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ORCIDJaime G. Carbonell
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- affiliation: Carnegie Mellon University, Language Technologies Institute, Pittsburgh, PA, USA
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2020 – today
- 2021
[c257]Vidhisha Balachandran, Artidoro Pagnoni, Jay Yoon Lee, Dheeraj Rajagopal, Jaime G. Carbonell, Yulia Tsvetkov:
StructSum: Summarization via Structured Representations. EACL 2021: 2575-2585- [c256]Petar Stojanov, Zijian Li, Mingming Gong, Ruichu Cai, Jaime G. Carbonell, Kun Zhang:
Domain Adaptation with Invariant Representation Learning: What Transformations to Learn? NeurIPS 2021: 24791-24803 - 2020
- [j55]Shuyan Zhou, Shruti Rijhwani, John Wieting, Jaime G. Carbonell, Graham Neubig:
Improving Candidate Generation for Low-resource Cross-lingual Entity Linking. Trans. Assoc. Comput. Linguistics 8: 109-124 (2020) - [c255]Shriphani Palakodety
, Ashiqur R. KhudaBukhsh, Jaime G. Carbonell:
Voice for the Voiceless: Active Sampling to Detect Comments Supporting the Rohingyas. AAAI 2020: 454-462 - [c254]Kyungtae Lim, Jay Yoon Lee, Jaime G. Carbonell, Thierry Poibeau:
Semi-Supervised Learning on Meta Structure: Multi-Task Tagging and Parsing in Low-Resource Scenarios. AAAI 2020: 8344-8351 - [c253]Shruti Rijhwani, Shuyan Zhou, Graham Neubig, Jaime G. Carbonell:
Soft Gazetteers for Low-Resource Named Entity Recognition. ACL 2020: 8118-8123 - [c252]Daegun Won, Peter J. Jansen, Jaime G. Carbonell:
Minimizing and Recovering from the Effect of Concept Drift via Feature Selection. ECAI 2020: 1611-1617 - [c251]Shriphani Palakodety, Ashiqur R. KhudaBukhsh, Jaime G. Carbonell:
Hope Speech Detection: A Computational Analysis of the Voice of Peace. ECAI 2020: 1881-1889 - [c250]Shriphani Palakodety, Ashiqur R. KhudaBukhsh, Jaime G. Carbonell:
Mining Insights from Large-Scale Corpora Using Fine-Tuned Language Models. ECAI 2020: 1890-1897 - [c249]Shriphani Palakodety, Ashiqur R. KhudaBukhsh, Jaime G. Carbonell:
The Refugee Experience Online: Surfacing Positivity Amidst Hate. ECAI 2020: 2925-2926 - [c248]Zirui Wang, Sanket Vaibhav Mehta, Barnabás Póczos, Jaime G. Carbonell:
Efficient Meta Lifelong-Learning with Limited Memory. EMNLP (1) 2020: 535-548 - [c247]Zirui Wang, Jiateng Xie, Ruochen Xu, Yiming Yang, Graham Neubig, Jaime G. Carbonell:
Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework. ICLR 2020 - [c246]Xinyi Wang, Hieu Pham, Paul Michel, Antonios Anastasopoulos, Jaime G. Carbonell, Graham Neubig:
Optimizing Data Usage via Differentiable Rewards. ICML 2020: 9983-9995 - [c245]Ashiqur R. KhudaBukhsh, Shriphani Palakodety, Jaime G. Carbonell:
Harnessing Code Switching to Transcend the Linguistic Barrier. IJCAI 2020: 4366-4374 - [i52]Ashiqur R. KhudaBukhsh, Shriphani Palakodety, Jaime G. Carbonell:
Harnessing Code Switching to Transcend the Linguistic Barrier. CoRR abs/2001.11258 (2020) - [i51]Vidhisha Balachandran, Artidoro Pagnoni, Jay Yoon Lee, Dheeraj Rajagopal, Jaime G. Carbonell, Yulia Tsvetkov:
