Tejaswini Pedapati

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 31

Top Topics

Illumination Compensation

Learning Curves

Top Venues

Publications

Zhenhan Huang, Tejaswini Pedapati, Pin-Yu Chen, Chunheng Jiang, Jianxi Gao
Graph is all you need? Lightweight data-agnostic neural architecture search without training. CoRR (2024)
Bharat Runwal, Tejaswini Pedapati, Pin-Yu Chen
From PEFT to DEFT: Parameter Efficient Finetuning for Reducing Activation Density in Transformers. CoRR (2024)
Tejaswini Pedapati, Amit Dhurandhar, Soumya Ghosh, Soham Dan, Prasanna Sattigeri
Large Language Model Confidence Estimation via Black-Box Access. CoRR (2024)
Amit Dhurandhar, Tejaswini Pedapati, Ronny Luss, Soham Dan, Aurélie C. Lozano, Payel Das, Georgios Kollias
NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models. CoRR (2024)
Aamod Khatiwada, Harsha Kokel, Ibrahim Abdelaziz, Subhajit Chaudhury, Julian Dolby, Oktie Hassanzadeh, Zhenhan Huang, Tejaswini Pedapati, Horst Samulowitz, Kavitha Srinivas
TabSketchFM: Sketch-based Tabular Representation Learning for Data Discovery over Data Lakes. CoRR (2024)
Amit Dhurandhar, Tejaswini Pedapati, Ronny Luss, Soham Dan, Aurélie C. Lozano, Payel Das, Georgios Kollias
NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models. ACL (Findings) (2024)
Kavitha Srinivas, Julian Dolby, Ibrahim Abdelaziz, Oktie Hassanzadeh, Harsha Kokel, Aamod Khatiwada, Tejaswini Pedapati, Subhajit Chaudhury, Horst Samulowitz
LakeBench: Benchmarks for Data Discovery over Data Lakes. CoRR (2023)
KirshnaTeja Killamsetty, Alexandre V. Evfimievski, Tejaswini Pedapati, Kiran Kate, Lucian Popa, Rishabh K. Iyer
MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning. CoRR (2023)
Chunheng Jiang, Tejaswini Pedapati, Pin-Yu Chen, Yizhou Sun, Jianxi Gao
Neural Capacitance: A New Perspective of Neural Network Selection via Edge Dynamics. CoRR (2022)
Amit Dhurandhar, Tejaswini Pedapati
Multihop: Leveraging Complex Models to Learn Accurate Simple Models. ICKG (2022)
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
AutoText: An End-to-End AutoAI Framework for Text. AAAI (2021)
Amit Dhurandhar, Tejaswini Pedapati
Building Accurate Simple Models with Multihop. CoRR (2021)
Jasmina Gajcin, Rahul Nair, Tejaswini Pedapati, Radu Marinescu, Elizabeth Daly, Ivana Dusparic
Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents. CoRR (2021)
Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney
CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions. NeurIPS (2021)
Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar
Learning Global Transparent Models from Local Contrastive Explanations. CoRR (2020)
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
Survey on Automated End-to-End Data Science? IJCNN (2020)
Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati
Automation of Deep Learning - Theory and Practice. ICMR (2020)
Martin Wistuba, Tejaswini Pedapati
Learning to Rank Learning Curves. CoRR (2020)
Martin Wistuba, Tejaswini Pedapati
Learning to Rank Learning Curves. ICML (2020)
Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar
Learning Global Transparent Models consistent with Local Contrastive Explanations. NeurIPS (2020)
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
How can AI Automate End-to-End Data Science? CoRR (2019)
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
NeuNetS: An Automated Synthesis Engine for Neural Network Design. CoRR (2019)
Martin Wistuba, Tejaswini Pedapati
Inductive Transfer for Neural Architecture Optimization. CoRR (2019)
Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati
A Survey on Neural Architecture Search. CoRR (2019)
Amit Dhurandhar, Tejaswini Pedapati, Avinash Balakrishnan, Pin-Yu Chen, Karthikeyan Shanmugam, Ruchir Puri
Model Agnostic Contrastive Explanations for Structured Data. CoRR (2019)
Fatemeh Nargesian, Udayan Khurana, Tejaswini Pedapati, Horst Samulowitz, Deepak S. Turaga
Dataset Evolver: An Interactive Feature Engineering Notebook. AAAI (2018)
Vidya Muthukumar, Tejaswini Pedapati, Nalini K. Ratha, Prasanna Sattigeri, Chai-Wah Wu, Brian Kingsbury, Abhishek Kumar, Samuel Thomas, Aleksandra Mojsilovic, Kush R. Varshney
Understanding Unequal Gender Classification Accuracy from Face Images. CoRR (2018)
Çagatay Demiralp, Peter J. Haas, Srinivasan Parthasarathy, Tejaswini Pedapati
Foresight: Recommending Visual Insights. CoRR (2017)
Tejas Dharamsi, Payel Das, Tejaswini Pedapati, Gregory Bramble, Vinod Muthusamy, Horst Samulowitz, Kush R. Varshney, Yuvaraj Rajamanickam, John Thomas, Justin Dauwels
Neurology-as-a-Service for the Developing World. CoRR (2017)
Çagatay Demiralp, Peter J. Haas, Srinivasan Parthasarathy, Tejaswini Pedapati
Foresight: Rapid Data Exploration Through Guideposts. CoRR (2017)
Çagatay Demiralp, Peter J. Haas, Srinivasan Parthasarathy, Tejaswini Pedapati
Foresight: Recommending Visual Insights. Proc. VLDB Endow. 10 (12) (2017)