 | 2009 |
|---|
| 122 |       | Jeff Pasternack,
Dan Roth:
Learning better transliterations.
CIKM 2009: 177-186 |
| 121 | | Dan Roth,
Rajhans Samdani:
Learning Multi-linear Representations of Distributions for Efficient Inference.
ECML/PKDD (1) 2009: 11 |
| 120 | | Alexandre Klementiev,
Dan Roth,
Kevin Small,
Ivan Titov:
Unsupervised Rank Aggregation with Domain-Specific Expertise.
IJCAI 2009: 1101-1106 |
| 119 | | Jeff Pasternack,
Dan Roth:
Extracting article text from the web with maximum subsequence segmentation.
WWW 2009: 971-980 |
| 2008 |
|---|
| 118 |  | Ming-Wei Chang,
Lev-Arie Ratinov,
Nicholas Rizzolo,
Dan Roth:
Learning and Inference with Constraints.
AAAI 2008: 1513-1518 |
| 117 | | Dan Roth,
Kevin Small:
Active Learning for Pipeline Models.
AAAI 2008: 683-688 |
| 116 | | Benjamin Liebald,
Dan Roth,
Neelay Shah,
Vivek Srikumar:
Proactive Intrusion Detection.
AAAI 2008: 772-777 |
| 115 | | Ming-Wei Chang,
Lev-Arie Ratinov,
Dan Roth,
Vivek Srikumar:
Importance of Semantic Representation: Dataless Classification.
AAAI 2008: 830-835 |
| 114 | | Eric Bengtson,
Dan Roth:
Understanding the Value of Features for Coreference Resolution.
EMNLP 2008: 294-303 |
| 113 | | Dan Goldwasser,
Dan Roth:
Transliteration as Constrained Optimization.
EMNLP 2008: 353-362 |
| 112 | | Alexandre Klementiev,
Dan Roth,
Kevin Small:
Unsupervised rank aggregation with distance-based models.
ICML 2008: 472-479 |
| 111 | | Mandar Rahurkar,
Dan Roth,
Thomas S. Huang:
Which "Apple" are you talking about ?
WWW 2008: 1197-1198 |
| 110 | | Rodrigo de Salvo Braz,
Eyal Amir,
Dan Roth:
A Survey of First-Order Probabilistic Models.
Innovations in Bayesian Networks 2008: 289-317 |
| 109 | | Vasin Punyakanok,
Dan Roth,
Wen-tau Yih:
The Importance of Syntactic Parsing and Inference in Semantic Role Labeling.
Computational Linguistics 34(2): 257-287 (2008) |
| 108 | | Ezra Daya,
Dan Roth,
Shuly Wintner:
Identifying Semitic Roots: Machine Learning with Linguistic Constraints.
Computational Linguistics 34(3): 429-448 (2008) |
| 2007 |
|---|
| 107 | | Ming-Wei Chang,
Lev-Arie Ratinov,
Dan Roth:
Guiding Semi-Supervision with Constraint-Driven Learning.
ACL 2007 |
| 106 | | Michael Connor,
Dan Roth:
Context Sensitive Paraphrasing with a Global Unsupervised Classifier.
ECML 2007: 104-115 |
| 105 | | Alexandre Klementiev,
Dan Roth,
Kevin Small:
An Unsupervised Learning Algorithm for Rank Aggregation.
ECML 2007: 616-623 |
| 104 | | Nicholas Rizzolo,
Dan Roth:
Modeling Discriminative Global Inference.
ICSC 2007: 597-604 |
| 103 | | Sariel Har-Peled,
Dan Roth,
Dav Zimak:
Maximum Margin Coresets for Active and Noise Tolerant Learning.
IJCAI 2007: 836-841 |
| 102 | | Zhihong Zeng,
Jilin Tu,
Ming Liu,
Thomas S. Huang,
Brian Pianfetti,
Dan Roth,
Stephen E. Levinson:
Audio-Visual Affect Recognition.
IEEE Transactions on Multimedia 9(2): 424-428 (2007) |
| 2006 |
|---|
| 101 | | Rodrigo de Salvo Braz,
Eyal Amir,
Dan Roth:
MPE and Partial Inversion in Lifted Probabilistic Variable Elimination.
AAAI 2006 |
| 100 | | Ming-Wei Chang,
Quang Do,
Dan Roth:
A Pipeline Framework for Dependency Parsing.
ACL 2006 |
| 99 | | Alexandre Klementiev,
Dan Roth:
Weakly Supervised Named Entity Transliteration and Discovery from Multilingual Comparable Corpora.
