12 citations · 67 across the 68 of their papers we have counts for
10 papers · 1 filter
Prediction and Control in Continual Reinforcement Learning
Nishanth Anand, Doina Precup
Temporal difference (TD) learning is often used to update the estimate of the value function which is used by RL agents to extract useful policies. In this paper, we focus on value…
Nash Learning from Human Feedback
Rémi Munos, Michal Valko, Daniele Calandriello +14
Reinforcement learning from human feedback (RLHF) has emerged as the main paradigm for aligning large language models (LLMs) with human preferences. Typically, RLHF involves the in…
Learning domain-invariant classifiers for infant cry sounds
Charles C. Onu, Hemanth K. Sheetha, Arsenii Gorin +1
The issue of domain shift remains a problematic phenomenon in most real-world datasets and clinical audio is no exception. In this work, we study the nature of domain shift in a cl…
DGFN: Double Generative Flow Networks
Elaine Lau, Nikhil Vemgal, Doina Precup +1
Deep learning is emerging as an effective tool in drug discovery, with potential applications in both predictive and generative models. Generative Flow Networks (GFlowNets/GFNs) ar…
A cry for help: Early detection of brain injury in newborns
Charles C. Onu, Samantha Latremouille, Arsenii Gorin +9
Since the 1960s, neonatal clinicians have known that newborns suffering from certain neurological conditions exhibit altered crying patterns such as the high-pitched cry in birth a…
Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search
Abbas Mehrabian, Ankit Anand, Hyunjik Kim +16
This work studies a central extremal graph theory problem inspired by a 1975 conjecture of Erdős, which aims to find graphs with a given size (number of nodes) that maximize the nu…