Foresight: Rapid Data Exploration Through Guideposts. - Çagatay Demiralp; Peter J. Haas; Srinivasan Parthasarathy 002; Tejaswini Pedapati (CoRR, 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: Recommending Visual Insights. - Çagatay Demiralp; Peter J. Haas; Srinivasan Parthasarathy 002; Tejaswini Pedapati (Proc. VLDB Endow., 2017)

Dataset Evolver: An Interactive Feature Engineering Notebook. - Fatemeh Nargesian; Udayan Khurana; Tejaswini Pedapati; Horst Samulowitz; Deepak S. Turaga (AAAI, 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 001; Samuel Thomas 001; Aleksandra Mojsilovic; Kush R. Varshney (CoRR, 2018)

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; et al. (CoRR, 2019)

Inductive Transfer for Neural Architecture Optimization. - Martin Wistuba; Tejaswini Pedapati (CoRR, 2019)

A Survey on Neural Architecture Search. - Martin Wistuba; Ambrish Rawat; Tejaswini Pedapati (CoRR, 2019)

Model Agnostic Contrastive Explanations for Structured Data. - Amit Dhurandhar; Tejaswini Pedapati; Avinash Balakrishnan; Pin-Yu Chen; Karthikeyan Shanmugam; Ruchir Puri (CoRR, 2019)

How can AI Automate End-to-End Data Science? - Charu C. Aggarwal; Djallel Bouneffouf 001; Horst Samulowitz; Beat Buesser; Thanh Hoang; Udayan Khurana; Sijia Liu 001; Tejaswini Pedapati; Parikshit Ram; Ambrish Rawat; Martin Wistuba; Alexander G. Gray (CoRR, 2019)

Learning to Rank Learning Curves. - Martin Wistuba; Tejaswini Pedapati (ICML, 2020)

Survey on Automated End-to-End Data Science? - Djallel Bouneffouf 001; Charu C. Aggarwal; Thanh Hoang; Udayan Khurana; Horst Samulowitz; Beat Buesser; Sijia Liu 001; Tejaswini Pedapati; Parikshit Ram; Ambrish Rawat; Martin Wistuba; Alexander G. Gray (IJCNN, 2020)

Automation of Deep Learning - Theory and Practice. - Martin Wistuba; Ambrish Rawat; Tejaswini Pedapati (ICMR, 2020)

Learning Global Transparent Models consistent with Local Contrastive Explanations. - Tejaswini Pedapati; Avinash Balakrishnan; Karthikeyan Shanmugam; Amit Dhurandhar (NeurIPS, 2020)

Learning Global Transparent Models from Local Contrastive Explanations. - Tejaswini Pedapati; Avinash Balakrishnan; Karthikeyan Shanmugam; Amit Dhurandhar (CoRR, 2020)

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 001; Tejaswini Pedapati; et al. (AAAI, 2021)

CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions. - Isha Puri; Amit Dhurandhar; Tejaswini Pedapati; Karthikeyan Shanmugam; Dennis Wei; Kush R. Varshney (NeurIPS, 2021)

Building Accurate Simple Models with Multihop. - Amit Dhurandhar; Tejaswini Pedapati (CoRR, 2021)

Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents. - Jasmina Gajcin; Rahul Nair; Tejaswini Pedapati; Radu Marinescu 002; Elizabeth Daly; Ivana Dusparic (CoRR, 2021)

Multihop: Leveraging Complex Models to Learn Accurate Simple Models. - Amit Dhurandhar; Tejaswini Pedapati (ICKG, 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)

MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning. - KrishnaTeja Killamsetty; Alexandre V. Evfimievski; Tejaswini Pedapati; Kiran Kate; Lucian Popa 001; Rishabh K. Iyer (CoRR, 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)

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 (ACL (Findings), 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)

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)

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)

Granite Guardian. - Inkit Padhi; Manish Nagireddy; Giandomenico Cornacchia; Subhajit Chaudhury; Tejaswini Pedapati; Pierre L. Dognin; Keerthiram Murugesan; Erik Miehling; Martin Santillan Cooper; Kieran Fraser; Giulio Zizzo; Muhammad Zaid Hameed; et al. (CoRR, 2024)