ACL 2006 |
| 98 | | Dan Roth,
Kevin Small:
Margin-Based Active Learning for Structured Output Spaces.
ECML 2006: 413-424 |
| 97 | | Alexandre Klementiev,
Dan Roth:
Named Entity Transliteration and Discovery from Multilingual Comparable Corpora.
HLT-NAACL 2006 |
| 96 | | Ole J. Mengshoel,
David C. Wilkins,
Dan Roth:
Controlled generation of hard and easy Bayesian networks: Impact on maximal clique size in tree clustering.
Artif. Intell. 170(16-17): 1137-1174 (2006) |
| 2005 |
|---|
| 95 | | Rodrigo de Salvo Braz,
Roxana Girju,
Vasin Punyakanok,
Dan Roth,
Mark Sammons:
An Inference Model for Semantic Entailment in Natural Language.
AAAI 2005: 1043-1049 |
| 94 | | Shivani Agarwal,
Dan Roth:
Learnability of Bipartite Ranking Functions.
COLT 2005: 16-31 |
| 93 | | Vasin Punyakanok,
Dan Roth,
Mark Sammons,
Wen-tau Yih:
Demonstrating an Interactive Semantic Role Labeling System.
HLT/EMNLP 2005 |
| 92 | | Cecilia Ovesdotter Alm,
Dan Roth,
Richard Sproat:
Emotions from Text: Machine Learning for Text-based Emotion Prediction.
HLT/EMNLP 2005 |
| 91 | | Brian Ziebart,
Dan Roth,
Roy H. Campbell,
Anind K. Dey:
Automated and Adaptive Threshold Setting: Enabling Technology for Autonomy and Self-Management.
ICAC 2005: 204-215 |
| 90 | | Dan Roth,
Wen-tau Yih:
Integer linear programming inference for conditional random fields.
ICML 2005: 736-743 |
| 89 | | Vasin Punyakanok,
Dan Roth,
Wen-tau Yih:
The Necessity of Syntactic Parsing for Semantic Role Labeling.
IJCAI 2005: 1117-1123 |
| 88 | | Vasin Punyakanok,
Dan Roth,
Wen-tau Yih,
Dav Zimak:
Learning and Inference over Constrained Output.
IJCAI 2005: 1124-1129 |
| 87 | | Rodrigo de Salvo Braz,
Eyal Amir,
Dan Roth:
Lifted First-Order Probabilistic Inference.
IJCAI 2005: 1319-1325 |
| 86 | | Rodrigo de Salvo Braz,
Roxana Girju,
Vasin Punyakanok,
Dan Roth,
Mark Sammons:
An Inference Model for Semantic Entailment in Natural Language.
IJCAI 2005: 1678-1679 |
| 85 | | Rodrigo de Salvo Braz,
Roxana Girju,
Vasin Punyakanok,
Dan Roth,
Mark Sammons:
An Inference Model for Semantic Entailment in Natural Language.
MLCW 2005: 261-286 |
| 84 | | Xin Li,
Paul Morie,
Dan Roth:
Semantic Integration in Text: From Ambiguous Names to Identifiable Entities.
AI Magazine 26(1): 45-58 (2005) |
| 83 | | Roni Khardon,
Dan Roth,
Rocco A. Servedio:
Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms.
J. Artif. Intell. Res. (JAIR) 24: 341-356 (2005) |
| 82 | | Shivani Agarwal,
Thore Graepel,
Ralf Herbrich,
Sariel Har-Peled,
Dan Roth:
Generalization Bounds for the Area Under the ROC Curve.
Journal of Machine Learning Research 6: 393-425 (2005) |
| 81 | | Pascale Fung,
Dan Roth:
Guest Editors Introduction: Machine Learning in Speech and Language Technologies.
Machine Learning 60(1-3): 5-9 (2005) |
| 2004 |
|---|
| 80 | | Xin Li,
Paul Morie,
Dan Roth:
Identification and Tracing of Ambiguous Names: Discriminative and Generative Approaches.
AAAI 2004: 419-424 |
| 79 | | Xin Li,
Paul Morie,
Dan Roth:
Robust Reading: Identification and Tracing of Ambiguous Names.
HLT-NAACL 2004: 17-24 |
| 78 | | Zhihong Zeng,
Jilin Tu,
Ming Liu,
Tong Zhang,
Nicholas Rizzolo,
ZhenQiu Zhang,
Thomas S. Huang,
Dan Roth,
Stephen E. Levinson:
Bimodal HCI-related affect recognition.
