakes medical settings poses a critical AI alignment challenge, as models can inherit and amplify societal biases, leading t…
Tractable Sharpness-Aware Learning of Probabilistic Circuits
Hrithik Suresh, Sahil Sidheekh, Vishnu Shreeram M. P +2
Probabilistic Circuits (PCs) are a class of generative models that allow exact and tractable inference for a wide range of queries. While recent developments have enabled the learn…
Tractable Representation Learning with Probabilistic Circuits
Steven Braun, Sahil Sidheekh, Antonio Vergari +3
Probabilistic circuits (PCs) are powerful probabilistic models that enable exact and tractable inference, making them highly suitable for probabilistic reasoning and inference task…
Combining Planning and Reinforcement Learning for Solving Relational Multiagent Domains
Nikhilesh Prabhakar, Ranveer Singh, Harsha Kokel +2
Multiagent Reinforcement Learning (MARL) poses significant challenges due to the exponential growth of state and action spaces and the non-stationary nature of multiagent environme…