Jaime G. Carbonell

Publication Activity (10 Years)

Years Active: 1978-2021
Publications (10 Years): 115

Top Venues

CoRR

AAAI

EMNLP

ECAI

Publications

Vidhisha Balachandran, Artidoro Pagnoni, Jay Yoon Lee, Dheeraj Rajagopal, Jaime G. Carbonell, Yulia Tsvetkov
StructSum: Summarization via Structured Representations. EACL (2021)
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)
Xinyi Wang, Hieu Pham, Paul Michel, Antonios Anastasopoulos, Jaime G. Carbonell, Graham Neubig
Optimizing Data Usage via Differentiable Rewards. ICML (2020)
Zirui Wang, Sanket Vaibhav Mehta, Barnabás Póczos, Jaime G. Carbonell
Efficient Meta Lifelong-Learning with Limited Memory. CoRR (2020)
Ashiqur R. KhudaBukhsh, Shriphani Palakodety, Jaime G. Carbonell
Harnessing Code Switching to Transcend the Linguistic Barrier. CoRR (2020)
Shriphani Palakodety, Ashiqur R. KhudaBukhsh, Jaime G. Carbonell
Hope Speech Detection: A Computational Analysis of the Voice of Peace. ECAI (2020)
Shriphani Palakodety, Ashiqur R. KhudaBukhsh, Jaime G. Carbonell
Voice for the Voiceless: Active Sampling to Detect Comments Supporting the Rohingyas. AAAI (2020)
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)
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)
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 (2020)
Shriphani Palakodety, Ashiqur R. KhudaBukhsh, Jaime G. Carbonell
The Refugee Experience Online: Surfacing Positivity Amidst Hate. ECAI (2020)
Ashiqur R. KhudaBukhsh, Shriphani Palakodety, Jaime G. Carbonell
Harnessing Code Switching to Transcend the Linguistic Barrier. IJCAI (2020)
Shruti Rijhwani, Shuyan Zhou, Graham Neubig, Jaime G. Carbonell
Soft Gazetteers for Low-Resource Named Entity Recognition. ACL (2020)
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 (2020)
Zirui Wang, Sanket Vaibhav Mehta, Barnabás Póczos, Jaime G. Carbonell
Efficient Meta Lifelong-Learning with Limited Memory. EMNLP (1) (2020)
Daegun Won, Peter J. Jansen, Jaime G. Carbonell
Minimizing and Recovering from the Effect of Concept Drift via Feature Selection. ECAI (2020)
Shriphani Palakodety, Ashiqur R. KhudaBukhsh, Jaime G. Carbonell
Mining Insights from Large-Scale Corpora Using Fine-Tuned Language Models. ECAI (2020)
Shruti Rijhwani, Shuyan Zhou, Graham Neubig, Jaime G. Carbonell
Soft Gazetteers for Low-Resource Named Entity Recognition. CoRR (2020)
Shuyan Zhou, Shruti Rijhwani, John Wieting, Jaime G. Carbonell, Graham Neubig
Improving Candidate Generation for Low-resource Cross-lingual Entity Linking. CoRR (2020)
Harsh Jhamtani, Sanket Vaibhav Mehta, Jaime G. Carbonell, Taylor Berg-Kirkpatrick
Learning Rhyming Constraints using Structured Adversaries. EMNLP/IJCNLP (1) (2019)
Ashiqur R. KhudaBukhsh, Jaime G. Carbonell
Expertise drift in referral networks. Auton. Agents Multi Agent Syst. 33 (5) (2019)
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)
Ashiqur R. KhudaBukhsh, Jaime G. Carbonell
Toward Reciprocity-Aware Distributed Learning in Referral Networks. PRICAI (2) (2019)
Junjie Hu, Mengzhou Xia, Graham Neubig, Jaime G. Carbonell
Domain Adaptation of Neural Machine Translation by Lexicon Induction. ACL (1) (2019)
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 (2019)
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)
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, Quoc V. Le
XLNet: Generalized Autoregressive Pretraining for Language Understanding. CoRR (2019)
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, Quoc V. Le
XLNet: Generalized Autoregressive Pretraining for Language Understanding. NeurIPS (2019)
Shriphani Palakodety, Ashiqur R. KhudaBukhsh, Jaime G. Carbonell
Kashmir: A Computational Analysis of the Voice of Peace. CoRR (2019)
Junjie Hu, Mengzhou Xia, Graham Neubig, Jaime G. Carbonell
Domain Adaptation of Neural Machine Translation by Lexicon Induction. CoRR (2019)
Harsh Jhamtani, Sanket Vaibhav Mehta, Jaime G. Carbonell, Taylor Berg-Kirkpatrick
Learning Rhyming Constraints using Structured Adversaries. CoRR (2019)
