7 citations · 11 across the 11 of their papers we have counts for
12 papers
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,…
Improving realistic semi-supervised learning with doubly robust estimation
Khiem Pham, Charles Herrmann, Ramin Zabih
A major challenge in Semi-Supervised Learning (SSL) is the limited information available about the class distribution in the unlabeled data. In many real-world applications this ar…
DIRI: Adversarial Patient Reidentification with Large Language Models for Evaluating Clinical Text Anonymization
John X. Morris, Thomas R. Campion, Sri Laasya Nutheti +4
Sharing protected health information (PHI) is critical for furthering biomedical research. Before data can be distributed, practitioners often perform deidentification to remove an…
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…
DreamWalk: Style Space Exploration using Diffusion Guidance
Michelle Shu, Charles Herrmann, Richard Strong Bowen +2
Text-conditioned diffusion models can generate impressive images, but fall short when it comes to fine-grained control. Unlike direct-editing tools like Photoshop, text conditioned…
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…
Test-Time Distribution Normalization for Contrastively Learned Vision-language Models
Yifei Zhou, Juntao Ren, Fengyu Li +2
Advances in the field of vision-language contrastive learning have made it possible for many downstream applications to be carried out efficiently and accurately by simply taking t…
Unsupervised Text Deidentification
John X. Morris, Justin T. Chiu, Ramin Zabih +1
Deidentification seeks to anonymize textual data prior to distribution. Automatic deidentification primarily uses supervised named entity recognition from human-labeled data points…