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ORCIDJay Pujara
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- affiliation: University of California, Santa Cruz, USA
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2020 – today
- 2024
[j7]Ju-Hyung Lee
, Dong-Ho Lee, Joohan Lee, Jay Pujara:
Integrating Pre-Trained Language Model With Physical Layer Communications. IEEE Trans. Wirel. Commun. 23(11): 17266-17278 (2024)- [c68]Saurav Joshi, Filip Ilievski, Jay Pujara:
Knowledge-Powered Recommendation for an Improved Diet Water Footprint. AAAI 2024: 23805-23807 - [c67]Pegah Jandaghi, XiangHai Sheng, Xinyi Bai, Jay Pujara, Hakim Sidahmed:
Faithful Persona-based Conversational Dataset Generation with Large Language Models. ACL (Findings) 2024: 15245-15270 - [c66]Kexuan Sun, Nicolaas Paul Jedema, Karishma Sharma, Ruben Janssen, Jay Pujara, Pedro A. Szekely, Alessandro Moschitti:
Efficient and Accurate Contextual Re-Ranking for Knowledge Graph Question Answering. LREC/COLING 2024: 5585-5595 - [c65]Kian Ahrabian, Alon Benhaim, Barun Patra, Jay Pujara, Saksham Singhal, Xia Song:
On the Adaptation of Unlimiformer for Decoder-Only Transformers. LREC/COLING 2024: 12395-12402 - [c64]Yifan Jiang, Jiarui Zhang, Kexuan Sun, Zhivar Sourati, Kian Ahrabian, Kaixin Ma, Filip Ilievski, Jay Pujara:
MARVEL: Multidimensional Abstraction and Reasoning through Visual Evaluation and Learning. NeurIPS 2024 - [i41]Kian Ahrabian, Zhivar Sourati, Kexuan Sun, Jiarui Zhang, Yifan Jiang, Fred Morstatter, Jay Pujara:
The Curious Case of Nonverbal Abstract Reasoning with Multi-Modal Large Language Models. CoRR abs/2401.12117 (2024) - [i40]Pei Zhou, Jay Pujara, Xiang Ren, Xinyun Chen, Heng-Tze Cheng, Quoc V. Le, Ed H. Chi, Denny Zhou, Swaroop Mishra, Huaixiu Steven Zheng:
Self-Discover: Large Language Models Self-Compose Reasoning Structures. CoRR abs/2402.03620 (2024) - [i39]Ju-Hyung Lee, Dong-Ho Lee, Joohan Lee, Jay Pujara:
Integrating Pre-Trained Language Model with Physical Layer Communications. CoRR abs/2402.11656 (2024) - [i38]Saurav Joshi, Filip Ilievski, Jay Pujara:
Knowledge-Powered Recommendation for an Improved Diet Water Footprint. CoRR abs/2403.17426 (2024) - [i37]Yifan Jiang, Jiarui Zhang, Kexuan Sun, Zhivar Sourati, Kian Ahrabian, Kaixin Ma, Filip Ilievski, Jay Pujara:
MARVEL: Multidimensional Abstraction and Reasoning through Visual Evaluation and Learning. CoRR abs/2404.13591 (2024) - [i36]Kian Ahrabian, Xihui Lin, Barun Patra, Vishrav Chaudhary, Alon Benhaim, Jay Pujara, Xia Song:
The Hitchhiker's Guide to Human Alignment with *PO. CoRR abs/2407.15229 (2024) - [i35]Kian Ahrabian, Casandra Rusti, Ziao Wang, Jay Pujara, Kristina Lerman:
Surprising Resilience of Science During a Global Pandemic: A Large-Scale Descriptive Analysis. CoRR abs/2409.07710 (2024) - [i34]Yifan Jiang, Kriti Aggarwal, Tanmay Laud, Kashif Munir, Jay Pujara, Subhabrata Mukherjee:
RED QUEEN: Safeguarding Large Language Models against Concealed Multi-Turn Jailbreaking. CoRR abs/2409.17458 (2024) - [i33]Kian Ahrabian, Alon Benhaim, Barun Patra, Jay Pujara, Saksham Singhal, Xia Song:
On The Adaptation of Unlimiformer for Decoder-Only Transformers. CoRR abs/2410.01637 (2024) - 2023
- [c63]Xinwei Du, Kian Ahrabian, Arun Baalaaji Sankar Ananthan, Richard Delwin Myloth, Jay Pujara:
