Tal Schuster

PhD Student

CSAIL, MIT

Biography

I'm a Ph.D. student at MIT CSAIL and a member of the NLP and Learn To Cure groups. I'm fortunate to be advised by Prof. Regina Barzilay.

Currently, I focus on developing ML and NLP methods to improve the robustness of models, prevent undesired outcomes, and increase their performance in challenging and realistic scenarios.
I have also worked on developing deep learning screening-based methods to improve cancer detection and risk assessment.

Before coming to MIT, I was an MSc student in the Computer Science Dep. at Tel-Aviv University, advised by Prof. Lior Wolf.

Interests

Machine Learning
Natural Language Processing
Computer Vision

Education

PhD in Computer Science, in progress

Massachusetts Institute of Technology
MSc in Computer Science, 2017

Tel Aviv University
BSc in Mathematics and Computer Science

Ben-Gurion University

Projects

Conformal Prediction

Predicting with confidence estimates

Word Embeddings

Dense representations of words

Fake News

Misinformation detection

Medical

Deep learning for cancer risk prediction

Metric Learning

Deep metric learning

Recent Publications

Click here to see all

Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence

Typical fact verification models use retrieved written evidence to verify claims. Evidence sources, however, often change over time as …

Tal Schuster, Adam Fisch, Regina Barzilay

March 2021 In North American Chapter of the Association for Computational Linguistics (NAACL)

PDF Code Dataset Project News article

Efficient Conformal Prediction via Cascaded Inference with Expanded Admission

In this paper, we present a novel approach for conformal prediction (CP), in which we aim to identify a set of promising prediction …

Adam Fisch*, Tal Schuster*, Tommi Jaakkola, Regina Barzilay

January 2021 In International Conference on Learning Representations (ICLR)

PDF Code Project Project

The Limitations of Stylometry for Detecting Machine-Generated Fake News

Recent developments in neural language models (LMs) have raised concerns about their potential misuse for automatically spreading …

Tal Schuster, Roei Schuster, Darsh J Shah, Regina Barzilay

February 2020 In Computational Linguistics

PDF Dataset Project News article

Towards Debiasing Fact Verification Models

Fact verification requires validating a claim in the context of evidence. We show, however, that in the popular FEVER dataset this …

Tal Schuster*, Darsh J Shah*, Yun Jie Serene Yeo, Daniel Filizzola, Enrico Santus, Regina Barzilay

August 2019 In Empirical Methods in Natural Language Processing (EMNLP)

PDF Code Project Slides Video News article

Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing

We introduce a novel method for multilingual transfer that utilizes deep contextual embeddings, pretrained in an unsupervised fashion. …

Tal Schuster*, Ori Ram*, Regina Barzilay, Amir Globerson

June 2019 In North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL)

PDF Code Project Slides Video

More Publications

Few-shot Conformal Prediction with Auxiliary Tasks
Adam Fisch, Tal Schuster, Tommi Jaakkola, Regina Barzilay.
2021.

PDF Project

Distilling the Evidence to Augment Fact Verification Models
Beatrice Portelli, Jason Zhao, Tal Schuster, Giuseppe Serra, Enrico Santus.
In Workshop on Fact Extraction and VERification (FEVER workshop at ACL), 2020.

PDF Project

Humpty Dumpty: Controlling Word Meanings via Corpus Poisoning
Roei Schuster, Tal Schuster, Yoav Meri, Vitaly Shmatikov.
In IEEE Symposium on Security and Privacy (IEEE S&P), 2020.

PDF Project Video

Automatic Fact-guided Sentence Modification
Darsh J Shah*, Tal Schuster*, Regina Barzilay.
In AAAI Conference on Artificial Intelligence (AAAI), 2019.

PDF Code Project News article

A Deep Learning Model to Triage Screening Mammograms: A Simulation Study
Adam Yala, Tal Schuster, Randy Miles, Regina Barzilay, Constance Lehman.
In Radiological Society of North America, 2019.

PDF Code Project

A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction
Adam Yala, Constance Lehman, Tal Schuster, Tally Portnoi, Regina Barzilay.
In Radiological Society of North America, 2019.

PDF Code Project News article

See all publications