StructSum: Incorporating Latent and Explicit Sentence Dependencies for Single Document Summarization. CoRR abs/2003.00576 (2020) - [i50]Shuyan Zhou, Shruti Rijhwani, John Wieting, Jaime G. Carbonell, Graham Neubig:
Improving Candidate Generation for Low-resource Cross-lingual Entity Linking. CoRR abs/2003.01343 (2020) - [i49]Shruti Rijhwani, Shuyan Zhou, Graham Neubig, Jaime G. Carbonell:
Soft Gazetteers for Low-Resource Named Entity Recognition. CoRR abs/2005.01866 (2020) - [i48]Zirui Wang, Sanket Vaibhav Mehta, Barnabás Póczos, Jaime G. Carbonell:
Efficient Meta Lifelong-Learning with Limited Memory. CoRR abs/2010.02500 (2020)
2010 – 2019
- 2019
- [j54]Ashiqur R. KhudaBukhsh, Jaime G. Carbonell:
Expertise drift in referral networks. Auton. Agents Multi Agent Syst. 33(5): 645-671 (2019) - [c244]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 - [c243]Shruti Rijhwani, Jiateng Xie, Graham Neubig, Jaime G. Carbonell:
Zero-Shot Neural Transfer for Cross-Lingual Entity Linking. AAAI 2019: 6924-6931 - [c242]Zihang Dai, Zhilin Yang, Yiming Yang, Jaime G. Carbonell, Quoc Viet Le, Ruslan Salakhutdinov:
Transformer-XL: Attentive Language Models beyond a Fixed-Length Context. ACL (1) 2019: 2978-2988 - [c241]Junjie Hu, Mengzhou Xia, Graham Neubig, Jaime G. Carbonell:
Domain Adaptation of Neural Machine Translation by Lexicon Induction. ACL (1) 2019: 2989-3001 - [c240]Petar Stojanov, Mingming Gong, Jaime G. Carbonell, Kun Zhang:
Low-Dimensional Density Ratio Estimation for Covariate Shift Correction. AISTATS 2019: 3449-3458 - [c239]Petar Stojanov, Mingming Gong, Jaime G. Carbonell, Kun Zhang:
Data-Driven Approach to Multiple-Source Domain Adaptation. AISTATS 2019: 3487-3496 - [c238]Zirui Wang, Zihang Dai, Barnabás Póczos, Jaime G. Carbonell:
Characterizing and Avoiding Negative Transfer. CVPR 2019: 11293-11302 - [c237]Aditi Chaudhary, Jiateng Xie, Zaid Sheikh, Graham Neubig, Jaime G. Carbonell:
A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers. EMNLP/IJCNLP (1) 2019: 5163-5173 - [c236]Harsh Jhamtani, Sanket Vaibhav Mehta, Jaime G. Carbonell, Taylor Berg-Kirkpatrick:
Learning Rhyming Constraints using Structured Adversaries. EMNLP/IJCNLP (1) 2019: 6024-6030 - [c235]Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, Quoc V. Le:
XLNet: Generalized Autoregressive Pretraining for Language Understanding. NeurIPS 2019: 5754-5764 - [c234]Ashiqur R. KhudaBukhsh, Jaime G. Carbonell:
Toward Reciprocity-Aware Distributed Learning in Referral Networks. PRICAI (2) 2019: 121-135 - [i47]Eduard H. Hovy, Jaime G. Carbonell, Hans Chalupsky, Anatole Gershman, Alex Hauptmann, Florian Metze, Teruko Mitamura, Zaid Sheikh, Ankit Dangi, Aditi Chaudhary, Xianyang Chen, Xiang Kong, Bernie Huang, Salvador Medina, Hector Liu, Xuezhe Ma, Maria Ryskina, Ramon Sanabria, Varun Gangal:
OPERA: Operations-oriented Probabilistic Extraction, Reasoning, and Analysis. TAC 2019 - [i46]Zihang Dai, Zhilin Yang, Yiming Yang, Jaime G. Carbonell, Quoc V. Le, Ruslan Salakhutdinov:
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context. CoRR abs/1901.02860 (2019) - [i45]Aditi Chaudhary, Siddharth Dalmia, Junjie Hu, Xinjian Li, Austin Matthews, Aldrian Obaja Muis, Naoki Otani, Shruti Rijhwani, Zaid Sheikh, Nidhi Vyas, Xinyi Wang, Jiateng Xie, Ruochen Xu, Chunting Zhou, Peter J. Jansen, Yiming Yang, Lori S. Levin, Florian Metze, Teruko Mitamura, David R. Mortensen, Graham Neubig, Eduard H. Hovy, Alan W. Black, Jaime G. Carbonell, Graham Horwood, Shabnam Tafreshi, Mona T. Diab, Efsun Sarioglu Kayi, Noura Farra, Kathleen R. McKeown:
The ARIEL-CMU Systems for LoReHLT18. CoRR abs/1902.08899 (2019) - [i44]Junjie Hu, Mengzhou Xia, Graham Neubig, Jaime G. Carbonell:
Domain Adaptation of Neural Machine Translation by Lexicon Induction. CoRR abs/1906.00376 (2019) - [i43]Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, Quoc V. Le:
XLNet: Generalized Autoregressive Pretraining for Language Understanding. CoRR abs/1906.08237 (2019) - [i42]Aditi Chaudhary, Elizabeth Salesky, Gayatri Bhat, David R. Mortensen, Jaime G. Carbonell, Yulia Tsvetkov:
CMU-01 at the SIGMORPHON 2019 Shared Task on Crosslinguality and Context in Morphology. CoRR abs/1907.10129 (2019) - [i41]Aditi Chaudhary, Jiateng Xie, Zaid Sheikh, Graham Neubig, Jaime G. Carbonell:
A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers. CoRR abs/1908.08983 (2019) - [i40]Harsh Jhamtani, Sanket Vaibhav Mehta, Jaime G. Carbonell, Taylor Berg-Kirkpatrick:
Learning Rhyming Constraints using Structured Adversaries. CoRR abs/1909.06743 (2019) - [i39]Shriphani Palakodety, Ashiqur R. KhudaBukhsh, Jaime G. Carbonell:
Kashmir: A Computational Analysis of the Voice of Peace. CoRR abs/1909.12940 (2019) - [i38]Shriphani Palakodety, Ashiqur R. KhudaBukhsh, Jaime G. Carbonell:
Voice for the Voiceless: Active Sampling to Detect Comments Supporting the Rohingyas. CoRR abs/1910.03206 (2019) - [i37]Zirui Wang, Jiateng Xie, Ruochen Xu, Yiming Yang, Graham Neubig, Jaime G. Carbonell:
Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework. CoRR abs/1910.04708 (2019) - [i36]Xinyi Wang, Hieu Pham, Paul Michel, Antonios Anastasopoulos, Graham Neubig, Jaime G. Carbonell:
Optimizing Data Usage via Differentiable Rewards. CoRR abs/1911.10088 (2019) - 2018
- [j53]Ashiqur R. KhudaBukhsh, Jaime G. Carbonell, Peter J. Jansen:
Robust learning in expert networks: a comparative analysis. J. Intell. Inf. Syst. 51(2): 207-234 (2018) - [j52]Liu Yang, Steve Hanneke, Jaime G. Carbonell:
Bounds on the minimax rate for estimating a prior over a VC class from independent learning tasks. Theor. Comput. Sci. 716: 124-140 (2018) - [c233]Ashiqur R. KhudaBukhsh, Jaime G. Carbonell:
Expertise Drift in Referral Networks. AAMAS 2018: 425-433 - [c232]Abdelwahab Bourai, Jaime G. Carbonell:
I Know What You Don't Know: Proactive Learning through Targeted Human Interaction. AAMAS 2018: 479-487 - [c231]Jiateng Xie, Zhilin Yang, Graham Neubig, Noah A. Smith, Jaime G. Carbonell:
Neural Cross-lingual Named Entity Recognition with Minimal Resources. EMNLP 2018: 369-379 - [c230]Jesse Dunietz, Jaime G. Carbonell, Lori S. Levin:
DeepCx: A transition-based approach for shallow semantic parsing with complex constructional triggers. EMNLP 2018: 1691-1701 - [c229]Aditi Chaudhary, Chunting Zhou, Lori S. Levin, Graham Neubig, David R. Mortensen, Jaime G. Carbonell:
Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations. EMNLP 2018: 3285-3295 - [c228]Sanket Vaibhav Mehta, Jay Yoon Lee, Jaime G. Carbonell:
Towards Semi-Supervised Learning for Deep Semantic Role Labeling. EMNLP 2018: 4958-4963 - [c227]Daegun Won, Peter J. Jansen, Jaime G. Carbonell:
Temporal transfer learning for drift adaptation. ESANN 2018 - [c226]Ashiqur R. KhudaBukhsh, Jaime G. Carbonell:
Endorsement in Referral Networks. EUMAS 2018: 172-187 - [c225]George Philipp, Dawn Song, Jaime G. Carbonell:
Gradients explode - Deep Networks are shallow - ResNet explained. ICLR (Workshop) 2018 - [c224]Ashiqur R. KhudaBukhsh, Jong Woo Hong, Jaime G. Carbonell:
Market-Aware Proactive Skill Posting. ISMIS 2018: 323-332 - [c223]Zirui Wang, Jaime G. Carbonell:
Towards More Reliable Transfer Learning. ECML/PKDD (2) 2018: 794-810 - [i35]Eduard H. Hovy, Taylor Berg-Kirkpatrick, Jaime G. Carbonell, Hans Chalupsky, Anatole Gershman, Alexander G. Hauptmann, Florian Metze, Teruko Mitamura, Aditi Chaudhary, Xianyang Chen, Bernie Po-Yao Huang, Hector Zhengzhong Liu, Xuezhe Ma, Shruti Palaskar, Dheeraj Rajagopal, Maria Ryskina, Ramon Sanabria:
OPERA: Operations-oriented Probabilistic Extraction, Reasoning, and Analysis. TAC 2018 - [i34]George Philipp, Jaime G. Carbonell:
The Nonlinearity Coefficient - Predicting Overfitting in Deep Neural Networks. CoRR abs/1806.00179 (2018) - [i33]Zirui Wang, Jaime G. Carbonell:
Towards more Reliable Transfer Learning. CoRR abs/1807.02235 (2018) - [i32]Aditi Chaudhary, Chunting Zhou, Lori S. Levin, Graham Neubig, David R. Mortensen, Jaime G. Carbonell:
Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations. CoRR abs/1808.09500 (2018) - [i31]Sanket Vaibhav Mehta, Jay Yoon Lee, Jaime G. Carbonell:
Towards Semi-Supervised Learning for Deep Semantic Role Labeling. CoRR abs/1808.09543 (2018) - [i30]Jiateng Xie, Zhilin Yang, Graham Neubig, Noah A. Smith, Jaime G. Carbonell:
Neural Cross-Lingual Named Entity Recognition with Minimal Resources. CoRR abs/1808.09861 (2018) - [i29]Shruti Rijhwani, Jiateng Xie, Graham Neubig, Jaime G. Carbonell:
Zero-shot Neural Transfer for Cross-lingual Entity Linking. CoRR abs/1811.04154 (2018) - [i28]Zirui Wang, Zihang Dai, Barnabás Póczos, Jaime G. Carbonell:
Characterizing and Avoiding Negative Transfer. CoRR abs/1811.09751 (2018) - 2017
- [j51]Meghana Kshirsagar, Keerthiram Murugesan, Jaime G. Carbonell, Judith Klein-Seetharaman:
Multitask Matrix Completion for Learning Protein Interactions Across Diseases. J. Comput. Biol. 24(6): 501-514 (2017) - [j50]Luís Marujo, Ricardo Ribeiro, Anatole Gershman, David Martins de Matos, João Paulo Neto, Jaime G. Carbonell:
Event-based summarization using a centrality-as-relevance model. Knowl. Inf. Syst. 50(3): 945-968 (2017) - [j49]Jaime G. Carbonell, Jade Goldstein:
