Zobrazeno 1 - 10
of 20 522
pro vyhledávání: '"A Aarti"'
Estimation of the Average Treatment Effect (ATE) is a core problem in causal inference with strong connections to Off-Policy Evaluation in Reinforcement Learning. This paper considers the problem of adaptively selecting the treatment allocation proba
Externí odkaz:
http://arxiv.org/abs/2411.14341
Sleep staging is a challenging task, typically manually performed by sleep technologists based on electroencephalogram and other biosignals of patients taken during overnight sleep studies. Recent work aims to leverage automated algorithms to perform
Externí odkaz:
http://arxiv.org/abs/2411.07964
Autor:
Grossman, Robert L., Boyd, Ceilyn, Do, Nhan, Elbers, Danne C., Fitzsimons, Michael S., Giger, Maryellen L., Juehne, Anthony, Larrick, Brienna, Lee, Jerry S. H., Lin, Dawei, Lukowski, Michael, Myers, James D., Schumm, L. Philip, Venkat, Aarti
Over the past few years, a growing number of data platforms have emerged, including data commons, data repositories, and databases containing biomedical, environmental, social determinants of health and other data relevant to improving health outcome
Externí odkaz:
http://arxiv.org/abs/2411.05248
Randomized controlled trials (RCTs) can be used to generate guarantees on treatment effects. However, RCTs often spend unnecessary resources exploring sub-optimal treatments, which can reduce the power of treatment guarantees. To address these concer
Externí odkaz:
http://arxiv.org/abs/2410.11212
Autor:
Gautam, Aarti, Mishra, Prabuddha Kant, Banerjee, Souvik, Sundaresan, A., Ganguli, Ashok Kumar
We report the detailed investigation of the magnetic, transport, and magnetocaloric effects of GdSbSe by magnetic susceptibility $\chi(T)$, isothermal magnetization $M(H)$, resistivity $\rho(T, H)$, and heat capacity $C_p(T)$ measurements, crystalliz
Externí odkaz:
http://arxiv.org/abs/2409.13539
The traditional viewpoint on Sparse Mixture of Experts (MoE) models is that instead of training a single large expert, which is computationally expensive, we can train many small experts. The hope is that if the total parameter count of the small exp
Externí odkaz:
http://arxiv.org/abs/2409.00879
In the era of evolving artificial intelligence, machines are increasingly emulating human-like capabilities, including visual perception and linguistic expression. Image captioning stands at the intersection of these domains, enabling machines to int
Externí odkaz:
http://arxiv.org/abs/2408.15714
We consider the hybrid reinforcement learning setting where the agent has access to both offline data and online interactive access. While Reinforcement Learning (RL) research typically assumes offline data contains complete action, reward and transi
Externí odkaz:
http://arxiv.org/abs/2406.07253
Learning from human preference data has emerged as the dominant paradigm for fine-tuning large language models (LLMs). The two most common families of techniques -- online reinforcement learning (RL) such as Proximal Policy Optimization (PPO) and off
Externí odkaz:
http://arxiv.org/abs/2406.01462
Is it possible to understand or imitate a policy maker's rationale by looking at past decisions they made? We formalize this question as the problem of learning social welfare functions belonging to the well-studied family of power mean functions. We
Externí odkaz:
http://arxiv.org/abs/2405.17700