Michael Wick

Michael Wick

University of Massachusetts Amherst

H-index: 23

North America-United States

About Michael Wick

Michael Wick, With an exceptional h-index of 23 and a recent h-index of 13 (since 2020), a distinguished researcher at University of Massachusetts Amherst, specializes in the field of machine learning, large graphical models, information extraction, knowledge base construction, FACTORIE.

His recent articles reflect a diverse array of research interests and contributions to the field:

Control system for learning to rank fairness

Similarity analysis using enhanced MinHash

Enforcing Fairness on Unlabeled Data to Improve Modeling Performance

Entropy-based anti-modeling for machine learning applications

Differentiable set to increase the memory capacity of recurrent neural net works

Providing Fairness in Fine-Tuning of Pre-Trained Language Models

Augmenting data sets for machine learning models

Augmenting data sets for selecting machine learning models

Michael Wick Information

University

Position

___

Citations(all)

1675

Citations(since 2020)

644

Cited By

1279

hIndex(all)

23

hIndex(since 2020)

13

i10Index(all)

30

i10Index(since 2020)

20

Email

University Profile Page

University of Massachusetts Amherst

Google Scholar

View Google Scholar Profile

Michael Wick Skills & Research Interests

machine learning

large graphical models

information extraction

knowledge base construction

FACTORIE

Top articles of Michael Wick

Title

Journal

Author(s)

Publication Date

Control system for learning to rank fairness

2024/4/2

Similarity analysis using enhanced MinHash

2024/3/5

Enforcing Fairness on Unlabeled Data to Improve Modeling Performance

2023/12/7

Entropy-based anti-modeling for machine learning applications

2023/11/16

Differentiable set to increase the memory capacity of recurrent neural net works

2023/4/25

Providing Fairness in Fine-Tuning of Pre-Trained Language Models

2023/12/21

Augmenting data sets for machine learning models

2023/2/2

Augmenting data sets for selecting machine learning models

2023/12/14

Guided augmention of data sets for machine learning models

2023/12/14

Online Post-Processing In Rankings For Constrained Utility Maximization

2022/2/17

Don’t just clean it, proxy clean it: Mitigating bias by proxy in pre-trained models

Swetasudha Panda

Ari Kobren

Michael Wick

Qinlan Shen

2022/12

Evaluating language models using negative data

2022/11/1

Debiasing Pre-trained Sentence Encoders With Probabilistic Dropouts

2022/8/4

Upstream mitigation is not all you need: Testing the bias transfer hypothesis in pre-trained language models

Ryan Steed

Swetasudha Panda

Ari Kobren

Michael Wick

2022/5

Ensembled decision systems using feature hashing models

2022/3/1

When output units must obey hard constraints

2022/12/6

Character-based attribute value extraction system

2021/5/18

Online post-processing in rankings for fair utility maximization

Ananya Gupta

Eric Johnson

Justin Payan

Aditya Kumar Roy

Ari Kobren

...

2021/3/8

Removing undesirable signals from language models using negative data

2021/12/2

Enhanced Techniques For Bias Analysis

2021/12/2

See List of Professors in Michael Wick University(University of Massachusetts Amherst)