37 citations · 41 across the 17 of their papers we have counts for
12 papers · 1 filter
Bootstrap Stochastic Interpolant Tree for Inference-time Search Under Sparse Reward
Binglin Ji, Anindya Sarkar, Yevgeniy Vorobeychik
Tree-based samplers offer a compelling alternative for scalable inference-time search, bypassing the per-step reward and gradient dependencies that introduce systematic bias into S…
LATS: Levy Adaptive Tree Sampling for Feedback-Driven Diverse Target Discovery
Binglin Ji, Anindya Sarkar, Hengchang Lu +3
While diffusion models excel at capturing complex data distributions, scientific discovery often requires steering generation toward specific, uncharacterized regions that maximize…
BFMT: Enhancing Search Capabilities of Tree Sampler via Bootstrap Flow-Map Tree
Binglin Ji, Anindya Sarkar, Hengchang Lu +4
The ability to efficiently navigate high-dimensional spaces to identify optimal candidates-whether designing targeted molecular structures or generating images with preferred attri…
Sampling Meets Interaction: Sequentially-Controlled Multi-Particle Flow-Maps for Efficient Inference-time Search
Binglin Ji, Anindya Sarkar, Hengchang Lu +2
While generative models have enabled training-free reward alignment, existing particle-based methods are fundamentally constrained by their propensity to local exploration within n…
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…
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…
Learning Policy Committees for Effective Personalization in MDPs with Diverse Tasks
Luise Ge, Michael Lanier, Anindya Sarkar +3
Many dynamic decision problems, such as robotic control, involve a series of tasks, many of which are unknown at training time. Typical approaches for these problems, such as multi…
Active Geospatial Search for Efficient Tenant Eviction Outreach
Anindya Sarkar, Alex DiChristofano, Sanmay Das +3
Tenant evictions threaten housing stability and are a major concern for many cities. An open question concerns whether data-driven methods enhance outreach programs that target at-…
Amortized nonmyopic active search via deep imitation learning
Quan Nguyen, Anindya Sarkar, Roman Garnett
Active search formalizes a specialized active learning setting where the goal is to collect members of a rare, valuable class. The state-of-the-art algorithm approximates the optim…
Reward Delay Attacks on Deep Reinforcement Learning
Anindya Sarkar, Jiarui Feng, Yevgeniy Vorobeychik +2
Most reinforcement learning algorithms implicitly assume strong synchrony. We present novel attacks targeting Q-learning that exploit a vulnerability entailed by this assumption by…
How Powerful are K-hop Message Passing Graph Neural Networks
Jiarui Feng, Yixin Chen, Fuhai Li +2
The most popular design paradigm for Graph Neural Networks (GNNs) is 1-hop message passing -- aggregating information from 1-hop neighbors repeatedly. However, the expressive power…
A Framework for Learning Ante-hoc Explainable Models via Concepts
Anirban Sarkar, Deepak Vijaykeerthy, Anindya Sarkar +1
Self-explaining deep models are designed to learn the latent concept-based explanations implicitly during training, which eliminates the requirement of any post-hoc explanation gen…