UMASS Data Science Talks
Michael Wick, UMASS data science workshop event
Exponential Stochastic Cellular Automata for Massively Parallel Inference
Manzil Zaheer, Michael Wick, Jean-Baptiste Tristan, Alex Smola, Guy Steele, AISTATS 2016
Minimally Constrained Multilingual Word Embeddings via Artificial Code Switching
Michael Wick, Pallika Kanani, Adam Pocock, AAAI 2016
Attribute Extraction from Noisy Text Using Character-based Sequence Tagging Models
Pallika Kanani, Michael Wick, Adam Pocock, NIPS Workshop on Machine Learning for e-Commerce 2016.
Minimally Constrained Multilingual Word Embeddings via Artificial Code Switching
Michael Wick, Pallika Kanani, Adam Pocock, NIPS Workshop on Multi-Task and Transfer Learning 2016.
Epistemological Databases for Probabilistic Knowledge Base Construction
Michael Wick, Ph.D. Dissertation, University of Massachusetts
Large-scale author coreference via hierarchical entity representations.
Michael Wick, Ari Kobren, Andrew McCallum, In Proceedings, (2013)
A Joint Model for Discovering and Linking Entities
Michael Wick, Sameer Singh, Harshal Pandya, Andrew McCallum, Automated Knowledge Base Construction (AKBC) WS
Assessing Confidence of Knowledge Base Content with an Experimental Study in Entity Resolution
Michael Wick, Sameer Singh, Ari Kobren, Andrew McCallum, Automated Knowledge Base Construction (AKBC) WS
Probabilistic Reasoning about Human Edits in Information Integration.
Michael Wick, Ari Kobren, Andrew McCallum, ICML WS: Machine Learning Meets Crowdsourcing
A Discriminative Hierarchical Model for Fast Coreference at Large Scale
Michael Wick, Sameer Singh, Andrew McCallum, Association for Computational Linguistics (ACL)
Human Machine Cooperation with Epistemological DBs: Supporting User Corrections to Automatically Constructed KBs
Michael Wick, Karl Schultz, Andrew McCallum, Automated Knowledge Base Construction (AKBC-WEKEX)
MCMCMC: Efficient Inference by Approximate Sampling
Sameer Singh, Michael Wick, Andrew McCallum, Empirical Methods in Natural Language Processing (EMNLP)
Monte Carlo MCMC: Efficient Inference by Sampling Factors
Sameer Singh, Michael Wick, Andrew McCallum, Automated Knowledge Base Construction (AKBC-WEKEX)
Hybrid In-Database Inference for Declarative Information Extraction
Daisy Zhe Wang, Michael J. Franklin, Minos Garofalakis, Joseph M. Hellerstein, Michael Wick, SIGMOD
Query-Aware MCMC
Michael Wick, Andrew McCallum, Neural Information Processing Systems (NIPS)
SampleRank: Training Factor Graphs with Atomic Gradients
Michael Wick, Khashayar Rohanimanesh, Kedare Bellare, Aron Culotta, Andrew McCallum, International Conference on Machine Learning (ICML)
Distantly labeling data for large scale cross-document coreference
Sameer Singh, Michael Wick, Andrew McCallum, arXiv tech report
Scalable Probabilistic Databases with Factor Graphs and MCMC
Michael Wick, Andrew McCallum, Gerome Miklau, Very Large Databases (VLDB)
Representing Uncertainty in Databases with Scalable Factor Graphs
Michael Wick, University of Massachusetts Masters Thesis
Advances in Learning and Inference for Partition-wise Models of Coreference Resolution
Michael Wick, Andrew McCallum, University of Massachusetts Technical Report # UM-CS-2009-028
An Entity Based Model for Coreference Resolution
Michael Wick, Aron Culotta, Khashayar Rohanimanesh, Andrew McCallum, SIAM International Conference on Data Mining (SDM)
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
SampleRank: Learning Preferences from Atomic Gradients
Michael Wick, Khashayar Rohanimanesh, Aron Culotta, Andrew McCallum, NIPS WS on Advances in Ranking
Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference
Michael Wick, Khashayar Rohanimanesh, Sameer Singh, Andrew McCallum, Neural Information Processing Systems (NIPS)
Reinforcement Learning for MAP Inference in Large Factor Graphs
Khashayar Rohanimanesh, Michael Wick, Sameer Singh, Andrew McCallum, University of Massachusetts Technical Report #UM-CS-2008-040
A Corpus for Cross-Document Co-reference
David Day, Janet Hitzeman, Michael Wick, Keith Crouch, Massimo Poesio, Language Resources and Evaluation (LREC)
A Discriminative Approach to Ontology Alignmen
Michael Wick, Khashayar Rohanimanesh, Andrew McCallum, AnHai Doan, VLDB WS on New Trends in Information Integration (NTII)
A Unified Approach for Schema Matching, Coreference,and Canonicalization
Michael Wick, Khashayar Rohanimanesh, Karl Schultz, Andrew McCallum, Knowledge Discovery and Data Mining (KDD)
FACTORIE: Efficient Probabilistic Programming for Relational Factor Graphs via Imperative Declarations of Structure, Inference and Learning
Andrew McCallum, Khashayar Rohanimanesh, Michael Wick, Karl Schultz, Sameer Singh, NIPS WS on Probabilistic Programming
Author Disambiguation using Error-Driven Machine Learning With a Ranking Loss Function
Aron Culotta, Pallika Kanani, Robert Hall, Michael Wick, Andrew McCallum, IIWeb WS at AAAI
Canonicalization of Database Records using Adaptive Similarity Measures
Aron Culotta, Michael Wick, Rob Hall, Matthew Marzilli, Andrew McCallum, Knowledge Discovery and Data Mining (KDD)
Context-Sensitive Error Correction: Using Topic Models to Improve OCR
Michael Wick, Michael Ross, Erik Learned-Miller, International Conference on Document Recognition (ICDAR)
Exploiting Encyclypedic and Lexical Resources for Entity Disambiguation
Massimo Poesio, David Day, Ron Arstein, Jason Dunacn, Vladimir Eidelman, Claudio Guiliano, Rob Hall, Janet Hitzeman, Alan Jern, Mijail Kabadjov, Gideon Mann, Paul McNamee, Alessandro Moschitti, Simone Ponzetto, Jason Smith, Josef Steinberger, Michael Strube, Jian Su, Yannick Versley, Xiaofeng Yang, Michael Wick, Michael Wick, Johns Hopkins Technical Report
First Order Probabilistic Models for Coreference Resolution
Aron Culotta, Michael Wick, Rob Hall, Andrew McCallum, The North American Chapter of the Association of Computational Linguistics and Human Language Technologies
Learning Field Compatibilities to Extract Database Records from Unstructured Text
Michael Wick, Aron Culotta, Andrew McCallum, Empirical Methods in Natural Language Processing (EMNLP)