Search Results for author: Sergey Feldman

Found 12 papers, 9 papers with code

SPECTER: Document-level Representation Learning using Citation-informed Transformers

5 code implementations ACL 2020 Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, Daniel S. Weld

We propose SPECTER, a new method to generate document-level embedding of scientific documents based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph.

Citation Prediction Document Classification +4

ABNIRML: Analyzing the Behavior of Neural IR Models

2 code implementations2 Nov 2020 Sean MacAvaney, Sergey Feldman, Nazli Goharian, Doug Downey, Arman Cohan

Pretrained contextualized language models such as BERT and T5 have established a new state-of-the-art for ad-hoc search.

Language Modelling Sentence

SciRepEval: A Multi-Format Benchmark for Scientific Document Representations

2 code implementations23 Nov 2022 Amanpreet Singh, Mike D'Arcy, Arman Cohan, Doug Downey, Sergey Feldman

In response, we introduce SciRepEval, the first comprehensive benchmark for training and evaluating scientific document representations.

Literature-Augmented Clinical Outcome Prediction

1 code implementation Findings (NAACL) 2022 Aakanksha Naik, Sravanthi Parasa, Sergey Feldman, Lucy Lu Wang, Tom Hope

We present BEEP (Biomedical Evidence-Enhanced Predictions), a novel approach for clinical outcome prediction that retrieves patient-specific medical literature and incorporates it into predictive models.

Decision Making

S2abEL: A Dataset for Entity Linking from Scientific Tables

1 code implementation30 Apr 2023 Yuze Lou, Bailey Kuehl, Erin Bransom, Sergey Feldman, Aakanksha Naik, Doug Downey

Entity linking (EL) is the task of linking a textual mention to its corresponding entry in a knowledge base, and is critical for many knowledge-intensive NLP applications.

Entity Linking Question Answering

RCT Rejection Sampling for Causal Estimation Evaluation

1 code implementation27 Jul 2023 Katherine A. Keith, Sergey Feldman, David Jurgens, Jonathan Bragg, Rohit Bhattacharya

We contribute a new sampling algorithm, which we call RCT rejection sampling, and provide theoretical guarantees that causal identification holds in the observational data to allow for valid comparisons to the ground-truth RCT.

Causal Identification

Multi-Task Averaging

no code implementations NeurIPS 2012 Sergey Feldman, Maya Gupta, Bela Frigyik

We present a multi-task learning approach to jointly estimate the means of multiple independent data sets.

Multi-Task Learning

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