10 citations · 16 across the 5 of their papers we have counts for
5 papers · 1 filter
When Repeated Measurements Hurt Tabular Foundation Models
Yixiang Liu, Ruizhe Deng, Bibhas Chakraborty +2
Tabular foundation models (TFMs) achieve state-of-the-art performance on IID benchmarks, but how do they respond to repeated measurements at the same inputs? Across six TFMs, synth…
PFN-TS: Thompson Sampling for Contextual Bandits via Prior-Data Fitted Networks
Yan Shuo Tan, Kenyon Ng, Ruizhe Deng +3
Thompson sampling is a widely used strategy for contextual bandits: at each round, it samples a reward function from a Bayesian posterior and acts greedily under that sample. Prior…
Artificial Intelligence-based Decision Support Systems for Precision and Digital Health
Nina Deliu, Bibhas Chakraborty
Precision health, increasingly supported by digital technologies, is a domain of research that broadens the paradigm of precision medicine, advancing everyday healthcare. This visi…
Skew-Probabilistic Neural Networks for Learning from Imbalanced Data
Shraddha M. Naik, Tanujit Chakraborty, Madhurima Panja +2
Real-world datasets often exhibit imbalanced data distribution, where certain class levels are severely underrepresented. In such cases, traditional pattern classifiers have shown…
Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study
Xueqing Liu, Nina Deliu, Tanujit Chakraborty +2
Mobile health (mHealth) interventions often aim to improve distal outcomes, such as clinical conditions, by optimizing proximal outcomes through just-in-time adaptive interventions…