10 papers
Bootstrap Flow-Map Tree Sampling Enables Online Feedback Driven Search
Binglin Ji, Anindya Sarkar, Hengchang Lu +2
In many scientific and engineering domains, maximizing discovery within a limited sampling budget demands strategic, observation-guided exploration. While generative models have en…
Sequentially-Controlled Interactive Multi-Particle Flow-Maps for Online Feedback-Driven Search
Binglin Ji, Anindya Sarkar, Hengchang Lu +2
While generative models have enabled training-free reward alignment, current methods typically excel in local exploration within narrow regions of the underlying distribution. Thes…
PAPA: Online Personalized Active Preference Alignment
Anindya Sarkar, Nasik Muhammad Nafi, Isaac Lyngaas +2
Diffusion models are highly effective at modeling complex data distributions, including images and text. However, in applications like personalized recommender systems, the objecti…
Adapting Actively on the Fly: Relevance-Guided Online Meta-Learning with Latent Concepts for Geospatial Discovery
Jowaria Khan, Anindya Sarkar, Yevgeniy Vorobeychik +1
In environmental monitoring, data collection is often costly, sparse, and shaped by urgent public-health needs. This is particularly true for cancer-causing PFAS (Per- and polyfluo…
DiffVAS: Diffusion-Guided Visual Active Search in Partially Observable Environments
Anindya Sarkar, Srikumar Sastry, Aleksis Pirinen +2
Visual active search (VAS) has been introduced as a modeling framework that leverages visual cues to direct aerial (e.g., UAV-based) exploration and pinpoint areas of interest with…
Active Target Discovery under Uninformative Prior: The Power of Permanent and Transient Memory
Anindya Sarkar, Binglin Ji, Yevgeniy Vorobeychik
In many scientific and engineering fields, where acquiring high-quality data is expensive--such as medical imaging, environmental monitoring, and remote sensing--strategic sampling…