7 citations · 8 across the 7 of their papers we have counts for
9 papers
Reorganizing Graduate Statistical Inference for Modern Biostatistical Practice
Raphael C Kim, Michele Santacatterina
First-year graduate courses in statistical inference within PhD programs in biostatistics often follow a classical mathematical statistics progression through probability models, d…
Statistical Inference for Continuous Action Bandits
Raphael C Kim, Michele Santacatterina, Ramin Zabih +2
Statistical inference under adaptively collected data is becoming increasingly popular across e-commerce and mobile health. Many methods exist for discrete action settings, ranging…
Are Good Generators Good Decision-Makers? Policy Learning for General Interventions via Retargeted Counterfactual Generation
Raphael C Kim, Jingsen Zhu, Ramin Zabih +1
Generative models are increasingly used to support decision-making in complex systems, where interventions may be joint and high-dimensional, and outcomes are high-dimensional. How…
Data-Adaptive and Model-Robust Covariate Adjustment for Time-to-Event Outcomes in Stratified Randomized Trials
Raphael C. Kim, Brian Gilbert, Ramin Zabih +2
Time-to-event outcomes are commonly used as primary endpoints in randomized clinical trials. Despite this, relatively little work incorporates baseline covariate information while…
DeDPO: Debiased Direct Preference Optimization for Diffusion Models
Khiem Pham, Quang Nguyen, Tung Nguyen +4
Direct Preference Optimization (DPO) has emerged as a predominant alignment method for diffusion models, facilitating off-policy training without explicit reward modeling. However,…
Non-parametric efficient estimation of marginal structural models with multi-valued time-varying treatments
Axel Martin, Michele Santacatterina, Iván Díaz
Marginal structural models are a popular method for estimating causal effects in the presence of time-varying exposures. In spite of their popularity, no scalable non-parametric es…
Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models
Ali Behrouz, Michele Santacatterina, Ramin Zabih
Modeling multivariate time series is a well-established problem with a wide range of applications from healthcare to financial markets. Traditional State Space Models (SSMs) are cl…
MambaMixer: Efficient Selective State Space Models with Dual Token and Channel Selection
Ali Behrouz, Michele Santacatterina, Ramin Zabih
Recent advances in deep learning have mainly relied on Transformers due to their data dependency and ability to learn at scale. The attention module in these architectures, however…
A Stable and Efficient Covariate-Balancing Estimator for Causal Survival Effects
Khiem Pham, David A. Hirshberg, Phuong-Mai Huynh-Pham +3
We propose an empirically stable and asymptotically efficient covariate-balancing approach to the problem of estimating survival causal effects in data with conditionally-independe…