ICMI 2004: 137-143 |
| 77 | | Shivani Agarwal,
Thore Graepel,
Ralf Herbrich,
Dan Roth:
A Large Deviation Bound for the Area Under the ROC Curve.
NIPS 2004 |
| 76 | | Shivani Agarwal,
Aatif Awan,
Dan Roth:
Learning to Detect Objects in Images via a Sparse, Part-Based Representation.
IEEE Trans. Pattern Anal. Mach. Intell. 26(11): 1475-1490 (2004) |
| 2003 |
|---|
| 75 | | Chad M. Cumby,
Dan Roth:
On Kernel Methods for Relational Learning.
ICML 2003: 107-114 |
| 74 | | Ashutosh Garg,
Dan Roth:
Margin Distribution and Learning.
ICML 2003: 210-217 |
| 2002 |
|---|
| 73 | | Sariel Har-Peled,
Dan Roth,
Dav Zimak:
Constraint Classification: A New Approach to Multiclass Classification.
ALT 2002: 365-379 |
| 72 | | Xin Li,
Dan Roth:
Learning Question Classifiers.
COLING 2002 |
| 71 | | Dan Roth,
Wen-tau Yih:
Probabilistic Reasoning for Entity & Relation Recognition.
COLING 2002 |
| 70 | | Shivani Agarwal,
Dan Roth:
Learning a Sparse Representation for Object Detection.
ECCV (4) 2002: 113-130 |
| 69 | | Ming-Hsuan Yang,
Dan Roth,
Narendra Ahuja:
A Tale of Two Classifiers: SNoW vs. SVM in Visual Recognition.
ECCV (4) 2002: 685-699 |
| 68 | | Xavier Carreras,
Lluís Màrquez,
Vasin Punyakanok,
Dan Roth:
Learning and Inference for Clause Identification.
ECML 2002: 35-47 |
| 67 | | Dan Roth:
Reasoning with Classifiers.
ECML 2002: 506-510 |
| 66 | | Ashutosh Garg,
Sariel Har-Peled,
Dan Roth:
On generalization bounds, projection profile, and margin distribution.
ICML 2002: 171-178 |
| 65 | | Chad M. Cumby,
Dan Roth:
Learning with Feature Description Logics.
ILP 2002: 32-47 |
| 64 | | Sariel Har-Peled,
Dan Roth,
Dav Zimak:
Constraint Classification for Multiclass Classification and Ranking.
NIPS 2002: 785-792 |
| 63 | | Dan Roth:
Reasoning with Classifiers.
PKDD 2002: 489-493 |
| 62 | | Dan Roth,
Chad M. Cumby,
Xin Li,
Paul Morie,
Ramya Nagarajan,
Nick Rizzolo,
Kevin Small,
Wen-tau Yih:
Question-Answering via Enhanced Understanding of Questions.
TREC 2002 |
| 61 | | Russell Greiner,
Adam J. Grove,
Dan Roth:
Learning cost-sensitive active classifiers.
Artif. Intell. 139(2): 137-174 (2002) |
| 60 | | Dan Roth,
Ming-Hsuan Yang,
Narendra Ahuja:
Learning to Recognize Three-Dimensional Objects.
Neural Computation 14(5): 1071-1103 (2002) |
| 2001 |
|---|
| 59 | | Ashutosh Garg,
Dan Roth:
Learning Coherent Concepts.
ALT 2001: 135-150 |
| 58 | | Ashutosh Garg,
Dan Roth:
Understanding Probabilistic Classifiers.
ECML 2001: 179-191 |
| 57 | | Andrew J. Carlson,
Jeffrey Rosen,
Dan Roth:
Scaling Up Context-Sensitive Text Correction.
IAAI 2001: 45-50 |
| 56 | | Ming-Hsuan Yang,
Dan Roth,
Narendra Ahuja:
Face detection using large margin classifiers.
ICIP (2) 2001: 665-668 |
| 55 | | Dan Roth,
Wen-tau Yih:
Relational Learning via Propositional Algorithms: An Information Extraction Case Study.
IJCAI 2001: 1257-1263 |
| 54 | | John S. Chuang,
Dan Roth:
Gene recognition based on DAG shortest paths.
ISMB (Supplement of Bioinformatics) 2001: 56-64 |
| 53 | | Roni Khardon,
Dan Roth,
Rocco A. Servedio:
Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms.
NIPS 2001: 423-430 |
| 52 | | Dan Roth,
Gio Kao Kao,
Xin Li,
Ramya Nagarajan,
Vasin Punyakanok,
Nick Rizzolo,
Wen-tau Yih,
Cecilia Ovesdotter Alm,
Liam Gerard Moran:
Learning Components for A Question-Answering System.
