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Tejaswini Pedapati
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2020 – today
- 2024
- [c9]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: 2416-2430 - [i23]Bharat Runwal, Tejaswini Pedapati, Pin-Yu Chen:
From PEFT to DEFT: Parameter Efficient Finetuning for Reducing Activation Density in Transformers. CoRR abs/2402.01911 (2024) - [i22]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 abs/2404.01306 (2024) - [i21]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 abs/2405.01306 (2024) - [i20]Tejaswini Pedapati, Amit Dhurandhar, Soumya Ghosh, Soham Dan, Prasanna Sattigeri:
Large Language Model Confidence Estimation via Black-Box Access. CoRR abs/2406.04370 (2024) - [i19]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 abs/2407.01619 (2024) - [i18]Zahra Ashktorab, Michael Desmond, Qian Pan, James M. Johnson, Martin Santillan Cooper, Elizabeth M. Daly, Rahul Nair, Tejaswini Pedapati, Swapnaja Achintalwar, Werner Geyer:
Aligning Human and LLM Judgments: Insights from EvalAssist on Task-Specific Evaluations and AI-assisted Assessment Strategy Preferences. CoRR abs/2410.00873 (2024) - [i17]Ching-Yun Ko, Pin-Yu Chen, Payel Das, Youssef Mroueh, Soham Dan, Georgios Kollias, Subhajit Chaudhury, Tejaswini Pedapati, Luca Daniel:
Large Language Models can be Strong Self-Detoxifiers. CoRR abs/2410.03818 (2024) - 2023
- [i16]KrishnaTeja 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 abs/2301.13287 (2023) - [i15]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 abs/2307.04217 (2023) - 2022
- [c8]Amit Dhurandhar, Tejaswini Pedapati:
Multihop: Leveraging Complex Models to Learn Accurate Simple Models. ICKG 2022: 48-55 - [i14]Chunheng Jiang, Tejaswini Pedapati, Pin-Yu Chen, Yizhou Sun, Jianxi Gao:
Neural Capacitance: A New Perspective of Neural Network Selection via Edge Dynamics. CoRR abs/2201.04194 (2022) - 2021
- [c7]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: 16001-16003 - [c6]Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Karthikeyan Shanmugam
CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions. NeurIPS 2021: 21668-21680 - [i13]Amit Dhurandhar, Tejaswini Pedapati:
Building Accurate Simple Models with Multihop. CoRR abs/2109.06961 (2021) - [i12]Jasmina Gajcin, Rahul Nair, Tejaswini Pedapati, Radu Marinescu, Elizabeth Daly, Ivana Dusparic:
Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents. CoRR abs/2112.09462 (2021) - 2020
- [c5]Martin Wistuba, Tejaswini Pedapati:
Learning to Rank Learning Curves. ICML 2020: 10303-10312 - [c4]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: 1-9 - [c3]Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati:
Automation of Deep Learning - Theory and Practice. ICMR 2020: 5-6 - [c2]Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam
Learning Global Transparent Models consistent with Local Contrastive Explanations. NeurIPS 2020 - [i11]Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar:
Learning Global Transparent Models from Local Contrastive Explanations. CoRR abs/2002.08247 (2020) - [i10]Martin Wistuba, Tejaswini Pedapati:
Learning to Rank Learning Curves. CoRR abs/2006.03361 (2020)
2010 – 2019
- 2019
- [i9]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 abs/1901.06261 (2019) - [i8]Martin Wistuba, Tejaswini Pedapati:
Inductive Transfer for Neural Architecture Optimization. CoRR abs/1903.03536 (2019) - [i7]Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati:
A Survey on Neural Architecture Search. CoRR abs/1905.01392 (2019) - [i6]Amit Dhurandhar, Tejaswini Pedapati, Avinash Balakrishnan, Pin-Yu Chen, Karthikeyan Shanmugam, Ruchir Puri:
Model Agnostic Contrastive Explanations for Structured Data. CoRR abs/1906.00117 (2019) - [i5]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 abs/1910.14436 (2019) - 2018
- [c1]Fatemeh Nargesian, Udayan Khurana, Tejaswini Pedapati, Horst Samulowitz, Deepak S. Turaga:
Dataset Evolver: An Interactive Feature Engineering Notebook. AAAI 2018: 8212-8213 - [i4]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 abs/1812.00099 (2018) - 2017
- [j1]Çagatay Demiralp, Peter J. Haas, Srinivasan Parthasarathy, Tejaswini Pedapati:
Foresight: Recommending Visual Insights. Proc. VLDB Endow. 10(12): 1937-1940 (2017) - [i3]Çagatay Demiralp, Peter J. Haas, Srinivasan Parthasarathy, Tejaswini Pedapati:
Foresight: Recommending Visual Insights. CoRR abs/1707.03877 (2017) - [i2]Çagatay Demiralp, Peter J. Haas, Srinivasan Parthasarathy, Tejaswini Pedapati:
Foresight: Rapid Data Exploration Through Guideposts. CoRR abs/1709.10513 (2017) - [i1]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 abs/1711.06195 (2017)
Coauthor Index
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last updated on 2024-11-13 23:49 CET by the dblp team
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