3.2k citations · 3.2k across the 14 of their papers we have counts for
15 papers
Verb-ICL: Rethinking In-Context Learning for Structured Prediction
Fan Bai, Hengshuo Miao, Sanjit S Batra +3
Structured prediction tasks pose unique challenges for in-context learning (ICL): their compositional outputs require modeling fine-grained, token-level patterns that sentence-leve…
MedFabric: Gold Evidence Hides the Difficulty of Word-Level Medical Fabrication Detection
Tung Sum Thomas Kwok, Qian Qian, Xiaofeng Lin +8
Large language models fabricate in medicine, producing fluent statements that are factually wrong, so reliable fabrication detection is a prerequisite for clinical deployment. Repo…
MedicalBench: Evaluating Large Language Models Toward Improved Medical Concept Extraction
Zhichao Yang, Gregory D. Lyng, Sanjit Singh Batra +1
Medical concept extraction from electronic health records underpins many downstream applications, yet remains challenging because medically meaningful concepts are frequently impli…
Imputation of Unknown Missingness in Sparse Electronic Health Records
Jun Han, Josue Nassar, Sanjit Singh Batra +3
Machine learning holds great promise for advancing the field of medicine, with electronic health records (EHRs) serving as a primary data source. However, EHRs are often sparse and…
Fast and Effective On-policy Distillation from Reasoning Prefixes
Dongxu Zhang, Zhichao Yang, Sepehr Janghorbani +6
On-policy distillation (OPD), which samples trajectories from the student model and supervises them with a teacher at the token level, avoids relying solely on verifiable terminal…
ESTAR: Early-Stopping Token-Aware Reasoning For Efficient Inference
Junda Wang, Zhichao Yang, Dongxu Zhang +2
Large reasoning models (LRMs) achieve state-of-the-art performance by generating long chains-of-thought, but often waste computation on redundant reasoning after the correct answer…
POET: Protocol Optimization via Eligibility Tuning
Trisha Das, Katherine Kero, Dorinda Schumann +4
Eligibility criteria (EC) are essential for clinical trial design, yet drafting them remains a time-intensive and cognitively demanding task for clinicians. Existing automated appr…
Health-SCORE: Towards Scalable Rubrics for Improving Health-LLMs
Zhichao Yang, Sepehr Janghorbani, Dongxu Zhang +6
Rubrics are essential for evaluating open-ended LLM responses, especially in safety-critical domains such as healthcare. However, creating high-quality and domain-specific rubrics…
XAIQA: Explainer-Based Data Augmentation for Extractive Question Answering
Joel Stremmel, Ardavan Saeedi, Hamid Hassanzadeh +4
Extractive question answering (QA) systems can enable physicians and researchers to query medical records, a foundational capability for designing clinical studies and understandin…
Surpassing GPT-4 Medical Coding with a Two-Stage Approach
Zhichao Yang, Sanjit Singh Batra, Joel Stremmel +1
Recent advances in large language models (LLMs) show potential for clinical applications, such as clinical decision support and trial recommendations. However, the GPT-4 LLM predic…
An end-to-end assembly of the Aedes aegypti genome
Daisy E. Pagete
We present an end-to-end genome assembly of a female Aedes aegypti mosquito, which spreads viral diseases such as yellow fever, dengue, chikungunya, and Zika to humans. The assembl…
Feature Selection for classification of hyperspectral data by minimizing a tight bound on the VC dimension
Phool Preet, Sanjit Singh Batra, Jayadeva
Hyperspectral data consists of large number of features which require sophisticated analysis to be extracted. A popular approach to reduce computational cost, facilitate informatio…
Learning a Fuzzy Hyperplane Fat Margin Classifier with Minimum VC dimension
Jayadeva, Sanjit Singh Batra, Siddarth Sabharwal
The Vapnik-Chervonenkis (VC) dimension measures the complexity of a learning machine, and a low VC dimension leads to good generalization. The recently proposed Minimal Complexity…
Feature Selection through Minimization of the VC dimension
Jayadeva, Sanjit S. Batra, Siddharth Sabharwal
Feature selection involes identifying the most relevant subset of input features, with a view to improving generalization of predictive models by reducing overfitting. Directly sea…
Learning a hyperplane regressor by minimizing an exact bound on the VC dimension
Jayadeva, Suresh Chandra, Siddarth Sabharwal +1
The capacity of a learning machine is measured by its Vapnik-Chervonenkis dimension, and learning machines with a low VC dimension generalize better. It is well known that the VC d…