Prithviraj Sen

According to our database1, Prithviraj Sen authored at least 48 papers between 2007 and 2023.

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Bibliography

2023
Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations.
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning.
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

2022
A Closer Look at the Calibration of Differentially Private Learners.
CoRR, 2022

Logical Neural Networks for Knowledge Base Completion with Embeddings & Rules.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Deep Indexed Active Learning for Matching Heterogeneous Entity Representations.
Proc. VLDB Endow., 2021

Combining Rules and Embeddings via Neuro-Symbolic AI for Knowledge Base Completion.
CoRR, 2021

Logic Embeddings for Complex Query Answering.
CoRR, 2021

Improving Cross-lingual Text Classification with Zero-shot Instance-Weighting.
Proceedings of the 6th Workshop on Representation Learning for NLP, 2021

Neuro-Symbolic Approaches for Text-Based Policy Learning.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking.
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021

2020
Forecasting in multivariate irregularly sampled time series with missing values.
CoRR, 2020

A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching.
Proceedings of the 2020 International Conference on Management of Data, 2020

A Survey of the State of Explainable AI for Natural Language Processing.
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, 2020

Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification.
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

Exploiting Node Content for Multiview Graph Convolutional Network and Adversarial Regularization.
Proceedings of the 28th International Conference on Computational Linguistics, 2020

2019
SystemER: A Human-in-the-loop System for Explainable Entity Resolution.
Proc. VLDB Endow., 2019

A Study on Interaction in Human-in-the-Loop Machine Learning for Text Analytics.
Proceedings of the Joint Proceedings of the ACM IUI 2019 Workshops co-located with the 24th ACM Conference on Intelligent User Interfaces (ACM IUI 2019), 2019

Learning-Based Methods with Human-in-the-Loop for Entity Resolution.
Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019

Learning Electronic Health Records through Hyperbolic Embedding of Medical Ontologies.
Proceedings of the 10th ACM International Conference on Bioinformatics, 2019

HEIDL: Learning Linguistic Expressions with Deep Learning and Human-in-the-Loop.
Proceedings of the 57th Conference of the Association for Computational Linguistics, 2019

2018
On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML.
Proc. VLDB Endow., 2018

Deep Learning with Apache SystemML.
CoRR, 2018

On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML.
CoRR, 2018

2017
Collective Classification.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Creation and Interaction with Large-scale Domain-Specific Knowledge Bases.
Proc. VLDB Endow., 2017

A Rectangle Mining Method for Understanding the Semantics of Financial Tables.
Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition, 2017

Active Learning for Large-Scale Entity Resolution.
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017

SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning.
Proceedings of the 8th Biennial Conference on Innovative Data Systems Research, 2017

2016
SystemML: Declarative Machine Learning on Spark.
Proc. VLDB Endow., 2016

2014
Hybrid Parallelization Strategies for Large-Scale Machine Learning in SystemML.
Proc. VLDB Endow., 2014

SystemML's Optimizer: Plan Generation for Large-Scale Machine Learning Programs.
IEEE Data Eng. Bull., 2014

2013
Compiling machine learning algorithms with SystemML.
Proceedings of the ACM Symposium on Cloud Computing, SOCC '13, 2013

Community detection in content-sharing social networks.
Proceedings of the Advances in Social Networks Analysis and Mining 2013, 2013

2012
Collective context-aware topic models for entity disambiguation.
Proceedings of the 21st World Wide Web Conference 2012, 2012

2011
Web information extraction using Markov logic networks.
Proceedings of the 20th International Conference on World Wide Web, 2011

Entity disambiguation with hierarchical topic models.
Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011

2010
Collective Classification.
Proceedings of the Encyclopedia of Machine Learning, 2010

Read-Once Functions and Query Evaluation in Probabilistic Databases.
Proc. VLDB Endow., 2010

Representing Large-Scale Uncertainty through Probabilistic Databases.
Proceedings of the 16th International Conference on Management of Data, 2010

2009
Representing and Querying Uncertain Data.
PhD thesis, 2009

PrDB: managing and exploiting rich correlations in probabilistic databases.
VLDB J., 2009

Bisimulation-based Approximate Lifted Inference.
Proceedings of the UAI 2009, 2009

2008
Exploiting shared correlations in probabilistic databases.
Proc. VLDB Endow., 2008

Cost-sensitive learning with conditional Markov networks.
Data Min. Knowl. Discov., 2008

Collective Classification in Network Data.
AI Mag., 2008

2007
Representing Tuple and Attribute Uncertainty in Probabilistic Databases.
Proceedings of the Workshops Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), 2007

Representing and Querying Correlated Tuples in Probabilistic Databases.
Proceedings of the 23rd International Conference on Data Engineering, 2007


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