TREC 2001 |
| 51 | | Yair Even-Zohar,
Dan Roth:
A Sequential Model for Multi-Class Classification
CoRR cs.AI/0106044: (2001) |
| 50 | | Vasin Punyakanok,
Dan Roth:
The Use of Classifiers in Sequential Inference
CoRR cs.LG/0111003: (2001) |
| 49 | | Adam J. Grove,
Dan Roth:
Linear Concepts and Hidden Variables.
Machine Learning 42(1/2): 123-141 (2001) |
| 2000 |
|---|
| 48 | | Dan Roth,
Dmitry Zelenko:
Toward a Theory of Learning Coherent Concepts.
AAAI/IAAI 2000: 639-644 |
| 47 | | Yair Even-Zohar,
Dan Roth:
A Classification Approach to Word Prediction.
ANLP 2000: 124-131 |
| 46 | | Erik F. Tjong Kim Sang,
Walter Daelemans,
Hervé Déjean,
Rob Koeling,
Yuval Krymolowski,
Vasin Punyakanok,
Dan Roth:
Applying System Combination to Base Noun Phrase Identification.
COLING 2000: 857-863 |
| 45 | | Dan Roth,
Ming-Hsuan Yang,
Narendra Ahuja:
Learning to Recognize Objects.
CVPR 2000: 1724-1731 |
| 44 | | Ming-Hsuan Yang,
Dan Roth,
Narendra Ahuja:
Learning to Recognize 3D Objects with SNoW.
ECCV (1) 2000: 439-454 |
| 43 | | Chad M. Cumby,
Dan Roth:
Relational Representations that Facilitate Learning.
KR 2000: 425-434 |
| 42 | | Vasin Punyakanok,
Dan Roth:
The Use of Classifiers in Sequential Inference.
NIPS 2000: 995-1001 |
| 41 | | Erik F. Tjong Kim Sang,
Walter Daelemans,
Hervé Déjean,
Rob Koeling,
Yuval Krymolowski,
Vasin Punyakanok,
Dan Roth:
Applying System Combination to Base Noun Phrase Identification
CoRR cs.CL/0008012: (2000) |
| 40 | | Yair Even-Zohar,
Dan Roth:
A Classification Approach to Word Prediction
CoRR cs.CL/0009027: (2000) |
| 39 | | Marcia Muñoz,
Vasin Punyakanok,
Dan Roth,
Dav Zimak:
A Learning Approach to Shallow Parsing
CoRR cs.LG/0008022: (2000) |
| 1999 |
|---|
| 38 | | Dan Roth:
Learning in Natural Language.
IJCAI 1999: 898-904 |
| 37 | | Roni Khardon,
Dan Roth,
Leslie G. Valiant:
Relational Learning for NLP using Linear Threshold Elements.
IJCAI 1999: 911-919 |
| 36 | | Ming-Hsuan Yang,
Dan Roth,
Narendra Ahuja:
A SNoW-Based Face Detector.
NIPS 1999: 862-868 |
| 35 | | Dan Roth,
Dmitry Zelenko:
Coherent Concepts, Robust Learning.
SOFSEM 1999: 264-276 |
| 34 | | Roni Khardon,
Heikki Mannila,
Dan Roth:
Reasoning with Examples: Propositional Formulae and Database Dependencies.
Acta Inf. 36(4): 267-286 (1999) |
| 33 | | Andrew R. Golding,
Dan Roth:
A Winnow-Based Approach to Context-Sensitive Spelling Correction.
Machine Learning 34(1-3): 107-130 (1999) |
| 32 | | Roni Khardon,
Dan Roth:
Learning to Reason with a Restricted View.
Machine Learning 35(2): 95-116 (1999) |
| 31 | | Marios Mavronicolas,
Dan Roth:
Linearizable Read/Write Objects.
Theor. Comput. Sci. 220(1): 267-319 (1999) |
| 1998 |
|---|
| 30 | | Dan Roth:
Learning to Resolve Natural Language Ambiguities: A Unified Approach.
AAAI/IAAI 1998: 806-813 |
| 29 | | Dan Roth,
Dmitry Zelenko:
Part of Speech Tagging Using a Network of Linear Separators.
COLING-ACL 1998: 1136-1142 |
| 28 | | Ronen Basri,
Dan Roth,
David W. Jacobs:
Clustering Appearances of 3D Objects.
