2009

• D. Roth and R. Samdani, Learning Multi-Linear Representations. Machine Learning  (2009) pp. 195--209 [bibitem]

2008

• V. Punyakanok, D. Roth, and W. Yih, The Importance of Syntactic Parsing and Inference in Semantic Role Labeling. Computational Linguistics  (2008)  [bibitem]
• E. Daya, D. Roth, and S. Wintner, Learning Hebrew Roots: Machine Learning with Linguistic Constraints. Computational Linguistics  (2008)  [bibitem]

2007

• Z. Zeng, J. Tu, M. Liu, T. Huang, B. Pianfetti, D. Roth, and S. Levinson, Audio-Visual Affect Recognition. IEEE Transactions on Multimedia  (2007) pp. 424-428 [bibitem]

2006

• R. de Salvo Braz, R. Girju, V. Punyakanok, D. Roth, and M. Sammons, An Inference Model for Semantic Entailment in Natural Language. Machine Learning Challenges, Evaluating Predictive Uncertainty, Visual Object Classification and Recognizing Textual Entailment, First PASCAL Machine Learning Challenges Workshop, Revised Selected Papers  (2006) pp. 261--286 [bibitem]
  • Revised version of PASCAL RTE Challenge. Textual Entailment, integrating NLP text analyis, shallow semantic inference
• O. Mengshoel, D. Roth, and D. Wilkins, Controlled generation of hard and easy Bayesian networks: Impact on maximal clique size in tree clustering. Artificial Intelligence  (2006)  [bibitem]

2005

• S. Agarwal, T. Graepel, R. Herbrich, S. Har-Peled, and D. Roth, Generalization Bounds for the Area Under the ROC Curve. Journal of Machine Learning Research  (2005) pp. 393--425 (abstract) [bibitem]
• R. Khardon, D. Roth, and R. Servedio, Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms. Journal of Machine Learning Research  (2005) pp. 341-356 [bibitem]
• P. Fung and D. Roth, Guest Editors Introduction: Machine Learning in Speech and Language Technologies. Machine Learning  (2005) pp. 1-6 [bibitem]
• D. Roth, Learning based Programming. Innovations in Machine Learning: Theory and Applications  (2005)  [bibitem]
  • Suggests a paradigm and a programming language for the programming of learning intensive computer systems. Springer: http://dx.doi.org/10.1007/3-540-33486-6_3
• X. Li, P. Morie, and D. Roth, Semantic Integration in Text: From Ambiguous Names to Identifiable Entities. AI Magazine. Special Issue on Semantic Integration  (2005) pp. 45--68 (abstract) [bibitem]
  • Named Entity Recognition; coreference resolution; entity identification; matching Entities Mentions within and across documents.
• X. Li and D. Roth, Learning Question Classifiers: The Role of Semantic Information. Journal of Natural Language Engineering  (2005)  [bibitem]
  • Question Classification; Hierarchical classifiers; Sequential Model; Expressive Feature generation with semantic information.
• V. Punyakanok and D. Roth, Inference with Classifiers: The Phrase Identification Problem. Computational Linguistics  (2005)  [bibitem]
  • In submission
  • Shallow Parsing; Chunking; HMMs, Conditional Models and Constraint Satisfaction Models. Top results on phrase chunking

2004

• S. Agarwal, A. Awan, and D. Roth, Learning to Detect Objects in Images via a Sparse, Part-Based Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence  (2004) pp. 1475--1490 (abstract) [bibitem]

2002

• D. Roth, M. Yang, and N. Ahuja, Learning to Recognize 3D Objects. Neural Computation  (2002) pp. 1071--1104 (abstract) [bibitem]
• R. Greiner, A. Grove, and D. Roth, Learning Cost-Sensitive Active Classifiers. Artificial Intelligence  (2002) pp. 137--174 (abstract) [bibitem]
  • Earlier version appeared in ICML'96

2001

• J. Chuang and D. Roth, Gene Recognition based on DAG shortest paths. Bioinformatics  (2001) pp. S56--S64 (abstract) [bibitem]
• A. Grove and D. Roth, Linear concepts and hidden variables. Machine Learning  (2001) pp. 123--141 (abstract) [bibitem]

1999

• R. Khardon and D. Roth, Learning to Reason with a Restricted View. Machine Learning  (1999) pp. 95--117 (abstract) [bibitem]
• R. Khardon, H. Mannila, and D. Roth, Reasoning with Examples: Propositional Formulae and Database Dependencies. Acta Informatica  (1999) pp. 267--286 (abstract) [bibitem]
• A. Golding and D. Roth, A Winnow based approach to Context-Sensitive Spelling Correction. Machine Learning  (1999) pp. 107--130 (abstract) [bibitem]
  • Special Issue on Machine Learning and Natural Language.
• M. Mavronicolas and D. Roth, Linearizable Read/Write Objects. Theoretical Computer Science  (1999) pp. 267--319 (abstract) [bibitem]

1998

• H. Aizenstein, A. Blum, R. Khardon, A. Kushilevitz, L. Pitt, and D. Roth, On learning read-k satisfy-j DNF. SIAM Journal of Computing  (1998) pp. 1515--1530 (abstract) [bibitem]

1997

• R. Khardon and D. Roth, Defaults and Relevance in Model Based Reasoning. Artificial Intelligence  (1997) pp. 169--193 (abstract) [bibitem]
• R. Khardon and D. Roth, Learning to Reason. Journal of the ACM  (1997) pp. 697--725 (abstract) [bibitem]
• K. Daniels, V. Milenkovic, and D. Roth, Finding the Maximum Area Axis-Parallel Rectangle in a Polygon. Computational Geometry: Theory and Applications  (1997) pp. 125--148 (abstract) [bibitem]

1996

• D. Roth, On the hardness of approximate reasoning. Artificial Intelligence  (1996) pp. 273--302 (abstract) [bibitem]
  • Hardness of Reasoning with Bayesian Networks; Exact inference is #P-Complete; Approximate Reasoning is NP-Hard.
• E. Kushilevitz and D. Roth, On Learning Visual Concepts and DNF Formulae. Machine Learning  (1996) pp. 65--85 (abstract) [bibitem]
• R. Khardon and D. Roth, Reasoning with Models. Artificial Intelligence  (1996) pp. 187--213 (abstract) [bibitem]
  • Characteristic Models of Boolean Functions