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 (2019)
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)
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)
Shriphani Palakodety, Ashiqur R. KhudaBukhsh, Jaime G. Carbonell
Voice for the Voiceless: Active Sampling to Detect Comments Supporting the Rohingyas. CoRR (2019)
Petar Stojanov, Mingming Gong, Jaime G. Carbonell, Kun Zhang
Data-Driven Approach to Multiple-Source Domain Adaptation. AISTATS (2019)
Shruti Rijhwani, Jiateng Xie, Graham Neubig, Jaime G. Carbonell
Zero-Shot Neural Transfer for Cross-Lingual Entity Linking. AAAI (2019)
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 (2019)
Zirui Wang, Zihang Dai, Barnabás Póczos, Jaime G. Carbonell
Characterizing and Avoiding Negative Transfer. CVPR (2019)
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 (2019)
Xinyi Wang, Hieu Pham, Paul Michel, Antonios Anastasopoulos, Graham Neubig, Jaime G. Carbonell
Optimizing Data Usage via Differentiable Rewards. CoRR (2019)
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, Noura Farra, Kathleen R. McKeown
The ARIEL-CMU Systems for LoReHLT18. CoRR (2019)
Petar Stojanov, Mingming Gong, Jaime G. Carbonell, Kun Zhang
Low-Dimensional Density Ratio Estimation for Covariate Shift Correction. AISTATS (2019)
Sanket Vaibhav Mehta, Jay Yoon Lee, Jaime G. Carbonell
Towards Semi-Supervised Learning for Deep Semantic Role Labeling. EMNLP (2018)
Zirui Wang, Jaime G. Carbonell
Towards More Reliable Transfer Learning. ECML/PKDD (2) (2018)
Zirui Wang, Jaime G. Carbonell
Towards more Reliable Transfer Learning. CoRR (2018)
Jiateng Xie, Zhilin Yang, Graham Neubig, Noah A. Smith, Jaime G. Carbonell
Neural Cross-Lingual Named Entity Recognition with Minimal Resources. CoRR (2018)
Ashiqur R. KhudaBukhsh, Jaime G. Carbonell
Expertise Drift in Referral Networks. AAMAS (2018)
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 (2018)
George Philipp, Dawn Song, Jaime G. Carbonell
Gradients explode - Deep Networks are shallow - ResNet explained. ICLR (Workshop) (2018)
Sanket Vaibhav Mehta, Jay Yoon Lee, Jaime G. Carbonell
Towards Semi-Supervised Learning for Deep Semantic Role Labeling. CoRR (2018)
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)
Ashiqur R. KhudaBukhsh, Jong Woo Hong, Jaime G. Carbonell
Market-Aware Proactive Skill Posting. ISMIS (2018)
Jiateng Xie, Zhilin Yang, Graham Neubig, Noah A. Smith, Jaime G. Carbonell
Neural Cross-lingual Named Entity Recognition with Minimal Resources. EMNLP (2018)
Ashiqur R. KhudaBukhsh, Jaime G. Carbonell
Endorsement in Referral Networks. EUMAS (2018)
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)
Ashiqur R. KhudaBukhsh, Jaime G. Carbonell, Peter J. Jansen
Robust learning in expert networks: a comparative analysis. J. Intell. Inf. Syst. 51 (2) (2018)
Daegun Won, Peter J. Jansen, Jaime G. Carbonell
Temporal transfer learning for drift adaptation. ESANN (2018)
Abdelwahab Bourai, Jaime G. Carbonell
I Know What You Don't Know: Proactive Learning through Targeted Human Interaction. AAMAS (2018)
Shruti Rijhwani, Jiateng Xie, Graham Neubig, Jaime G. Carbonell
Zero-shot Neural Transfer for Cross-lingual Entity Linking. CoRR (2018)
Jesse Dunietz, Jaime G. Carbonell, Lori S. Levin
DeepCx: A transition-based approach for shallow semantic parsing with complex constructional triggers. EMNLP (2018)
Zirui Wang, Zihang Dai, Barnabás Póczos, Jaime G. Carbonell
Characterizing and Avoiding Negative Transfer. CoRR (2018)
George Philipp, Jaime G. Carbonell
The Nonlinearity Coefficient - Predicting Overfitting in Deep Neural Networks. CoRR (2018)
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 (2018)
George Philipp, Jaime G. Carbonell
Nonparametric Neural Networks. CoRR (2017)
Guoqing Zheng, Yiming Yang, Jaime G. Carbonell
Likelihood Almost Free Inference Networks. CoRR (2017)
Jesse Dunietz, Lori S. Levin, Jaime G. Carbonell
Automatically Tagging Constructions of Causation and Their Slot-Fillers. Trans. Assoc. Comput. Linguistics 5 (2017)
Ashiqur R. KhudaBukhsh, Jaime G. Carbonell, Peter J. Jansen
