Learning Field Compatibilities to Extract Database Records from Unstructured Text. - Michael L. Wick; Aron Culotta; Andrew McCallum (EMNLP, 2006)

Context-Sensitive Error Correction: Using Topic Models to Improve OCR. - Michael L. Wick; Michael G. Ross; Erik G. Learned-Miller (ICDAR, 2007)

Canonicalization of database records using adaptive similarity measures. - Aron Culotta; Michael L. Wick; Robert J. Hall 002; Matthew Marzilli; Andrew McCallum (KDD, 2007)

First-Order Probabilistic Models for Coreference Resolution. - Aron Culotta; Michael L. Wick; Andrew McCallum (HLT-NAACL, 2007)

A unified approach for schema matching, coreference and canonicalization. - Michael L. Wick; Khashayar Rohanimanesh; Karl Schultz; Andrew McCallum (KDD, 2008)

A Corpus for Cross-Document Co-reference. - David Day; Janet Hitzeman; Michael L. Wick; Keith Crouch; Massimo Poesio (LREC, 2008)

A Discriminative Approach to Ontology Mapping. - Michael L. Wick; Khashayar Rohanimanesh; Andrew McCallum; AnHai Doan (NTII, 2008)

Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference. - Michael L. Wick; Khashayar Rohanimanesh; Sameer Singh 001; Andrew McCallum (NIPS, 2009)

An Entity Based Model for Coreference Resolution. - Michael L. Wick; Aron Culotta; Khashayar Rohanimanesh; Andrew McCallum (SDM, 2009)

Distantly Labeling Data for Large Scale Cross-Document Coreference - Sameer Singh 001; Michael L. Wick; Andrew McCallum (CoRR, 2010)

Scalable Probabilistic Databases with Factor Graphs and MCMC. - Michael L. Wick; Andrew McCallum; Gerome Miklau (Proc. VLDB Endow., 2010)

SampleRank: Training Factor Graphs with Atomic Gradients. - Michael L. Wick; Khashayar Rohanimanesh; Kedar Bellare; Aron Culotta; Andrew McCallum (ICML, 2011)

Query-Aware MCMC. - Michael L. Wick; Andrew McCallum (NIPS, 2011)

Hybrid in-database inference for declarative information extraction. - Daisy Zhe Wang; Michael J. Franklin; Minos N. Garofalakis; Joseph M. Hellerstein; Michael L. Wick (SIGMOD Conference, 2011)

A Discriminative Hierarchical Model for Fast Coreference at Large Scale. - Michael L. Wick; Sameer Singh 001; Andrew McCallum (ACL (1), 2012)

Monte Carlo MCMC: Efficient Inference by Approximate Sampling. - Sameer Singh 001; Michael L. Wick; Andrew McCallum (EMNLP-CoNLL, 2012)

Monte Carlo MCMC: Efficient Inference by Sampling Factors. - Sameer Singh 001; Michael L. Wick; Andrew McCallum (AKBC-WEKEX@NAACL-HLT, 2012)

Human-Machine Cooperation: Supporting User Corrections to Automatically Constructed KBs. - Michael L. Wick; Karl Schultz; Andrew McCallum (AKBC-WEKEX@NAACL-HLT, 2012)

Assessing confidence of knowledge base content with an experimental study in entity resolution. - Michael L. Wick; Sameer Singh 001; Ari Kobren; Andrew McCallum (AKBC@CIKM, 2013)

A joint model for discovering and linking entities. - Michael L. Wick; Sameer Singh 001; Harshal Pandya; Andrew McCallum (AKBC@CIKM, 2013)

Minimally-Constrained Multilingual Embeddings via Artificial Code-Switching. - Michael L. Wick; Pallika Kanani; Adam Craig Pocock (AAAI, 2016)

Exponential Stochastic Cellular Automata for Massively Parallel Inference. - Manzil Zaheer; Michael L. Wick; Jean-Baptiste Tristan; Alexander J. Smola; Guy L. Steele Jr. (AISTATS, 2016)

Query-Driven Sampling for Collective Entity Resolution. - Christan Earl Grant; Daisy Zhe Wang; Michael L. Wick (IRI, 2016)

Filling in the details: Perceiving from low fidelity images. - Farahnaz Ahmed Wick; Michael L. Wick; Marc Pomplun (CoRR, 2016)

Enforcing Output Constraints via SGD: A Step Towards Neural Lagrangian Relaxation. - Jay Yoon Lee; Michael L. Wick; Jean-Baptiste Tristan; Jaime G. Carbonell (AKBC@NIPS, 2017)

Enforcing Constraints on Outputs with Unconstrained Inference. - Jay Yoon Lee; Michael L. Wick; Jean-Baptiste Tristan; Jaime G. Carbonell (CoRR, 2017)

Gradient-Based Inference for Networks with Output Constraints. - Jay Yoon Lee; Sanket Vaibhav Mehta; Michael L. Wick; Jean-Baptiste Tristan; Jaime G. Carbonell (AAAI, 2019)

Sketching for Latent Dirichlet-Categorical Models. - Joseph Tassarotti; Jean-Baptiste Tristan; Michael L. Wick (AISTATS, 2019)

Scaling Hierarchical Coreference with Homomorphic Compression. - Michael L. Wick; Swetasudha Panda; Joseph Tassarotti; Jean-Baptiste Tristan (AKBC, 2019)

Unlocking Fairness: a Trade-off Revisited. - Michael L. Wick; Swetasudha Panda; Jean-Baptiste Tristan (NeurIPS, 2019)

Verification of ML Systems via Reparameterization. - Jean-Baptiste Tristan; Joseph Tassarotti; Koundinya Vajjha; Michael L. Wick; Anindya Banerjee 001 (CoRR, 2020)

Detecting and Exorcising Statistical Demons from Language Models with Anti-Models of Negative Data. - Michael L. Wick; Kate Silverstein; Jean-Baptiste Tristan; Adam Craig Pocock; Mark Johnson (CoRR, 2020)

Conjugate Energy-Based Models. - Hao Wu 020; Babak Esmaeili 001; Michael L. Wick; Jean-Baptiste Tristan; Jan-Willem van de Meent (ICML, 2021)

Online Post-Processing in Rankings for Fair Utility Maximization. - Ananya Gupta; Eric Johnson; Justin Payan; Aditya Kumar Roy; Ari Kobren; Swetasudha Panda; Jean-Baptiste Tristan; Michael L. Wick (WSDM, 2021)

Upstream Mitigation Is Not All You Need: Testing the Bias Transfer Hypothesis in Pre-Trained Language Models. - Ryan Steed; Swetasudha Panda; Ari Kobren; Michael L. Wick (ACL (1), 2022)

Don't Just Clean It, Proxy Clean It: Mitigating Bias by Proxy in Pre-Trained Models. - Swetasudha Panda; Ari Kobren; Michael L. Wick; Qinlan Shen (EMNLP (Findings), 2022)

Understanding the Interplay of Scale, Data, and Bias in Language Models: A Case Study with BERT. - Muhammad Ali; Swetasudha Panda; Qinlan Shen; Michael L. Wick; Ari Kobren (CoRR, 2024)