Model Agnostic Contrastive Explanations for Classification Models. - Amit Dhurandhar; Tejaswini Pedapati; Avinash Balakrishnan; Pin-Yu Chen; Karthikeyan Shanmugam; Ruchir Puri (IEEE J. Emerg. Sel. Topics Circuits Syst., 2024)

EvalAssist: LLM-as-a-Judge Simplified. - Michael Desmond; Zahra Ashktorab; Werner Geyer; Elizabeth M. Daly; Martín Santillán Cooper; Qian Pan; Rahul Nair; Nico Wagner; Tejaswini Pedapati (AAAI, 2025)

From PEFT to DEFT: Parameter Efficient Finetuning for Reducing Activation Density in Transformers. - Bharat Runwal; Tejaswini Pedapati; Pin-Yu Chen (AAAI, 2025)

EpMAN: Episodic Memory AttentioN for Generalizing to Longer Contexts. - Subhajit Chaudhury; Payel Das; Sarathkrishna Swaminathan; Georgios Kollias; Elliot Nelson; Khushbu Pahwa; Tejaswini Pedapati; Igor Melnyk; Matthew Riemer (ACL (1), 2025)

Modular Prompt Learning Improves Vision-Language Models. - Zhenhan Huang; Tejaswini Pedapati; Pin-Yu Chen; Jianxi Gao (ICASSP, 2025)

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 (ICDE, 2025)

Large Language Models can Become Strong Self-Detoxifiers. - Ching-Yun Ko; Pin-Yu Chen; Payel Das; Youssef Mroueh; Soham Dan; Georgios Kollias; Subhajit Chaudhury; Tejaswini Pedapati; Luca Daniel (ICLR, 2025)

STAR: Spectral Truncation and Rescale for Model Merging. - Yu-Ang Lee; Ching-Yun Ko; Tejaswini Pedapati; I-Hsin Chung; Mi-Yen Yeh; Pin-Yu Chen (NAACL (Short Papers), 2025)

Granite Guardian: Comprehensive LLM Safeguarding. - Inkit Padhi; Manish Nagireddy; Giandomenico Cornacchia; Subhajit Chaudhury; Tejaswini Pedapati; Pierre L. Dognin; Keerthiram Murugesan; Erik Miehling; Martín Santillán Cooper; Kieran Fraser; Giulio Zizzo; Muhammad Zaid Hameed; et al. (NAACL (Industry Track), 2025)

Large Language Model Confidence Estimation via Black-Box Access. - Tejaswini Pedapati; Amit Dhurandhar; Soumya Ghosh; Soham Dan; Prasanna Sattigeri (Trans. Mach. Learn. Res., 2025)

Differentiable Prompt Learning for Vision Language Models. - Zhenhan Huang; Tejaswini Pedapati; Pin-Yu Chen; Jianxi Gao (CoRR, 2025)

Sparse Gradient Compression for Fine-Tuning Large Language Models. - David H. Yang; Mohammad Mohammadi Amiri; Tejaswini Pedapati; Subhajit Chaudhury; Pin-Yu Chen (CoRR, 2025)

LongFuncEval: Measuring the effectiveness of long context models for function calling. - Kiran Kate; Tejaswini Pedapati; Kinjal Basu 002; Yara Rizk; Vijil Chenthamarakshan; Subhajit Chaudhury; Mayank Agarwal; Ibrahim Abdelaziz (CoRR, 2025)

CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions. - Isha Puri; Amit Dhurandhar; Tejaswini Pedapati; Kartikeyan Shanmugam; Dennis Wei; Kush R. Varshney (CoRR, 2025)

EvalAssist: A Human-Centered Tool for LLM-as-a-Judge. - Zahra Ashktorab; Elizabeth M. Daly; Erik Miehling; Werner Geyer; Martin Santillan Cooper; Tejaswini Pedapati; Michael Desmond; Qian Pan; Hyo Jin Do (CoRR, 2025)

TOPJoin: A Context-Aware Multi-Criteria Approach for Joinable Column Search. - Harsha Kokel; Aamod Khatiwada; Tejaswini Pedapati; Haritha Ananthakrishnan; Oktie Hassanzadeh; Horst Samulowitz; Kavitha Srinivas (CoRR, 2025)