Incentive Compatible Proactive Skill Posting in Referral Networks. EUMAS/AT (2017)
Meghana Kshirsagar, Keerthiram Murugesan, Jaime G. Carbonell, Judith Klein-Seetharaman
Multitask Matrix Completion for Learning Protein Interactions Across Diseases. J. Comput. Biol. 24 (6) (2017)
George Philipp, Jaime G. Carbonell
Nonparametric Neural Networks. ICLR (Poster) (2017)
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) (2017)
Adams Wei Yu, Qihang Lin, Ruslan Salakhutdinov, Jaime G. Carbonell
Normalized Gradient with Adaptive Stepsize Method for Deep Neural Network Training. CoRR (2017)
Keerthiram Murugesan, Jaime G. Carbonell
Multi-Task Multiple Kernel Relationship Learning. SDM (2017)
Keerthiram Murugesan, Jaime G. Carbonell, Yiming Yang
Co-Clustering for Multitask Learning. CoRR (2017)
Ashiqur R. KhudaBukhsh, Jaime G. Carbonell, Peter J. Jansen
Robust Learning in Expert Networks: A Comparative Analysis. ISMIS (2017)
Keerthiram Murugesan, Jaime G. Carbonell
Self-Paced Multitask Learning with Shared Knowledge. IJCAI (2017)
Jesse Dunietz, Lori S. Levin, Jaime G. Carbonell
The BECauSE Corpus 2.0: Annotating Causality and Overlapping Relations. LAW@ACL (2017)
Jay Yoon Lee, Michael L. Wick, Jean-Baptiste Tristan, Jaime G. Carbonell
Enforcing Constraints on Outputs with Unconstrained Inference. CoRR (2017)
Andrew Hsi, Jaime G. Carbonell, Yiming Yang
CMU CS Event TAC-KBP2017 Event Argument Extraction System. TAC (2017)
Keerthiram Murugesan, Jaime G. Carbonell
Active Learning from Peers. NIPS (2017)
George Philipp, Dawn Song, Jaime G. Carbonell
Gradients explode - Deep Networks are shallow - ResNet explained. CoRR (2017)
Keerthiram Murugesan, Jaime G. Carbonell
Self-Paced Multitask Learning with Shared Knowledge. CoRR (2017)
Guoqing Zheng, Yiming Yang, Jaime G. Carbonell
Convolutional Normalizing Flows. CoRR (2017)
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)
Sz-Rung Shiang, Stephanie Rosenthal, Anatole Gershman, Jaime G. Carbonell, Jean Oh
Vision-Language Fusion for Object Recognition. AAAI (2017)
Seungwhan Moon, Jaime G. Carbonell
Completely Heterogeneous Transfer Learning with Attention - What And What Not To Transfer. IJCAI (2017)
Guoqing Zheng, Yiming Yang, Jaime G. Carbonell
Efficient Shift-Invariant Dictionary Learning. KDD (2016)
Ashiqur R. KhudaBukhsh, Peter J. Jansen, Jaime G. Carbonell
Distributed Learning in Expert Referral Networks. ECAI (2016)
Jeffrey Flanigan, Chris Dyer, Noah A. Smith, Jaime G. Carbonell
Generation from Abstract Meaning Representation using Tree Transducers. HLT-NAACL (2016)
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 (2016)
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)
Keerthiram Murugesan, Jaime G. Carbonell
Multi-Task Multiple Kernel Relationship Learning. CoRR (2016)
Andrew Hsi, Yiming Yang, Jaime G. Carbonell, Ruochen Xu
Leveraging Multilingual Training for Limited Resource Event Extraction. COLING (2016)
Andrew Hsi, Jaime G. Carbonell, Yiming Yang
CMU CS Event TAC-KBP2016 Event Argument Extraction System. TAC (2016)
Hanxiao Liu, Wanli Ma, Yiming Yang, Jaime G. Carbonell
Learning Concept Graphs from Online Educational Data. J. Artif. Intell. Res. 55 (2016)
Keerthiram Murugesan, Hanxiao Liu, Jaime G. Carbonell, Yiming Yang
Adaptive Smoothed Online Multi-Task Learning. NIPS (2016)
Seungwhan Moon, Jaime G. Carbonell
Proactive Transfer Learning for Heterogeneous Feature and Label Spaces. ECML/PKDD (2) (2016)
Meghana Kshirsagar, Jaime G. Carbonell, Judith Klein-Seetharaman, Keerthiram Murugesan
Multitask Matrix Completion for Learning Protein Interactions Across Diseases. RECOMB (2016)
Ashiqur R. KhudaBukhsh, Jaime G. Carbonell, Peter J. Jansen
Proactive Skill Posting in Referral Networks. Australasian Conference on Artificial Intelligence (2016)
Devendra Singh Chaplot, Yiming Yang, Jaime G. Carbonell, Kenneth R. Koedinger
Data-driven Automated Induction of Prerequisite Structure Graphs. EDM (2016)

Jaime G. Carbonell.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Jaime G. Carbonell