- William W. Cohen (2016): TensorLog: A Differentiable Deductive Database in arxiv.org 1605.06523.
- William Yang Wang and William W. Cohen (2016): Learning First-Order Logic Embeddings via Matrix Factorization in IJCAI-2016.
- Ni Lao, Einat Minkov, and William W. Cohen (2015): Learning Relational Features with Backward Random Walks in ACL-2015.
- William Yang Wang, Kathryn Mazaitis, and William W. Cohen (2015): Joint Information Extraction and Reasoning: A Scalable Statistical Relational Learning Approach in ACL-2015.
- Dana Movshovitz-Attias and William W. Cohen (2015): KB-LDA: Jointly Learning a Knowledge Base of Hierarchy, Relations, and Facts in ACL-2015.
- William Yang Wang, Kathryn Mazaitis, and William W. Cohen (2015): A Soft Version of Predicate Invention Based on Structured Sparsity in IJCAI-2015.
- William Yang Wang, Kathryn Mazaitis, Ni Lao, Tom Mitchell, and William W. Cohen (2015): Efficient Inference and Learning in a Large Knowledge Base: Reasoning with Extracted Information using a Locally Groundable First-Order Probabilistic Logic in Machine Learning, 2015.
- William Yang Wang, Lingpeng Kong, Kathryn Mazaitis, and William W. Cohen (2014): Dependency Parsing for Weibo: An Efficient Probabilistic Logic Programming Approach in EMNLP-2014.
- William Yang Wang, Kathryn Mazaitis, and William W. Cohen (2014): Structure Learning via Parameter Learning in CIKM-2014.
- William Yang Wang, Kathryn Mazaitis, William W. Cohen (2013): Programming with Personalized PageRank: A Locally Groundable First-Order Probabilistic Logic in CIKM-2013
**(Honorable Mention for Best Paper at CIKM-2013)**. (Originally published as: William Yang Wang, Kathryn Mazaitis, William W. Cohen (2013): Programming with Personalized PageRank: A Locally Groundable First-Order Probabilistic Logic in arxiv 1305.2254; William Yang Wang, Kathryn Mazaitis, William W. Cohen (2013):*Programming with Personalized PageRank: A Locally Groundable First-Order Probabilistic Logic*in ICML 2103 Workshop on Inferning). - Andrew Arnold and William W. Cohen (2009): Information Extraction as Link Prediction: Using Curated Citation Networks to Improve Gene Detection in WASA-2009.
- Andrew Arnold and William W. Cohen (2009): Information Extraction as Link Prediction: Using Curated Citation Networks to Improve Gene Detection in ICWSM-2009 (poster).
- Noboru Matsuda, William Cohen, Jonathan Sewall, Gustavo Lacerda and Ken Koedinger (2007): Predicting students performance with a SimStudent that learns cognitive skills from observation in AIED-2007.
- Noboru Matsuda, William Cohen, Jonathan Sewall, Gustavo Lacerda and Ken Koedinger (2007): Evaluating a simulated student using real students data for training and testing in UM-2007.
- Noboru Matsuda, William Cohen & Ken Koedinger (2006): What characterizes a better demonstration for cognitive modeling by demonstration? in CMU SCS Technical Report Series (CMU-ML-06-106).
- Noboru Matsuda, William W. Cohen, Jonathan Sewall, Kenneth R. Koedinger (2006): Applying Machine Learning to Cognitive Modeling for Cognitive Tutors in CMU SCS Technical Report Series (CMU-ML-06-105).
- Noboru Matsuda, William Cohen & Ken Koedinger (2005): An Intelligent Authoring System with Programming by Demonstration. in Proceedings of the Japan National Conference on Information and Systems in Education.
- Noboru Matsuda, William Cohen & Ken Koedinger (2005): Building Cognitive Tutors with Programming by Demonstration in ILP-2005 (late-breaking paper).
- Noboru Matsuda, William Cohen & Ken Koedinger (2005): Applying Programming by Demonstration in an Intelligent Authoring Tool for Cognitive Tutors in AAAI Workshop on Human Comprehensible Machine Learning.
- William W. Cohen & Prem Devanbu (2000):
*Automatically Exploring Hypotheses about Fault Prediction: a Comparative Study of Inductive Logic Programming Methods*in International Journal of Software Engineering and Knowledge Engineering 9(5): 519-546 (1999). (Originally published as: William W. Cohen and Prem Devanbu (1997): A Comparative Study of Inductive Logic Programming Methods for Software Fault Prediction in ICML 1997: 66-74). - William W. Cohen (1998):
*Hardness Results for Learning First-Order Representations and Programming by Demonstration*in Machine Learning 30(1): 57-87 (1998). (Originally published as: William W. Cohen (1996): The Dual DFA Learning Problem: Hardness Results for Programming by Demonstration and Learning First-Order Representations (Extended Abstract) in COLT 1996: 29-40). - William W. Cohen (1995):
*Pac-learning non-recursive prolog clauses*in Artif. Intell. 79(1): 1-38 (1995). - William W. Cohen (1995): Learning to Classify English Text with ILP Methods in
*Advances in ILP, ed. L. de Readt, IOS Press*. - William W. Cohen (1995): Text categorization and relational learning in ICML 1995: 124-132.
- William W. Cohen and C. David Page Jr (1995):
*Polynomial learnability and inductive logic programming: Methods and results*in New Generation Comput. 13(3&4): 369-409 (1995). - William W. Cohen (1995): Pac-learning recursive logic programs: Efficient algorithms in J. Artif. Intell. Res. (JAIR) 2: 501-539 (1995).
- William W. Cohen (1995): Pac-learning recursive logic programs: Negative results in J. Artif. Intell. Res. (JAIR) 2: 541-573 (1995).
- William W. Cohen (1994):
*Pac-learning nondeterminate Clauses*in AAAI 1994: 676-681. - William W. Cohen (1994): Grammatically biased learning: learning logic programs using an explicit antecedent description language in Artif. Intell. 68(2): 303-366 (1994).
- William W. Cohen (1993): Cryptographic limitations on learning one-clause logic programs in AAAI 1993: 80-85.
- William W. Cohen (1993): Rapid prototyping of ILP systems using explicit bias in
*Proc. of the 1993 IJCAI Workshop on Inductive Logic Programming*. - William W. Cohen (1993): Pac-learning a restricted class of recursive logic programs in AAAI 1993: 86-92.
- William W. Cohen (1992): Compiling prior knowledge into an explicit bias in
*ICML 1992: 102-110*. - William W. Cohen (1993):
*Learnability of Restricted Logic Programs*in*Proc. of the Third International Workshop on Inductive Logic Programming (ILP-93)*.

[Selected papers| By topic: Matching/Data Integration| Text Categorization| Topic Modeling| Rule Learning| Explanation-Based Learning| Formal Results| Inductive Logic Programming| Information Extraction| Collaborative Filtering| Applications| Intelligent Tutoring| Learning in Graphs| By year: All papers]