The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries. SIGIR Forum 51(2): 209-210 (2017) - [j48]Jesse Dunietz, Lori S. Levin, Jaime G. Carbonell:
Automatically Tagging Constructions of Causation and Their Slot-Fillers. Trans. Assoc. Comput. Linguistics 5: 117-133 (2017) - [c222]Sz-Rung Shiang, Stephanie Rosenthal, Anatole Gershman, Jaime G. Carbonell, Jean Oh:
Vision-Language Fusion for Object Recognition. AAAI 2017: 4603-4610 - [c221]Jesse Dunietz, Lori S. Levin, Jaime G. Carbonell:
The BECauSE Corpus 2.0: Annotating Causality and Overlapping Relations. LAW@ACL 2017: 95-104 - [c220]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 - [c219]Ashiqur R. KhudaBukhsh, Jaime G. Carbonell, Peter J. Jansen:
Incentive Compatible Proactive Skill Posting in Referral Networks. EUMAS/AT 2017: 29-43 - [c218]George Philipp, Jaime G. Carbonell:
Nonparametric Neural Networks. ICLR (Poster) 2017 - [c217]Seungwhan Moon, Jaime G. Carbonell:
Completely Heterogeneous Transfer Learning with Attention - What And What Not To Transfer. IJCAI 2017: 2508-2514 - [c216]Keerthiram Murugesan, Jaime G. Carbonell:
Self-Paced Multitask Learning with Shared Knowledge. IJCAI 2017: 2522-2528 - [c215]Ashiqur R. KhudaBukhsh, Jaime G. Carbonell, Peter J. Jansen:
Robust Learning in Expert Networks: A Comparative Analysis. ISMIS 2017: 292-301 - [c214]Keerthiram Murugesan, Jaime G. Carbonell:
Active Learning from Peers. NIPS 2017: 7008-7017 - [c213]Keerthiram Murugesan, Jaime G. Carbonell:
Multi-Task Multiple Kernel Relationship Learning. SDM 2017: 687-695 - [i27]Andrew Hsi, Jaime G. Carbonell, Yiming Yang:
CMU CS Event TAC-KBP2017 Event Argument Extraction System. TAC 2017 - [i26]Keerthiram Murugesan, Jaime G. Carbonell:
Self-Paced Multitask Learning with Shared Knowledge. CoRR abs/1703.00977 (2017) - [i25]Keerthiram Murugesan, Jaime G. Carbonell, Yiming Yang:
Co-Clustering for Multitask Learning. CoRR abs/1703.00994 (2017) - [i24]Adams Wei Yu, Qihang Lin, Ruslan Salakhutdinov, Jaime G. Carbonell:
Normalized Gradient with Adaptive Stepsize Method for Deep Neural Network Training. CoRR abs/1707.04822 (2017) - [i23]Jay Yoon Lee, Michael L. Wick, Jean-Baptiste Tristan, Jaime G. Carbonell:
Enforcing Constraints on Outputs with Unconstrained Inference. CoRR abs/1707.08608 (2017) - [i22]Guoqing Zheng, Yiming Yang, Jaime G. Carbonell:
Convolutional Normalizing Flows. CoRR abs/1711.02255 (2017) - [i21]Guoqing Zheng, Yiming Yang, Jaime G. Carbonell:
Likelihood Almost Free Inference Networks. CoRR abs/1711.08352 (2017) - [i20]George Philipp, Jaime G. Carbonell:
Nonparametric Neural Networks. CoRR abs/1712.05440 (2017) - [i19]George Philipp, Dawn Song, Jaime G. Carbonell:
Gradients explode - Deep Networks are shallow - ResNet explained. CoRR abs/1712.05577 (2017) - 2016
- [j47]Hanxiao Liu, Wanli Ma, Yiming Yang, Jaime G. Carbonell:
Learning Concept Graphs from Online Educational Data. J. Artif. Intell. Res. 55: 1059-1090 (2016) - [j46]Luís Marujo, Wang Ling, Ricardo Ribeiro, Anatole Gershman, Jaime G. Carbonell, David Martins de Matos, João Paulo Neto:
Exploring events and distributed representations of text in multi-document summarization. Knowl. Based Syst. 94: 33-42 (2016) - [c212]Ashiqur R. KhudaBukhsh, Jaime G. Carbonell, Peter J. Jansen:
Proactive Skill Posting in Referral Networks. Australasian Conference on Artificial Intelligence 2016: 585-596 - [c211]Andrew Hsi, Yiming Yang, Jaime G. Carbonell, Ruochen Xu:
Leveraging Multilingual Training for Limited Resource Event Extraction. COLING 2016: 1201-1210 - [c210]Ashiqur R. KhudaBukhsh, Peter J. Jansen, Jaime G. Carbonell:
Distributed Learning in Expert Referral Networks. ECAI 2016: 1620-1621 - [c209]Devendra Singh Chaplot, Yiming Yang, Jaime G. Carbonell, Kenneth R. Koedinger:
Data-driven Automated Induction of Prerequisite Structure Graphs. EDM 2016: 318-323 - [c208]Akash Bharadwaj, David R. Mortensen, Chris Dyer, Jaime G. Carbonell:
Phonologically Aware Neural Model for Named Entity Recognition in Low Resource Transfer Settings. EMNLP 2016: 1462-1472 - [c207]Ashiqur R. KhudaBukhsh, Jaime G. Carbonell, Peter J. Jansen:
Proactive-DIEL in Evolving Referral Networks. EUMAS/AT 2016: 148-156 - [c206]Guoqing Zheng, Yiming Yang, Jaime G. Carbonell:
Efficient Shift-Invariant Dictionary Learning. KDD 2016: 2095-2104 - [c205]Jeffrey Flanigan, Chris Dyer, Noah A. Smith, Jaime G. Carbonell:
Generation from Abstract Meaning Representation using Tree Transducers. HLT-NAACL 2016: 731-739 - [c204]Keerthiram Murugesan, Hanxiao Liu, Jaime G. Carbonell, Yiming Yang:
Adaptive Smoothed Online Multi-Task Learning. NIPS 2016: 4296-4304 - [c203]Seungwhan Moon, Jaime G. Carbonell:
Proactive Transfer Learning for Heterogeneous Feature and Label Spaces. ECML/PKDD (2) 2016: 706-721 - [c202]Meghana Kshirsagar, Jaime G. Carbonell, Judith Klein-Seetharaman, Keerthiram Murugesan:
Multitask Matrix Completion for Learning Protein Interactions Across Diseases. RECOMB 2016: 53-64 - [c201]Jeffrey Flanigan, Chris Dyer, Noah A. Smith, Jaime G. Carbonell:
CMU at SemEval-2016 Task 8: Graph-based AMR Parsing with Infinite Ramp Loss. SemEval@NAACL-HLT 2016: 1202-1206 - [i18]Andrew Hsi, Jaime G. Carbonell, Yiming Yang:
CMU CS Event TAC-KBP2016 Event Argument Extraction System. TAC 2016 - [i17]Keerthiram Murugesan, Jaime G. Carbonell:
Multi-Task Multiple Kernel Relationship Learning. CoRR abs/1611.03427 (2016) - 2015
- [c200]Dishan Gupta, Jaime G. Carbonell, Anatole Gershman, Steve Klein, David Miller:
Unsupervised Phrasal Near-Synonym Generation from Text Corpora. AAAI 2015: 2253-2259 - [c199]Meghana Kshirsagar, Sam Thomson, Nathan Schneider, Jaime G. Carbonell, Noah A. Smith, Chris Dyer:
Frame-Semantic Role Labeling with Heterogeneous Annotations. ACL (2) 2015: 218-224 - [c198]Luís Marujo, Wang Ling, Isabel Trancoso, Chris Dyer, Alan W. Black, Anatole Gershman, David Martins de Matos, João Paulo da Silva Neto, Jaime G. Carbonell:
Automatic Keyword Extraction on Twitter. ACL (2) 2015: 637-643 - [c197]Jesse Dunietz, Lori S. Levin, Jaime G. Carbonell:
Annotating Causal Language Using Corpus Lexicography of Constructions. LAW@NAACL-HLT 2015: 188-196 - [c196]Liu Yang, Steve Hanneke, Jaime G. Carbonell:
Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks. ALT 2015: 270-284 - [c195]Öznur Tastan, Yanjun Qi, Jaime G. Carbonell, Judith Klein-Seetharaman:
Refining Literature Curated Protein Interactions Using Expert Opinions. Pacific Symposium on Biocomputing 2015: 318-329 - [c194]Luís Marujo, Ricardo Ribeiro, David Martins de Matos, João Paulo Neto, Anatole Gershman, Jaime G. Carbonell:
Extending a Single-Document Summarizer to Multi-Document: a Hierarchical Approach. *SEM@NAACL-HLT 2015: 176-181 - [c193]Yiming Yang, Hanxiao Liu, Jaime G. Carbonell, Wanli Ma:
Concept Graph Learning from Educational Data. WSDM 2015: 159-168 - [i16]Andrew Hsi, Jaime G. Carbonell, Yiming Yang:
Modeling Event Extraction via Multilingual Data Sources. TAC 2015 - [i15]Liu Yang, Steve Hanneke, Jaime G. Carbonell:
Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks. CoRR abs/1505.05231 (2015) - [i14]Luís Marujo, Ricardo Ribeiro, David Martins de Matos, João Paulo Neto, Anatole Gershman, Jaime G. Carbonell:
Extending a Single-Document Summarizer to Multi-Document: a Hierarchical Approach. CoRR abs/1507.02907 (2015) - [i13]Luís Marujo, José Portelo, Wang Ling, David Martins de Matos, João Paulo Neto, Anatole Gershman, Jaime G. Carbonell, Isabel Trancoso, Bhiksha Raj:
Privacy-Preserving Multi-Document Summarization. CoRR abs/1508.01420 (2015) - [i12]Adams Wei Yu, Wanli Ma, Yaoliang Yu, Jaime G. Carbonell, Suvrit Sra:
Efficient Structured Matrix Rank Minimization. CoRR abs/1509.02447 (2015) - 2014
- [c192]Chung-Chi Huang, Maxine Eskénazi, Jaime G. Carbonell, Lun-Wei Ku, Ping-Che Yang:
Cross-Lingual Information to the Rescue in Keyword Extraction. ACL (System Demonstrations) 2014: 1-6 - [c191]Jeffrey Flanigan, Sam Thomson, Jaime G. Carbonell, Chris Dyer, Noah A. Smith:
A Discriminative Graph-Based Parser for the Abstract Meaning Representation. ACL (1) 2014: 1426-1436 - [c190]Seungwhan Moon, Jaime G. Carbonell:
Proactive learning with multiple class-sensitive labelers. DSAA 2014: 32-38 - [c189]Ashiqur R. KhudaBukhsh, Jaime G. Carbonell, Peter J. Jansen:
Detecting Non-Adversarial Collusion in Crowdsourcing. HCOMP 2014: 104-111 - [c188]Adams Wei Yu, Fatma Kilinç-Karzan, Jaime G. Carbonell:
Saddle Points and Accelerated Perceptron Algorithms. ICML 2014: 1827-1835 - [c187]Luís Marujo, João Paulo Carvalho, Anatole Gershman, Jaime G. Carbonell, João Paulo Neto, David Martins de Matos:
Textual Event Detection Using Fuzzy Fingerprints. IEEE Conf. on Intelligent Systems (1) 2014: 825-836 - [c186]Lori S. Levin, Teruko Mitamura, Brian MacWhinney, Davida Fromm, Jaime G. Carbonell, Weston Feely, Robert E. Frederking, Anatole Gershman, Carlos Ramírez:
Resources for the Detection of Conventionalized Metaphors in Four Languages. LREC 2014: 498-501 - [c185]Adams Wei Yu, Wanli Ma, Yaoliang Yu, Jaime G. Carbonell, Suvrit Sra:
Efficient Structured Matrix Rank Minimization. NIPS 2014: 1350-1358 - [c184]