Publications

Foundation Models & Agents | Natural Language Processing | Computer Vision | Reinforcement Learning | Information Theory

My research bridges multiple disciplines within artificial intelligence—from foundation models and agentic systems to natural language processing, computer vision, reinforcement learning, and information theory. Below, I categorize my publications by topic.

Foundation Models & Agents

[ICML SCALE 2026 (ORAL)] Exploratory and Assimilating Reflection: Reflective Recall Cycle for Long-term Memory

Ganesh S, Moyuru Yamada, Ishan Jindal, Kiran Purohit

(Outstanding Paper Runner-Up Award)

[ICLR 2026] The Path of Least Resistance: Guiding LLM Reasoning Trajectories with Prefix Consensus

Ishan Jindal, Sai Prashanth Akuthota, Jayant Taneja, SACHIN DEV SHARMA

[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.

Natural Language Processing

[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

PDF Code Project

[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

PDF Code Project

[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

[Preprint 2020] Improved Semantic Role Labeling using Parameterized Neighborhood Memory Adaptation.

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

[ICDM 2016] Learning Deep Networks from Noisy Labels with Dropout Regularization

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

[ICASSP 2019] Tensor Matched Kronecker-Structured Subspace Detection for Missing Information

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

[ISIT 2017] Performance Limits on the Classification of Kronecker-Structured Models

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

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