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Publications


See also: an approximation of me on Google Scholar.

2016

Filling in the details: learning to perceive from low-fidelity visual input

Farahnaz Ahmed Wick, Michael Wick, Marc Pomplun. CoRR 2016. [pdf].


Exponential Stochastic Cellular Automata for Massively Parallel Inference

Manzil Zaheer, Michael Wick, Jean-Baptiste Tristan, Alex Smola and Guy Steele. AISTATS 2016. [pdf].


Minimally Constrained Multilingual Word Embeddings via Artificial Code Switching

Michael Wick, Pallika Kanani and Adam Pocock. AAAI 2016. [pdf].


2015

Query-Driven Sampling for Collective Entity Resolution

Christan Grant, Daisy Wang and Michael Wick. CoRR 2015. [pdf].


Exponential Stochastic Cellular Automata for Massively Parallel Inference.

Manzil Zaheer, Michael Wick, Jean-Baptiste Tristan, Alex Smola and Guy Steele. NIPS WS on Learning Systems (LearningSys) 2015. [pdf].


Comparing Gibbs, EM and SEM for MAP Inference in Mixture Models.

Manzil Zaheer, Satwik Kottur, Michael Wick, Jean-Baptiste Tristan. NIPS WS on Optimization for Machine Learning 2015. [pdf].


Minimally Constrained Multilingual Word Embeddings via Artificial Code Switching

Michael Wick, Pallika Kanani and Adam Pocock. NIPS WS on Multi-Task and Transfer Learning 2016. [pdf].


Attribute Extraction from Noisy Text Using Character-based Sequence Tagging Models

Pallika Kanani, Michael Wick and Adam Pocock. NIPS WS on Machine Learning for e-Commerce 2016. [pdf].


2014

Epistemological Databases for Probabilistic Knowledge Base Construction.

Michael Wick. University of Massachusetts Dissertation 2015. [pdf].


Universal Schema for Slot Filling and Cold Start.

UMass IESL at TAC-KBP 2014.


2013

Probabilistic Reasoning about Human Edits in Information Integration.

Michael Wick, Ari Kobren, and Andrew McCallum.

In proc. ICML WS: Machine Learning Meets Crowdsourcing 2013. Selected for oral presentation. [pdf].


Assessing Confidence of Knowledge Base Content with an Experimental Study in Entity Resolution.

Michael Wick, Sameer Singh, Ari Kobren, and Andrew McCallum.

In proc. Automated Knowledge Base Construction Workshop (AKBC) 2013. Selected for oral presentation. [pdf].


A Joint Model for Discovering and Linking Entities.

Michael Wick, Sameer Singh, Harshal Pandya, and Andrew McCallum.

Automated Knowledge Base Construction Workshop (AKBC) 2013. [pdf].


Large-scale author coreference via hierarchical entity representations.

Michael Wick, Ari Kobren, and Andrew McCallum.

ICML WS on Peer Review 2013. [pdf].


2012

MCMCMC: Efficient Inference by Approximate Sampling.

Sameer Singh, Michael Wick, and Andrew McCallum.

To appear in proc. Empirical Methods in Natural Language Processing (EMNLP) 2012. [pdf].


A Discriminative Hierarchical Model for Fast Coreference at Large Scale.

Michael Wick, Sameer Singh, and Andrew McCallum.

To appear in proc. Association for Computational Linguistics (ACL) 2012. [pdf].


Human Machine Cooperation with Epistemological DBs:
Supporting User Corrections to Automatically Constructed KBs

Michael Wick, Karl Schultz, and Andrew McCallum.

NAACL WS on Automatic Knowledge Base Construction (AKBC-WEKEX) 2012. [pdf].
Best paper runner-up.


Monte Carlo MCMC: Efficient Inference by Sampling Factors

Sameer Singh, Michael Wick, and Andrew McCallum.

NAACL WS on Automatic Knowledge Base Construction (AKBC-WEKEX) 2012. [pdf].


2011

Query Aware McMC

Michael Wick and Andrew McCallum.

To appear, proceedings of Neural Information Processing Systems (NIPS) 2011. [pdf].


SampleRank: training factor graphs with atomic gradients

Michael Wick, Khashayar Rohanimanesh, Kedare Bellare, Aron Culotta, Andrew McCallum.

Proceedings of the International Conference on Machine Learning (ICML) 2011. [pdf].


Hybrid in-database inference for declarative information extraction

Daisy Zhe Wang, Michael J. Franklin, Minos Garofalakis, Joseph M. Hellerstein, Michael Wick.

Proceedings of SIGMOD 2011.


2010

Scalable probabilistic databases with factor graphs and MCMC

Michael Wick, Andrew McCallum, Gerome Miklau.

Very Large Data Bases (VLDB) 2010. [arXiv][pdf].


Distantly labeling data for large scale cross-document coreference

Sameer Singh, Michael Wick, Andrew McCallum.

Technical report on arXiv [pdf].