Citation Intent Classification Through Weakly Supervised Knowledge Graphs. SDU@AAAI 2023 - [c62]Richard Delwin Myloth, Kian Ahrabian, Arun Baalaaji Sankar Ananthan, Xinwei Du, Jay Pujara:
Is Dynamicity All You Need? SDU@AAAI 2023 - [c61]Dong-Ho Lee, Akshen Kadakia, Brihi Joshi, Aaron Chan, Ziyi Liu, Kiran Narahari, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, Xiang Ren:
XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models. ACL (demo) 2023: 264-273 - [c60]Pei Zhou, Andrew Zhu, Jennifer Hu, Jay Pujara, Xiang Ren, Chris Callison-Burch, Yejin Choi, Prithviraj Ammanabrolu:
I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons. ACL (1) 2023: 11136-11155 - [c59]Ju-Hyung Lee, Dong-Ho Lee, Eunsoo Sheen, Thomas Choi, Jay Pujara:
SEQ2SEQ-SC: End-To-End Semantic Communication Systems with Pre-Trained Language Model. ACSSC 2023: 260-264 - [c58]Dong-Ho Lee, Ravi Kiran Selvam, Sheikh Muhammad Sarwar, Bill Yuchen Lin, Fred Morstatter, Jay Pujara, Elizabeth Boschee, James Allan, Xiang Ren:
AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction. EACL 2023: 3003-3017 - [c57]Dong-Ho Lee, Kian Ahrabian, Woojeong Jin, Fred Morstatter, Jay Pujara:
Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning. EMNLP 2023: 544-557 - [c56]Jihyung Moon, Dong-Ho Lee, Hyundong Cho, Woojeong Jin, Chan Young Park, Minwoo Kim, Jonathan May, Jay Pujara, Sungjoon Park:
Analyzing Norm Violations in Live-Stream Chat. EMNLP 2023: 852-868 - [c55]Avijit Thawani, Saurabh Ghanekar, Xiaoyuan Zhu, Jay Pujara:
Learn Your Tokens: Word-Pooled Tokenization for Language Modeling. EMNLP (Findings) 2023: 9883-9893 - [c54]Dong-Ho Lee, Jay Pujara, Mohit Sewak, Ryen White, Sujay Kumar Jauhar:
Making Large Language Models Better Data Creators. EMNLP 2023: 15349-15360 - [c53]Ana Iglesias-Molina, Kian Ahrabian, Filip Ilievski, Jay Pujara, Óscar Corcho:
Comparison of Knowledge Graph Representations for Consumer Scenarios. ISWC 2023: 271-289 - [p1]Filip Ilievski, Kaixin Ma, Alessandro Oltramari, Peifeng Wang, Jay Pujara:
Building Robust and Explainable AI with Commonsense Knowledge Graphs and Neural Models. Compendium of Neurosymbolic Artificial Intelligence 2023: 178-209 - [i32]Kian Ahrabian, Xinwei Du, Richard Delwin Myloth, Arun Baalaaji Sankar Ananthan, Jay Pujara:
PubGraph: A Large Scale Scientific Temporal Knowledge Graph. CoRR abs/2302.02231 (2023) - [i31]Dong-Ho Lee, Kian Ahrabian, Woojeong Jin, Fred Morstatter, Jay Pujara:
Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning. CoRR abs/2305.10613 (2023) - [i30]Jihyung Moon, Dong-Ho Lee, Hyundong Cho, Woojeong Jin, Chan Young Park, Minwoo Kim, Jonathan May, Jay Pujara, Sungjoon Park:
Analyzing Norm Violations in Live-Stream Chat. CoRR abs/2305.10731 (2023) - [i29]Lee Kezar, Jay Pujara:
Finding Pragmatic Differences Between Disciplines. CoRR abs/2310.00204 (2023) - [i28]Pei Zhou, Aman Madaan, Srividya Pranavi Potharaju, Aditya Gupta, Kevin R. McKee, Ari Holtzman, Jay Pujara, Xiang Ren, Swaroop Mishra, Aida Nematzadeh, Shyam Upadhyay, Manaal Faruqui:
How FaR Are Large Language Models From Agents with Theory-of-Mind? CoRR abs/2310.03051 (2023) - [i27]Avijit Thawani, Jay Pujara, Ashwin Kalyan:
Estimating Numbers without Regression. CoRR abs/2310.06204 (2023) - [i26]Avijit Thawani, Saurabh Ghanekar, Xiaoyuan Zhu, Jay Pujara:
Learn Your Tokens: Word-Pooled Tokenization for Language Modeling. CoRR abs/2310.11628 (2023) - [i25]Dong-Ho Lee, Jay Pujara, Mohit Sewak, Ryen W. White, Sujay Kumar Jauhar:
Making Large Language Models Better Data Creators. CoRR abs/2310.20111 (2023) - [i24]Pegah Jandaghi, XiangHai Sheng, Xinyi Bai, Jay Pujara, Hakim Sidahmed:
Faithful Persona-based Conversational Dataset Generation with Large Language Models. CoRR abs/2312.10007 (2023) - 2022
- [c52]Pei Zhou, Karthik Gopalakrishnan, Behnam Hedayatnia, Seokhwan Kim, Jay Pujara, Xiang Ren, Yang Liu, Dilek Hakkani-Tur:
Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation. ACL (1) 2022: 1237-1252 - [c51]Dong-Ho Lee, Akshen Kadakia, Kangmin Tan, Mahak Agarwal, Xinyu Feng, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, Xiang Ren:
Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER. ACL (1) 2022: 2687-2700 - [c50]Woojeong Jin, Dong-Ho Lee, Chenguang Zhu, Jay Pujara, Xiang Ren:
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-Modal Knowledge Transfer. ACL (1) 2022: 2750-2762 - [c49]Pei Zhou, Hyundong Cho, Pegah Jandaghi, Dong-Ho Lee, Bill Yuchen Lin, Jay Pujara, Xiang Ren:
Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality. EMNLP 2022: 10450-10468 - [c48]Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi, Connor Pryor, Luke Yoffe, Deepak Ramachandran, Lise Getoor, Jay Pujara, William Yang Wang:
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue. EMNLP 2022: 10936-10953 - [c47]Filip Ilievski, Jay Pujara, Kartik Shenoy:
Does Wikidata Support Analogical Reasoning? KGSWC 2022: 178-191 - [c46]Eriq Augustine, Connor Pryor, Charles Dickens, Jay Pujara, William Yang Wang, Lise Getoor:
Visual Sudoku Puzzle Classification: A Suite of Collective Neuro-Symbolic Tasks. NeSy 2022: 15-29 - [c45]Kexuan Sun, Zhiqiang Qiu, Abel Salinas, Yuzhong Huang, Dong-Ho Lee, Daniel Benjamin, Fred Morstatter, Xiang Ren, Kristina Lerman, Jay Pujara:
Assessing Scientific Research Papers with Knowledge Graphs. SIGIR 2022: 2467-2472 - [i23]Ehsan Qasemi, Lee Kezar, Jay Pujara, Pedro A. Szekely:
Evaluating Machine Common Sense via Cloze Testing. CoRR abs/2201.07902 (2022) - [i22]Woojeong Jin, Dong-Ho Lee, Chenguang Zhu, Jay Pujara, Xiang Ren:
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-modal Knowledge Transfer. CoRR abs/2203.07519 (2022) - [i21]Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi, Connor Pryor, Luke Yoffe, Deepak Ramachandran, Lise Getoor, Jay Pujara, William Yang Wang:
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue. CoRR abs/2205.06262 (2022) - [i20]Thiloshon Nagarajah, Filip Ilievski, Jay Pujara:
Understanding Narratives through Dimensions of Analogy. CoRR abs/2206.07167 (2022) - [i19]Filip Ilievski, Jay Pujara, Kartik Shenoy:
Does Wikidata Support Analogical Reasoning? CoRR abs/2210.00620 (2022) - [i18]Ju-Hyung Lee, Dong-Ho Lee, Eunsoo Sheen, Thomas Choi, Jay Pujara, Joongheon Kim:
Seq2Seq-SC: End-to-End Semantic Communication Systems with Pre-trained Language Model. CoRR abs/2210.15237 (2022) - [i17]Dong-Ho Lee, Akshen Kadakia, Brihi Joshi, Aaron Chan, Ziyi Liu, Kiran Narahari, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, Xiang Ren:
XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models. CoRR abs/2210.16978 (2022) - [i16]Pei Zhou, Hyundong Cho, Pegah Jandaghi, Dong-Ho Lee, Bill Yuchen Lin, Jay Pujara, Xiang Ren:
Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality. CoRR abs/2211.09267 (2022) - [i15]Pei Zhou, Andrew Zhu, Jennifer Hu, Jay Pujara, Xiang Ren, Chris Callison-Burch, Yejin Choi, Prithviraj Ammanabrolu:
An AI Dungeon Master's Guide: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons. CoRR abs/2212.10060 (2022) - 2021
- [j6]Majid Ghasemi-Gol, Jay Pujara, Pedro A. Szekely:
Learning cell embeddings for understanding table layouts. Knowl. Inf. Syst. 63(1): 39-64 (2021) - [j5]Yolanda Gil, Daniel Garijo, Deborah Khider, Craig A. Knoblock, Varun Ratnakar, Maximiliano Osorio, Hernán Vargas, Minh Pham, Jay Pujara, Basel Shbita, Binh Vu, Yao-Yi Chiang, Dan Feldman, Yijun Lin, Hayley Song, Vipin Kumar, Ankush Khandelwal, Michael S. Steinbach, Kshitij Tayal, Shaoming Xu, Suzanne A. Pierce, Lissa Pearson, Daniel Hardesty-Lewis, Ewa Deelman, Rafael Ferreira da Silva, Rajiv Mayani, Armen R. Kemanian, Yuning Shi, Lorne Leonard, Scott D. Peckham, Maria Stoica, Kelly M. Cobourn, Zeya Zhang, Christopher J. Duffy, Lele Shu:
Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making. ACM Trans. Interact. Intell. Syst. 11(2): 11:1-11:49 (2021) - [c44]Kexuan Sun, Harsha Rayudu, Jay Pujara:
A Hybrid Probabilistic Approach for Table Understanding. AAAI 2021: 4366-4374 - [c43]Kexuan Sun, Fei Wang, Muhao Chen, Jay Pujara:
Tabular Functional Block Detection with Embedding-based Agglomerative Cell Clustering. CIKM 2021: 1744-1753 - [c42]Fei Wang, Kexuan Sun, Jay Pujara, Pedro A. Szekely, Muhao Chen:
Table-based Fact Verification With Salience-aware Learning. EMNLP (Findings) 2021: 4025-4036 - [c41]Pei Zhou, Pegah Jandaghi, Hyundong Cho, Bill Yuchen Lin, Jay Pujara, Xiang Ren:
Probing Commonsense Explanation in Dialogue Response Generation. EMNLP (Findings) 2021: 4132-4146 - [c40]Ninareh Mehrabi, Pei Zhou, Fred Morstatter, Jay Pujara, Xiang Ren, Aram Galstyan:
Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources. EMNLP (1) 2021: 5016-5033 - [c39]Avijit Thawani, Jay Pujara, Filip Ilievski:
Numeracy enhances the Literacy of Language Models. EMNLP (1) 2021: 6960-6967 - [c38]Pei Zhou, Rahul Khanna, Seyeon Lee, Bill Yuchen Lin, Daniel Ho, Jay Pujara, Xiang Ren:
RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms. EMNLP (1) 2021: 7560-7579 - [c37]Minh Pham, Craig A. Knoblock, Muhao Chen, Binh Vu, Jay Pujara:
SPADE: A Semi-supervised Probabilistic Approach for Detecting Errors in Tables. IJCAI 2021: 3543-3551 - [c36]Jay Pujara, Pedro A. Szekely, Huan Sun, Muhao Chen:
From Tables to Knowledge: Recent Advances in Table Understanding. KDD 2021: 4060-4061 - [c35]Avijit Thawani, Jay Pujara, Filip Ilievski, Pedro A. Szekely:
Representing Numbers in NLP: a Survey and a Vision. NAACL-HLT 2021: 644-656 - [c34]Binh Vu, Craig A. Knoblock, Pedro A. Szekely, Minh Pham, Jay Pujara:
A Graph-Based Approach for Inferring Semantic Descriptions of Wikipedia Tables. ISWC 2021: 304-320 - [c33]Pei Zhou, Karthik Gopalakrishnan, Behnam Hedayatnia, Seokhwan Kim, Jay Pujara, Xiang Ren, Yang Liu, Dilek Hakkani-Tür:
Commonsense-Focused Dialogues for Response Generation: An Empirical Study. SIGDIAL 2021: 121-132 - [c32]Fei Wang, Kexuan Sun, Muhao Chen, Jay Pujara, Pedro A. Szekely:
Retrieving Complex Tables with Multi-Granular Graph Representation Learning. SIGIR 2021: 1472-1482 - [i14]Ninareh Mehrabi, Pei Zhou, Fred Morstatter, Jay Pujara, Xiang Ren, Aram Galstyan:
Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources. CoRR abs/2103.11320 (2021) - [i13]Avijit Thawani, Jay Pujara, Pedro A. Szekely, Filip Ilievski:
Representing Numbers in NLP: a Survey and a Vision. CoRR abs/2103.13136 (2021) - [i12]Pei Zhou, Pegah Jandaghi, Bill Yuchen Lin, Justin Cho, Jay Pujara, Xiang Ren:
Probing Causal Common Sense in Dialogue Response Generation. CoRR abs/2104.09574 (2021) - [i11]Fei Wang, Kexuan Sun, Muhao Chen, Jay Pujara, Pedro A. Szekely:
Retrieving Complex Tables with Multi-Granular Graph Representation Learning. CoRR abs/2105.01736 (2021) - [i10]Fei Wang, Kexuan Sun, Jay Pujara, Pedro A. Szekely, Muhao Chen:
Table-based Fact Verification with Salience-aware Learning. CoRR abs/2109.04053 (2021) - [i9]Dong-Ho Lee, Ravi Kiran Selvam, Sheikh Muhammad Sarwar, Bill Yuchen Lin, Mahak Agarwal, Fred Morstatter, Jay Pujara, Elizabeth Boschee, James Allan, Xiang Ren:
AutoTriggER: Named Entity Recognition with Auxiliary Trigger Extraction. CoRR abs/2109.04726 (2021) - [i8]Pei Zhou, Karthik Gopalakrishnan, Behnam Hedayatnia, Seokhwan Kim, Jay Pujara, Xiang Ren, Yang Liu, Dilek Hakkani-Tur:
Commonsense-Focused Dialogues for Response Generation: An Empirical Study. CoRR abs/2109.06427 (2021) - [i7]Dong-Ho Lee, Mahak Agarwal, Akshen Kadakia, Jay Pujara, Xiang Ren:
Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER. CoRR abs/2110.08454 (2021) - [i6]Pei Zhou, Karthik Gopalakrishnan, Behnam Hedayatnia, Seokhwan Kim, Jay Pujara, Xiang Ren, Yang Liu, Dilek Hakkani-Tur:
Think Before You Speak: Using Self-talk to Generate Implicit Commonsense Knowledge for Response Generation. CoRR abs/2110.08501 (2021) - 2020
- [j4]Pigi Kouki, James Schaffer, Jay Pujara, John O'Donovan, Lise Getoor:
Generating and Understanding Personalized Explanations in Hybrid Recommender Systems. ACM Trans. Interact. Intell. Syst. 10(4): 31:1-31:40 (2020) - [e2]Douglas Burdick, Jay Pujara:
Proceedings of the Sixth International Workshop on Data Science for Macro-Modeling, DSMM 2020, In conjunction with the ACM SIGMOD/PODS Conference, Portland, OR, USA, June 14, 2020. ACM 2020, ISBN 978-1-4503-8030-0 [contents] - [i5]Pegah Jandaghi, Jay Pujara:
Human-like Time Series Summaries via Trend Utility Estimation. CoRR abs/2001.05665 (2020) - [i4]Pei Zhou, Rahul Khanna, Bill Yuchen Lin, Daniel Ho, Xiang Ren, Jay Pujara:
Can BERT Reason? Logically Equivalent Probes for Evaluating the Inference Capabilities of Language Models. CoRR abs/2005.00782 (2020)
2010 – 2019
- 2019
- [j3]Jay Pujara, Kartik D. Kothari, Ashish V. Gohil:
Statistical investigation of surface roughness and kerf on wire electrical discharge machining performance. Int. J. Manuf. Res. 14(3): 231-244 (2019) - [j2]Pigi Kouki, Jay Pujara, Christopher Marcum, Laura M. Koehly, Lise Getoor:
Collective entity resolution in multi-relational familial networks. Knowl. Inf. Syst. 61(3): 1547-1581 (2019) - [c31]Minh Pham, Craig A. Knoblock, Jay Pujara:
Learning Data Transformations with Minimal User Effort. IEEE BigData 2019: 657-664 - [c30]Majid Ghasemi-Gol, Jay Pujara, Pedro A. Szekely:
Tabular Cell Classification Using Pre-Trained Cell Embeddings. ICDM 2019: 230-239 - [c29]Daniel Garijo, Deborah Khider, Varun Ratnakar, Yolanda Gil, Ewa Deelman, Rafael Ferreira da Silva, Craig A. Knoblock, Yao-Yi Chiang, Minh Pham, Jay Pujara, Binh Vu, Dan Feldman, Rajiv Mayani, Kelly M. Cobourn, Christopher J. Duffy, Armen R. Kemanian, Lele Shu, Vipin Kumar, Ankush Khandelwal, Kshitij Tayal, Scott D. Peckham, Maria Stoica, Anna Dabrowski, Daniel Hardesty-Lewis, Suzanne A. Pierce:
An intelligent interface for integrating climate, hydrology, agriculture, and socioeconomic models. IUI Companion 2019: 111-112 - [c28]Pigi Kouki, James Schaffer, Jay Pujara, John O'Donovan, Lise Getoor:
Personalized explanations for hybrid recommender systems. IUI 2019: 379-390 - [c27]Binh Vu, Jay Pujara, Craig A. Knoblock:
D-REPR: A Language for Describing and Mapping Diversely-Structured Data Sources to RDF. K-CAP 2019: 189-196 - [c26]Pedro A. Szekely, Daniel Garijo, Divij Bhatia, Jiasheng Wu, Yixiang Yao, Jay Pujara:
T2WML: Table To Wikidata Mapping Language. K-CAP 2019: 267-270 - [c25]Avijit Thawani, Minda Hu, Erdong Hu, Husain Zafar, Naren Teja Divvala, Amandeep Singh, Ehsan Qasemi, Pedro A. Szekely, Jay Pujara:
Entity Linking to Knowledge Graphs to Infer Column Types and Properties. SemTab@ISWC 2019: 25-32 - [c24]Pedro A. Szekely, Daniel Garijo, Jay Pujara, Divij Bhatia, Jiasheng Wu:
T2WML: A Cell-Based Language to Map Tables into Wikidata Records. ISWC (Satellites) 2019: 45-48 - [c23]Jay Pujara, Arunkumar Rajendran, Majid Ghasemi-Gol, Pedro A. Szekely:
A Common Framework for Developing Table Understanding Models. ISWC (Satellites) 2019: 133-136 - [c22]Louiqa Raschid, Douglas Burdick, Cesar de Pablo, Mark D. Flood, John Grant, Joe Langsam, Jay Pujara, Elena Tomas, Ian Soboroff:
Financial Entity Identification and Information Integration (FEIII) 2019 Challenge: The Report of the Organizing Committee. DSMM@SIGMOD 2019: 6:1-6:3 - [c21]Yixiang Yao, Pedro A. Szekely, Jay Pujara:
Extensible and Scalable Entity Resolution for Financial Datasets Using RLTK. DSMM@SIGMOD 2019: 11:1 - [c20]Binh Vu, Craig A. Knoblock, Jay Pujara:
Learning Semantic Models of Data Sources Using Probabilistic Graphical Models. WWW 2019: 1944-1953 - 2018
- [c19]Dhanya Sridhar, Jay Pujara, Lise Getoor:
Scalable Probabilistic Causal Structure Discovery. IJCAI 2018: 5112-5118 - [c18]Rahul Gupta, Jay Pujara, Craig A. Knoblock, Shushyam M. Sharanappa, Bharat Pulavarti, Gerard Hoberg, Gordon Phillips:
Feature Selection Methods For Understanding Business Competitor Relationships. DSMM@SIGMOD 2018: 2:1-2:6 - [c17]Louiqa Raschid, Douglas Burdick, John Grant, Joe Langsam, Jay Pujara, Elizabeth Roman, Ian Soboroff, Mohammed J. Zaki, Elena Zotkina:
Financial Entity Identification and Information Integration (FEIII) 2018 Challenge: The Report of the Organizing Committee. DSMM@SIGMOD 2018: 9:1-9:3 - [c16]Jay Pujara:
Hybrid Link Prediction for Competitor Relationships. DSMM@SIGMOD 2018: 14:1-14:4 - [c15]Jay Pujara, Sameer Singh:
Mining Knowledge Graphs From Text. WSDM 2018: 789-790 - 2017
- [c14]Dhanya Sridhar, Jay Pujara, Lise Getoor:
Using Noisy Extractions to Discover Causal Knowledge. AKBC@NIPS 2017 - [c13]Jay Pujara, Eriq Augustine, Lise Getoor:
Sparsity and Noise: Where Knowledge Graph Embeddings Fall Short. EMNLP 2017: 1751-1756 - [c12]Pigi Kouki, Jay Pujara, Christopher Marcum, Laura M. Koehly, Lise Getoor:
Collective Entity Resolution in Familial Networks. ICDM 2017: 227-236 - [c11]Sabina Tomkins, Jay Pujara, Lise Getoor:
Disambiguating Energy Disaggregation: A Collective Probabilistic Approach. IJCAI 2017: 2857-2863 - [c10]Pigi Kouki, James Schaffer, Jay Pujara, John O'Donovan, Lise Getoor:
User Preferences for Hybrid Explanations. RecSys 2017: 84-88 - [c9]Jay Pujara:
Extracting Knowledge Graphs from Financial Filings: Extended Abstract. DSMM@SIGMOD 2017: 5:1-5:2 - [c8]Sungchul Kim, Nikhil Kini, Jay Pujara, Eunyee Koh, Lise Getoor:
Probabilistic Visitor Stitching on Cross-Device Web Logs. WWW 2017: 1581-1589 - [i3]Dhanya Sridhar, Jay Pujara, Lise Getoor:
Using Noisy Extractions to Discover Causal Knowledge. CoRR abs/1711.05900 (2017) - 2016
- [b1]Jay Pujara:
Probabilistic Models for Scalable Knowledge Graph Construction. University of Maryland, College Park, MD, USA, 2016 - [c7]Shachi H. Kumar, Jay Pujara, Lise Getoor, David Mares, Dipak Gupta, Ellen Riloff:
Unsupervised models for predicting strategic relations between organizations. ASONAM 2016: 711-718 - [e1]Jay Pujara, Tim Rocktäschel, Danqi Chen, Sameer Singh:
Proceedings of the 5th Workshop on Automated Knowledge Base Construction, AKBC@NAACL-HLT 2016, San Diego, CA, USA, June 17, 2016. The Association for Computer Linguistics 2016, ISBN 978-1-941643-53-2 [contents] - [i2]Shobeir Fakhraei, Dhanya Sridhar, Jay Pujara, Lise Getoor:
Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks. CoRR abs/1607.00474 (2016) - [i1]Jay Pujara, Lise Getoor:
Generic Statistical Relational Entity Resolution in Knowledge Graphs. CoRR abs/1607.00992 (2016) - 2015
- [j1]Jay Pujara, Hui Miao, Lise Getoor, William W. Cohen:
Using Semantics and Statistics to Turn Data into Knowledge. AI Mag. 36(1): 65-74 (2015) - [c6]Adam Grycner, Gerhard Weikum, Jay Pujara, James R. Foulds, Lise Getoor:
RELLY: Inferring Hypernym Relationships Between Relational Phrases. EMNLP 2015: 971-981 - [c5]Jay Pujara, Ben London, Lise Getoor:
Budgeted Online Collective Inference. UAI 2015: 712-721 - 2013
- [c4]Jay Pujara, Hui Miao, Lise Getoor, William W. Cohen:
Large-Scale Knowledge Graph Identification Using PSL. AAAI Fall Symposia 2013 - [c3]Jay Pujara, Hui Miao, Lise Getoor, William W. Cohen:
Ontology-aware partitioning for knowledge graph identification. AKBC@CIKM 2013: 19-24 - [c2]Jay Pujara, Hui Miao, Lise Getoor, William W. Cohen:
Knowledge Graph Identification. ISWC (1) 2013: 542-557 - 2011
- [c1]Jay Pujara, Hal Daumé III, Lise Getoor:
Using classifier cascades for scalable e-mail classification. CEAS 2011: 55-63
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Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
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Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
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Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
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Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
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OpenAlex data
Load additional information about publications from 
.
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last updated on 2025-02-10 21:40 CET by the dblp team
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