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Publications - Ishan Jindal 
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Ishan Jindal
Staff Engineer @ Samsung. Former Staff Research Scientist @ IBM Research, alum of ECE @ WSU, EE @ IIT Roorkee and IIE @ KUK. Rumble Fellow @ WSU and DAAD Fellow @ DAAD
- Delhi, India
- ResearchGate
- Github
- Google Scholar
Publications
As a researcher, I have had the opportunity to work across computer vision, natural language processing, machine learning, deep learning, reinforcement learning, and information theory. Below, I categorize my publications by topic.
Natural Language Processing
[EMNLP Findings 2025] Offloaded Reasoning: Efficient Inference for Large Language Models via Modular Reasoning and Refinement
Ishan Jindal, Jayant Taneja, Badrinath Chandana, Vikas Kapur, Sachin Dev Sharma.
[EMNLP Findings 2025] Identifying Noise in Human-Created Datasets using Training Dynamics from Generative Models
Maeda Hanafi, Ishan Jindal, Yannis Katsis, Lucian Popa, Huaiyu Zhu.
[ICML On-Device 2025 (ORAL)] Offloaded Reasoning: Efficient Inference for Large Language Models via Modular Reasoning and Refinement
Ishan Jindal, Jayant Taneja, Badrinath Chandana, Vikas Kapur, Sachin Dev Sharma.
[ICML PUT 2025] Keep the Alignment, Skip the Overhead: Lightweight Instruction Alignment for Continually Trained LLMs.
Ishan Jindal, Badrinath Chandana, Pranjal Bharti, Lakkidi Vinay, Sachin Dev Sharma.
[Journal of DSNA 2025] Applying Linguistic Principles to PropBank Annotation Projection Between Languages.
Myers, Skatje, Ishan Jindal, and Martha Palmer.
[EMNLP Main 2023] Abstractive Open Information Extraction.
Kevin Pei, Ishan Jindal, and Kevin Chen-Chuan Chang.
[EMNLP Findings 2023] Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture
Bingsheng Yao, Ishan Jindal, Lucian Popa, Yannis Katsis, Sayan Ghosh, Lihong He, Yuxuan Lu, Shashank Srivastava, Yunyao Li, James Hendler, and Dakuo Wang.
[ACL Main 2023] When to Use What: An In-Depth Comparative Empirical Analysis of OpenIE Systems for Downstream Applications.
Kevin Pei, Ishan Jindal, and Kevin Chen-Chuan Chang.
[Journal of NEJL 2023] NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Kaustubh Dhole, ... Ishan Jindal, ... 100+ researchers.
[EACL Findings 2023] PriMeSRL-Eval: A Practical Quality Metric for Semantic Role Labeling Systems Evaluation
Ishan Jindal, Alexandre Rademaker, Khoi-Nguyen Tran, Huaiyu Zhu, Hiroshi Kanayama, Marina Danilevsky, Yunyao Li
[NAACL Main 2022] Label Definitions Improve Semantic Role Labeling
Li Zhang, Ishan Jindal, Yunyao Li
[LREC Main 2022] Universal Proposition Bank 2.0.
Ishan Jindal, Alexandre Rademaker, Michał Ulewicz, Linh Ha, Huyen Nguyen, Khoi-Nguyen Tran, Huaiyu Zhu, Yunyao Li
[NAACL SUKI 2022] Is Semantic-aware BERT more Linguistically Aware? A Case Study on Natural Language Inference.
Ling Liu, Ishan Jindal, Yunyao Li
[EMNLP DASH 2022] A Comparative Analysis between Human-in-the-loop Systems and Large Language Models for Pattern Extraction Tasks.
Maeda F. Hanafi, Yannis Katsis, Ishan Jindal, Lucian Popa
[EMNLP Findings 2021] CLAR: A Cross-Lingual Argument Regularizer for Semantic Role Labeling.
Ishan Jindal, Yunyao Li, Siddhartha Brahma, Huaiyu Zhu
Ishan Jindal, Ranit Aharonov, Siddhartha Brahma, Huaiyu Zhu, Yunyao Li
[NAACL Main 2019] An Effective Label Noise Model for DNN Text Classification
Ishan Jindal, Daniel Pressel, Brian Lester, Matthew Nokleby
In North American Chapter of the Association for Computational Linguistics (NAACL 2019)
Computer Vision
[Book Chapter 2020] Deep Neural Networks for Corrupted Labels
Ishan Jindal, Matthew Nokleby, Daniel Pressel, Xuewen Chen, Harpreet Singh
In Book Deep Learning: Concepts and Architectures by Springer
[CVPRW 2019] A Nonlinear, Noise-aware, Quasi-clustering Approach to Learning Deep CNNs from Noisy Labels
Ishan Jindal, Matthew S Nokleby, Daniel Pressel, Xuewen Chen
In IEEE CVPR 2019 Workshop
Ishan Jindal, Matthew Nokleby, Xuewen Chen
In IEEE 16th International Conference on Data Mining (ICDM 2016)
[GlobalSIP 2016] Dynamic Scene Classification Using Convolutional Neural Networks
Aalok Gangopadhyay, Shivam Mani Tripathi, Ishan Jindal, Shanmuganathan Raman
In IEEE Global Conference on Signal and Information Processing (2016)
[IConSIP 2016] Effective Object Tracking in Unstructured Crowd Scenes
Ishan Jindal, Shanmuganathan Raman
In IEEE International Conference on Signal and Information Processing (2016)
[NCVPRIPG 2015] Semantic Description of a Video Using Representative Frames
Ishan Jindal, Shanmuganathan Raman
In IEEE Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (2015)
Reinforcement Learning
[IEEE Big Data 2018] Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-temporal Mining
Ishan Jindal, Zhiwei Tony Qin, Xuewen Chen, Matthew Nokleby, Jieping Ye
[Accepted at MLDM 2017 conference] A Unified Neural Network Approach for Estimating Travel Time and Distance for a Taxi Trip.
Ishan Jindal, Zhiwei Tony Qin, Xuewen Chen, Matthew Nokleby, Jieping Ye
Information Theory
Ishan Jindal, Matthew Nokleby
[Journal 2018] Classification and Representation via Separable Subspaces: Performance Limits and Algorithms
Ishan Jindal, Matthew Nokleby
In IEEE Journal of Selected Topics in Signal Processing
Ishan Jindal, Matthew Nokleby
In IEEE International Symposium on Information Theory (ISIT)
[Asilomar 2017] Fast and Compact Kronecker-Structured Dictionary Learning for Classification and Representation
Ishan Jindal, Matthew Nokleby
In IEEE 51st Asilomar Conference on Signals, Systems, and Computers