2009

Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference

Michael Wick, Khashayar Rohanimanesh, Sameer Singh, Andrew McCallum.

Neural Information Processing Systems (NIPS) spotlight (only 7% accepted for spotlight) 2009. [pdf] [slide].


SampleRank: Learning Preferences from Atomic Gradients

Michael Wick, Khashayar Rohanimanesh, Aron Culotta, Andrew McCallum.

Neural Information Processing Systems (NIPS-WS) Workshop on Advances in Ranking (talk) 2009. [pdf].


Inference and Learning in Large Factor Graphs with Adaptive Proposal Distributions

Khashayar Rohanimanesh, Michael Wick, Andrew McCallum.

University of Massachusetts Technical Report #UM-CS-2009-008, 2009 [pdf].


Advances in Learning and Inference for Partition-wise Models of Coreference Resolution

Michael Wick and Andrew McCallum.

University of Massachusetts Technical Report # UM-CS-2009-028, 2009. [pdf].


Representing Uncertainty in Databases with Scalable Factor Graphs

Michael Wick, Masters Thesis/Synthesis. Readers: Andrew McCallum and Gerome Miklau. Proposed Fall 2008, Submitted: April 2009. [pdf].


An Entity Based Model for Coreference Resolution

Michael Wick, Aron Culotta, Khashayar Rohanimanesh, Andrew McCallum.

in the proceedings of the SIAM International Conference on Data Mining (SDM), Reno, Nevada, 2009 [pdf].


2008

Reinforcement Learning for MAP Inference in Large Factor Graphs.

Khashayar Rohanimanesh, Michael Wick, Sameer Singh, and Andrew McCallum. University of Massachusetts Technical Report #UM-CS-2008-040, 2008 [pdf].


FACTORIE: Efficient Probabilistic Programming for Relational Factor Graphs via Imperative Declarations of Structure, Inference and Learning.

Andrew McCallum, Khashayar Rohanimanesh, Michael Wick, Karl Schultz, and Sameer Singh. In the proceedings of the Neural Information Processing Systems (NIPS-WS) workshop on Probabilistic Programming, Vancouver, 2008 [pdf]


A Unified Approach for Schema Matching, Coreference,and Canonicalization.

Michael Wick, Khashayar Rohanimanesh, Karl Schultz, Andrew McCallum.

In the 14th ACM SIGKDD international conference on Knowledge Discovery and Data Mining (KDD), Las Vegas, Nevada, 2008. [pdf]


A Discriminative Approach to Ontology Alignment.

Michael Wick, Khashayar Rohanimanesh, Andrew McCallum, and AnHai Doan.

International Workshop on New Trends in Information Integration (NTII) at the conference for Very Large Databases (VLDB-WS), Auckland, New Zealand, 2008. [pdf]


A Corpus for Cross-Document Co-reference.

David Day, Janet Hitzeman, Michael Wick, Keith Crouch, and Massimo Poesio. The sixth international conference on Language Resources and Evaluation (LREC), Marrakech, Morocco, 2008. [pdf]


2007

Exploiting Encyclypedic and Lexical Resources for Entity Disambiguation.

Massimo Poesio, David Day, Ron Arstein, Jason Duncan, Vladimir Eidelman, Claudio Giuliano, Rob Hall, Janet Hitzeman, Alan Jern, Mijail Kabadjov, Gideon Mann, Paul McNamee, Allessandro Moschitti, Simone Ponzetto, Jason Smith, Josef Steinberger, Michael Strube, Jian Su, Yannick Versley, Xiaofeng Yang, and Michael Wick. Johns Hopkins Tech Report 2007 [pdf].


Canonicalization of Database Records using Adaptive Similarity Measures.

Aron Culotta, Michael Wick, Robert Hall, Matthew Marzilli, and Andrew McCallum. In the proceedings of Knowledge Discovery and Data-mining (KDD), San Jose, California, 2007 [pdf].


Author Disambiguation using Error-Driven Machine Learning With a Ranking Loss Function.

Aron Culotta, Pallika Kanani, Robert Hall, Michael Wick, and Andrew McCallum. Information Integration on the Web (IIWeb) workshop for the Association for Advancements in Artificial Intelligence (AAAI) 2007 [pdf].


Context-Sensitive Error Correction: Using Topic Models to Improve OCR.

Michael Wick, Michael Ross, Erik Learned-Miller. In the proceedings of the 9th International Conference on Document Extraction and Analysis (ICDAR), Curitiba, Brazil, 2007. [pdf]


First Order Probabilistic Models for Coreference Resolution.

Aron Culotta, Michael Wick, Robert Hall, Andrew McCallum. The North American Chapter of the Association of Computational Linguistics and Human Language Technologies (NAACL-HLT), Rochester, NY,2007. [pdf]


2006

Learning Field Compatibilities to Extract Database Records from Unstructured Text.

Michael Wick, Aron Culotta, Andrew McCallum. Empirical Methods in Natural Language Processing (EMNLP), Sydney, Australia, 2006. [pdf]