CVPR 1998: 414-420 |
| 27 | | Dan Roth:
Learning to Resolve Natural Language Ambiguities: A Unified Approach
CoRR cs.CL/9811010: (1998) |
| 26 | | Andrew R. Golding,
Dan Roth:
A Winnow-Based Approach to Context-Sensitive Spelling Correction
CoRR cs.LG/9811003: (1998) |
| 25 | | Howard Aizenstein,
Avrim Blum,
Roni Khardon,
Eyal Kushilevitz,
Leonard Pitt,
Dan Roth:
On Learning Read-k-Satisfy-j DNF.
SIAM J. Comput. 27(6): 1515-1530 (1998) |
| 1997 |
|---|
| 24 | | Dan Roth:
Learning to Perform Knowledge-Intensive Inferences.
MFCS 1997: 108-109 |
| 23 | | Adam J. Grove,
Dan Roth:
Linear Concepts and Hidden Variables: An Empirical Study.
NIPS 1997 |
| 22 | | Roni Khardon,
Dan Roth:
Defaults and Relevance in Model-Based Reasoning.
Artif. Intell. 97(1-2): 169-193 (1997) |
| 21 | | Ido Dagan,
Yael Karov,
Dan Roth:
Mistake-Driven Learning in Text Categorization
CoRR cmp-lg/9706006: (1997) |
| 20 | | Karen L. Daniels,
Victor J. Milenkovic,
Dan Roth:
Finding the Largest Area Axis-parallel Rectangle in a Polygon.
Comput. Geom. 7: 125-148 (1997) |
| 19 | | Roni Khardon,
Dan Roth:
Learning to reason.
J. ACM 44(5): 697-725 (1997) |
| 1996 |
|---|
| 18 | | Dan Roth:
A Connectionist Framework for Reasoning: Reasoning with Examples.
AAAI/IAAI, Vol. 2 1996: 1256-1261 |
| 17 | | Andrew R. Golding,
Dan Roth:
Applying Winnow to Context-Sensitive Spelling Correction.
ICML 1996: 182-190 |
| 16 | | Russell Greiner,
Adam J. Grove,
Dan Roth:
Learning Active Classifiers.
ICML 1996: 207-215 |
| 15 | | Dan Roth:
Learning in Order to Reason: The Approach.
SOFSEM 1996: 113-124 |
| 14 | | Dan Roth:
On the Hardness of Approximate Reasoning.
Artif. Intell. 82(1-2): 273-302 (1996) |
| 13 | | Roni Khardon,
Dan Roth:
Reasoning with Models.
Artif. Intell. 87(1-2): 187-213 (1996) |
| 12 | | Andrew R. Golding,
Dan Roth:
Applying Winnow to Context-Sensitive Spelling Correction
CoRR cmp-lg/9607024: (1996) |
| 11 | | Eyal Kushilevitz,
Dan Roth:
On Learning Visual Concepts and DNF Formulae.
Machine Learning 24(1): 65-85 (1996) |
| 1995 |
|---|
| 10 | | Roni Khardon,
Dan Roth:
Learning to Reason with a Restricted View.
COLT 1995: 301-310 |
| 9 | | Dan Roth:
Learning to Reason: The Non-Monotonic Case.
IJCAI 1995: 1178-1184 |
| 8 | | Roni Khardon,
Dan Roth:
Default-Reasoning with Models.
IJCAI 1995: 319-327 |
| 1994 |
|---|
| 7 | | Roni Khardon,
Dan Roth:
Reasoning with Models.
AAAI 1994: 1148-1153 |
| 6 | | Roni Khardon,
Dan Roth:
Learning to Reason.
AAAI 1994: 682-687 |
| 5 | | Avrim Blum,
Roni Khardon,
Eyal Kushilevitz,
Leonard Pitt,
Dan Roth:
On Learning Read-k-Satisfy-j DNF.
COLT 1994: 110-117 |
| 1993 |
|---|
| 4 | | Karen L. Daniels,
Victor Milenkovic,
Dan Roth:
Finding the Maximum Area Axis-parallel Rectangle in a Polygon.
CCCG 1993: 322-327 |
| 3 | | Eyal Kushilevitz,
Dan Roth:
On Learning Visual Concepts and DNF Formulae.
COLT 1993: 317-326 |
| 2 | | Dan Roth:
On the Hardness of Approximate Reasoning.
IJCAI 1993: 613-619 |
| 1992 |
|---|
| 1 | | Marios Mavronicolas,
Dan Roth:
Efficient, Strongly Consistent Implementations of Shared Memory (Extended Abstract).
WDAG 1992: 346-361 |
