Tejaswini Pedapati

According to our database, Tejaswini Pedapati authored at least 30 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Large Language Models can be Strong Self-Detoxifiers.

Ching-Yun Ko, Pin-Yu Chen, Payel Das, Youssef Mroueh, Soham Dan, Georgios Kollias, Subhajit Chaudhury, Tejaswini Pedapati, Luca Daniel

CoRR, 2024

Aligning Human and LLM Judgments: Insights from EvalAssist on Task-Specific Evaluations and AI-assisted Assessment Strategy Preferences.

Zahra Ashktorab, Michael Desmond, Qian Pan, James M. Johnson, Martin Santillan Cooper, Elizabeth M. Daly, Rahul Nair, Tejaswini Pedapati, Swapnaja Achintalwar, Werner Geyer

CoRR, 2024

TabSketchFM: Sketch-based Tabular Representation Learning for Data Discovery over Data Lakes.

Aamod Khatiwada, Harsha Kokel, Ibrahim Abdelaziz, Subhajit Chaudhury, Julian Dolby, Oktie Hassanzadeh, Zhenhan Huang, Tejaswini Pedapati, Horst Samulowitz, Kavitha Srinivas

CoRR, 2024

Large Language Model Confidence Estimation via Black-Box Access.

Tejaswini Pedapati, Amit Dhurandhar, Soumya Ghosh, Soham Dan, Prasanna Sattigeri

CoRR, 2024

Graph is all you need? Lightweight data-agnostic neural architecture search without training.

Zhenhan Huang, Tejaswini Pedapati, Pin-Yu Chen, Chunheng Jiang, Jianxi Gao

CoRR, 2024

From PEFT to DEFT: Parameter Efficient Finetuning for Reducing Activation Density in Transformers.

Bharat Runwal, Tejaswini Pedapati, Pin-Yu Chen

CoRR, 2024

NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models.

Amit Dhurandhar, Tejaswini Pedapati, Ronny Luss, Soham Dan, Aurélie C. Lozano, Payel Das, Georgios Kollias

Proceedings of the Findings of the Association for Computational Linguistics, 2024

2023

LakeBench: Benchmarks for Data Discovery over Data Lakes.

Kavitha Srinivas, Julian Dolby, Ibrahim Abdelaziz, Oktie Hassanzadeh, Harsha Kokel, Aamod Khatiwada, Tejaswini Pedapati, Subhajit Chaudhury, Horst Samulowitz

CoRR, 2023

MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning.

KrishnaTeja Killamsetty, Alexandre V. Evfimievski, Tejaswini Pedapati, Kiran Kate, Lucian Popa, Rishabh K. Iyer

CoRR, 2023

2022

Neural Capacitance: A New Perspective of Neural Network Selection via Edge Dynamics.

Chunheng Jiang, Tejaswini Pedapati, Pin-Yu Chen, Yizhou Sun, Jianxi Gao

CoRR, 2022

Multihop: Leveraging Complex Models to Learn Accurate Simple Models.

Amit Dhurandhar, Tejaswini Pedapati

Proceedings of the IEEE International Conference on Knowledge Graph, 2022

2021

Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents.

Jasmina Gajcin, Rahul Nair, Tejaswini Pedapati, Radu Marinescu, Elizabeth Daly, Ivana Dusparic

CoRR, 2021

Building Accurate Simple Models with Multihop.

Amit Dhurandhar, Tejaswini Pedapati

CoRR, 2021

CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions.

Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

AutoText: An End-to-End AutoAI Framework for Text.

Arunima Chaudhary, Alayt Issak, Kiran Kate, Yannis Katsis, Abel N. Valente, Dakuo Wang, Alexandre V. Evfimievski, Sairam Gurajada, Ban Kawas, A. Cristiano I. Malossi, Lucian Popa, Tejaswini Pedapati, Horst Samulowitz, Martin Wistuba, Yunyao Li

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Learning Global Transparent Models from Local Contrastive Explanations.

Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar

CoRR, 2020

Learning Global Transparent Models consistent with Local Contrastive Explanations.

Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Automation of Deep Learning - Theory and Practice.

Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati

Proceedings of the 2020 on International Conference on Multimedia Retrieval, 2020

Survey on Automated End-to-End Data Science?

Djallel Bouneffouf, Charu C. Aggarwal, Thanh Hoang, Udayan Khurana, Horst Samulowitz, Beat Buesser, Sijia Liu, Tejaswini Pedapati, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Alexander G. Gray

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Learning to Rank Learning Curves.

Martin Wistuba, Tejaswini Pedapati

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

How can AI Automate End-to-End Data Science?

Charu C. Aggarwal, Djallel Bouneffouf, Horst Samulowitz, Beat Buesser, Thanh Hoang, Udayan Khurana, Sijia Liu, Tejaswini Pedapati, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Alexander G. Gray

CoRR, 2019

Model Agnostic Contrastive Explanations for Structured Data.

Amit Dhurandhar, Tejaswini Pedapati, Avinash Balakrishnan, Pin-Yu Chen, Karthikeyan Shanmugam, Ruchir Puri

CoRR, 2019

A Survey on Neural Architecture Search.

Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati

CoRR, 2019

Inductive Transfer for Neural Architecture Optimization.

Martin Wistuba, Tejaswini Pedapati

CoRR, 2019

NeuNetS: An Automated Synthesis Engine for Neural Network Design.

Atin Sood, Benjamin Elder, Benjamin Herta, Chao Xue, Costas Bekas, A. Cristiano I. Malossi, Debashish Saha, Florian Scheidegger, Ganesh Venkataraman, Gegi Thomas, Giovanni Mariani, Hendrik Strobelt, Horst Samulowitz, Martin Wistuba, Matteo Manica, Mihir R. Choudhury, Rong Yan, Roxana Istrate, Ruchir Puri, Tejaswini Pedapati

CoRR, 2019

2018

Understanding Unequal Gender Classification Accuracy from Face Images.

Vidya Muthukumar, Tejaswini Pedapati, Nalini K. Ratha, Prasanna Sattigeri, Chai-Wah Wu, Brian Kingsbury, Abhishek Kumar, Samuel Thomas, Aleksandra Mojsilovic, Kush R. Varshney

CoRR, 2018

Dataset Evolver: An Interactive Feature Engineering Notebook.

Fatemeh Nargesian, Udayan Khurana, Tejaswini Pedapati, Horst Samulowitz, Deepak S. Turaga

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Foresight: Recommending Visual Insights.

Çagatay Demiralp, Peter J. Haas, Srinivasan Parthasarathy, Tejaswini Pedapati

Proc. VLDB Endow., 2017

Neurology-as-a-Service for the Developing World.

Tejas Dharamsi, Payel Das, Tejaswini Pedapati, Gregory Bramble, Vinod Muthusamy, Horst Samulowitz, Kush R. Varshney, Yuvaraj Rajamanickam, John Thomas, Justin Dauwels

CoRR, 2017

Foresight: Rapid Data Exploration Through Guideposts.

Çagatay Demiralp, Peter J. Haas, Srinivasan Parthasarathy, Tejaswini Pedapati

CoRR, 2017