William W. Cohen's Papers: Inductive Logic Programming

1. William W. Cohen, Fan Yang, and Kathryn Rivard Mazaitis (2020): TensorLog: A Probabilistic Database Implemented Using Deep-Learning Infrastructure in JAIR.
   
   • Most complete paper on TensorLog, a predecessor of NQL/EmQL that was a Prolog-like logic, not a dataflow query language.
2. William W. Cohen, Haitian Sun, Alex Hofer, Matthew Siegler (2019): Differentiable Representations For Multihop Inference Rules in arxiv.
   
   • Earlier version of ICLR paper on NQL.
3. William W. Cohen, Matthew Siegler, Alex Hofer (2019): Neural Query Language: A Knowledge Base Query Language for Tensorflow in arxiv.
   
   • Earlier version of ICLR paper on NQL focusing on the language constructs used.
4. Fan Yang, Jiazhong Nie, William W. Cohen, Ni Lao (2017): Learning to Organize Knowledge with N-Gram Machines in arxiv.org/abs/1711.06744.
5. Fan Yang, Zhilin Yang, William W. Cohen (2017): Differentiable Learning of Logical Rules for Knowledge Base Reasoning in NIPS-2017.
6. William W. Cohen and Fan Yang (2017): TensorLog: Deep Learning Meets Probabilistic Databases in arxiv.org 1707.05390.
7. William W. Cohen (2016): TensorLog: A Differentiable Deductive Database in arxiv.org 1605.06523.
8. Rose Catherine and William W. Cohen (2016): Personalized Recommendations using Knowledge Graphs: A Probabilistic Logic Programming Approach in RecSys 2016.
9. William Yang Wang and William W. Cohen (2016): Learning First-Order Logic Embeddings via Matrix Factorization in IJCAI-2016.
10. Ni Lao, Einat Minkov, and William W. Cohen (2015): Learning Relational Features with Backward Random Walks in ACL-2015.
11. William Yang Wang, Kathryn Mazaitis, and William W. Cohen (2015): Joint Information Extraction and Reasoning: A Scalable Statistical Relational Learning Approach in ACL-2015.
12. Dana Movshovitz-Attias and William W. Cohen (2015): KB-LDA: Jointly Learning a Knowledge Base of Hierarchy, Relations, and Facts in ACL-2015.
13. William Yang Wang, Kathryn Mazaitis, and William W. Cohen (2015): A Soft Version of Predicate Invention Based on Structured Sparsity in IJCAI-2015.
14. 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.
15. 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.
16. William Yang Wang, Kathryn Mazaitis, and William W. Cohen (2014): Structure Learning via Parameter Learning in CIKM-2014.
17. 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
18. William Yang Wang, Kathryn Mazaitis, William W. Cohen (2013): Programming with Personalized PageRank: A Locally Groundable First-Order Probabilistic Logic in arxiv 1305.2254.
19. 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.
20. Andrew Arnold and William W. Cohen (2009): Information Extraction as Link Prediction: Using Curated Citation Networks to Improve Gene Detection in WASA-2009.
21. Andrew Arnold and William W. Cohen (2009): Information Extraction as Link Prediction: Using Curated Citation Networks to Improve Gene Detection in ICWSM-2009 (poster).
22. 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.
23. 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.
24. 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).
25. 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).
26. 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.
27. Noboru Matsuda, William Cohen & Ken Koedinger (2005): Building Cognitive Tutors with Programming by Demonstration in ILP-2005 (late-breaking paper).
28. 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.
29. 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).
30. William W. Cohen (1998): Hardness Results for Learning First-Order Representations and Programming by Demonstration in Machine Learning 30(1): 57-87 (1998).
31. William W. Cohen and Prem Devanbu (1997): A Comparative Study of Inductive Logic Programming Methods for Software Fault Prediction in ICML 1997: 66-74.
32. William W. Cohen (1995): Pac-learning non-recursive prolog clauses in Artif. Intell. 79(1): 1-38 (1995).
33. William W. Cohen (1995): Learning to Classify English Text with ILP Methods in Advances in ILP, ed. L. de Readt, IOS Press.
34. William W. Cohen (1995): Text categorization and relational learning in ICML 1995: 124-132.
35. 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).
36. William W. Cohen (1995): Pac-learning recursive logic programs: Efficient algorithms in J. Artif. Intell. Res. (JAIR) 2: 501-539 (1995).
37. William W. Cohen (1995): Pac-learning recursive logic programs: Negative results in J. Artif. Intell. Res. (JAIR) 2: 541-573 (1995).
38. William W. Cohen (1994): Pac-learning nondeterminate Clauses in AAAI 1994: 676-681.
39. William W. Cohen (1994): Recovering Software Specifications with Inductive Logic Programming in AAAI 1994: 142-148.
40. William W. Cohen (1994): Grammatically biased learning: learning logic programs using an explicit antecedent description language in Artif. Intell. 68(2): 303-366 (1994).
41. William W. Cohen (1993): Cryptographic limitations on learning one-clause logic programs in AAAI 1993: 80-85.
42. William W. Cohen (1993): Rapid prototyping of ILP systems using explicit bias in Proc. of the 1993 IJCAI Workshop on Inductive Logic Programming.
43. William W. Cohen (1993): Pac-learning a restricted class of recursive logic programs in AAAI 1993: 86-92.
44. William W. Cohen and Haym Hirsh (1992): Learnability of Description Logics in COLT 1992: 116-127.
45. William W. Cohen (1992): Compiling prior knowledge into an explicit bias in ICML 1992: 102-110.
46. 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: GNAT System| Retrieval Augmented LMs| Applications| Collaborative Filtering| Intelligent Tutoring| Explanation-Based Learning| Formal Results| Learning in Graphs| Inductive Logic Programming| Neural Knowledge Representation| Topic Modeling| Matching/Data Integration| Deep Learning| Rule Learning| Text Categorization| Info Extraction/Reading/QA| By year: All papers]