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2020 – today
- 2024
[i10]Muhammad Ali, Swetasudha Panda, Qinlan Shen, Michael L. Wick, Ari Kobren:
Understanding the Interplay of Scale, Data, and Bias in Language Models: A Case Study with BERT. CoRR abs/2407.21058 (2024)- 2022
- [c30]Ryan Steed
, Swetasudha Panda, Ari Kobren, Michael L. Wick:
Upstream Mitigation Is Not All You Need: Testing the Bias Transfer Hypothesis in Pre-Trained Language Models. ACL (1) 2022: 3524-3542 - [c29]Swetasudha Panda, Ari Kobren, Michael L. Wick, Qinlan Shen:
Don't Just Clean It, Proxy Clean It: Mitigating Bias by Proxy in Pre-Trained Models. EMNLP (Findings) 2022: 5073-5085 - 2021
- [c28]Hao Wu, Babak Esmaeili, Michael L. Wick, Jean-Baptiste Tristan, Jan-Willem van de Meent:
Conjugate Energy-Based Models. ICML 2021: 11228-11239 - [c27]Ananya Gupta, Eric Johnson, Justin Payan, Aditya Kumar Roy, Ari Kobren, Swetasudha Panda, Jean-Baptiste Tristan, Michael L. Wick:
Online Post-Processing in Rankings for Fair Utility Maximization. WSDM 2021: 454-462 - [i9]Hao Wu, Babak Esmaeili, Michael L. Wick, Jean-Baptiste Tristan, Jan-Willem van de Meent:
Conjugate Energy-Based Models. CoRR abs/2106.13798 (2021) - 2020
- [i8]Jean-Baptiste Tristan, Joseph Tassarotti, Koundinya Vajjha, Michael L. Wick, Anindya Banerjee:
Verification of ML Systems via Reparameterization. CoRR abs/2007.06776 (2020) - [i7]Michael L. Wick, Kate Silverstein, Jean-Baptiste Tristan, Adam Craig Pocock, Mark Johnson:
Detecting and Exorcising Statistical Demons from Language Models with Anti-Models of Negative Data. CoRR abs/2010.11855 (2020)
2010 – 2019
- 2019
- [c26]Jay Yoon Lee, Sanket Vaibhav Mehta, Michael L. Wick, Jean-Baptiste Tristan, Jaime G. Carbonell:
Gradient-Based Inference for Networks with Output Constraints. AAAI 2019: 4147-4154 - [c25]Joseph Tassarotti, Jean-Baptiste Tristan, Michael L. Wick:
Sketching for Latent Dirichlet-Categorical Models. AISTATS 2019: 256-265 - [c24]Michael L. Wick, Swetasudha Panda, Joseph Tassarotti, Jean-Baptiste Tristan:
Scaling Hierarchical Coreference with Homomorphic Compression. AKBC 2019 - [c23]Michael L. Wick, Swetasudha Panda, Jean-Baptiste Tristan:
Unlocking Fairness: a Trade-off Revisited. NeurIPS 2019: 8780-8789 - 2018
- [i6]Joseph Tassarotti, Jean-Baptiste Tristan, Michael L. Wick:
Sketching for Latent Dirichlet-Categorical Models. CoRR abs/1810.01400 (2018) - 2017
- [c22]Jay Yoon Lee, Michael L. Wick, Jean-Baptiste Tristan, Jaime G. Carbonell:
Enforcing Output Constraints via SGD: A Step Towards Neural Lagrangian Relaxation. AKBC@NIPS 2017 - [i5]Jay Yoon Lee, Michael L. Wick, Jean-Baptiste Tristan, Jaime G. Carbonell:
Enforcing Constraints on Outputs with Unconstrained Inference. CoRR abs/1707.08608 (2017) - 2016
- [c21]Michael L. Wick, Pallika Kanani, Adam Craig Pocock:
Minimally-Constrained Multilingual Embeddings via Artificial Code-Switching. AAAI 2016: 2849-2855 - [c20]Manzil Zaheer, Michael L. Wick, Jean-Baptiste Tristan, Alexander J. Smola, Guy L. Steele Jr.:
Exponential Stochastic Cellular Automata for Massively Parallel Inference. AISTATS 2016: 966-975 - [c19]Christan Earl Grant, Daisy Zhe Wang, Michael L. Wick:
Query-Driven Sampling for Collective Entity Resolution. IRI 2016: 208-217 - [i4]Farahnaz Ahmed Wick, Michael L. Wick, Marc Pomplun:
Filling in the details: Perceiving from low fidelity images. CoRR abs/1604.04125 (2016) - 2015
- [i3]Christan Earl Grant, Daisy Zhe Wang, Michael L. Wick:
Query-Driven Sampling for Collective Entity Resolution. CoRR abs/1508.03116 (2015) - 2013
- [c18]Michael L. Wick, Sameer Singh, Ari Kobren, Andrew McCallum:
Assessing confidence of knowledge base content with an experimental study in entity resolution. AKBC@CIKM 2013: 13-18 - [c17]Michael L. Wick, Sameer Singh, Harshal Pandya, Andrew McCallum:
A joint model for discovering and linking entities. AKBC@CIKM 2013: 67-72 - 2012
- [c16]Michael L. Wick, Sameer Singh, Andrew McCallum:
A Discriminative Hierarchical Model for Fast Coreference at Large Scale. ACL (1) 2012: 379-388 - [c15]Sameer Singh, Michael L. Wick, Andrew McCallum:
Monte Carlo MCMC: Efficient Inference by Approximate Sampling. EMNLP-CoNLL 2012: 1104-1113 - [c14]Michael L. Wick, Karl Schultz, Andrew McCallum:
Human-Machine Cooperation: Supporting User Corrections to Automatically Constructed KBs. AKBC-WEKEX@NAACL-HLT 2012: 89-94 - [c13]Sameer Singh, Michael L. Wick, Andrew McCallum:
Monte Carlo MCMC: Efficient Inference by Sampling Factors. AKBC-WEKEX@NAACL-HLT 2012: 111-115 - 2011
- [c12]Michael L. Wick, Khashayar Rohanimanesh, Kedar Bellare, Aron Culotta, Andrew McCallum:
SampleRank: Training Factor Graphs with Atomic Gradients. ICML 2011: 777-784 - [c11]Michael L. Wick, Andrew McCallum:
Query-Aware MCMC. NIPS 2011: 2564-2572 - [c10]Daisy Zhe Wang, Michael J. Franklin, Minos N. Garofalakis, Joseph M. Hellerstein, Michael L. Wick:
Hybrid in-database inference for declarative information extraction. SIGMOD Conference 2011: 517-528 - 2010
- [j1]Michael L. Wick, Andrew McCallum, Gerome Miklau:
Scalable Probabilistic Databases with Factor Graphs and MCMC. Proc. VLDB Endow. 3(1): 794-804 (2010) - [i2]Michael L. Wick, Andrew McCallum, Gerome Miklau:
Scalable Probabilistic Databases with Factor Graphs and MCMC. CoRR abs/1005.1934 (2010) - [i1]Sameer Singh, Michael L. Wick, Andrew McCallum:
Distantly Labeling Data for Large Scale Cross-Document Coreference. CoRR abs/1005.4298 (2010)
2000 – 2009
- 2009
- [c9]Michael L. Wick, Khashayar Rohanimanesh, Sameer Singh, Andrew McCallum:
Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference. NIPS 2009: 2044-2052 - [c8]Michael L. Wick, Aron Culotta, Khashayar Rohanimanesh, Andrew McCallum:
An Entity Based Model for Coreference Resolution. SDM 2009: 365-376 - 2008
- [c7]Michael L. Wick, Khashayar Rohanimanesh, Karl Schultz, Andrew McCallum:
A unified approach for schema matching, coreference and canonicalization. KDD 2008: 722-730 - [c6]David Day, Janet Hitzeman, Michael L. Wick, Keith Crouch, Massimo Poesio:
A Corpus for Cross-Document Co-reference. LREC 2008 - [c5]Michael L. Wick, Khashayar Rohanimanesh, Andrew McCallum, AnHai Doan:
A Discriminative Approach to Ontology Mapping. NTII 2008: 16-19 - 2007
- [c4]Michael L. Wick, Michael G. Ross, Erik G. Learned-Miller:
Context-Sensitive Error Correction: Using Topic Models to Improve OCR. ICDAR 2007: 1168-1172 - [c3]Aron Culotta, Michael L. Wick, Robert J. Hall, Matthew Marzilli, Andrew McCallum:
Canonicalization of database records using adaptive similarity measures. KDD 2007: 201-209 - [c2]Aron Culotta, Michael L. Wick, Andrew McCallum:
First-Order Probabilistic Models for Coreference Resolution. HLT-NAACL 2007: 81-88 - 2006
- [c1]Michael L. Wick, Aron Culotta, Andrew McCallum:
Learning Field Compatibilities to Extract Database Records from Unstructured Text. EMNLP 2006: 603-611
Coauthor Index
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