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ORCIDPreethi Lahoti
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2020 – today
- 2024
[c10]Alexandru Tifrea, Preethi Lahoti, Ben Packer, Yoni Halpern, Ahmad Beirami, Flavien Prost:
FRAPPÉ: A Group Fairness Framework for Post-Processing Everything. ICML 2024- [i12]James Atwood, Preethi Lahoti, Ananth Balashankar, Flavien Prost, Ahmad Beirami:
Inducing Group Fairness in LLM-Based Decisions. CoRR abs/2406.16738 (2024) - [i11]Yash Kumar Lal, Preethi Lahoti, Aradhana Sinha, Yao Qin, Ananth Balashankar:
Automated Adversarial Discovery for Safety Classifiers. CoRR abs/2406.17104 (2024) - 2023
- [j3]Preethi Lahoti
, P. Krishna Gummadi, Gerhard Weikum:
Responsible model deployment via model-agnostic uncertainty learning. Mach. Learn. 112(3): 939-970 (2023) - [c9]Bhaktipriya Radharapu, Kevin Robinson, Lora Aroyo, Preethi Lahoti:
AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications. EMNLP (Industry Track) 2023: 380-395 - [c8]Preethi Lahoti, Nicholas Blumm, Xiao Ma, Raghavendra Kotikalapudi, Sahitya Potluri, Qijun Tan, Hansa Srinivasan, Ben Packer, Ahmad Beirami, Alex Beutel, Jilin Chen:
Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting. EMNLP 2023: 10383-10405 - [i10]Preethi Lahoti, Nicholas Blumm, Xiao Ma, Raghavendra Kotikalapudi, Sahitya Potluri, Qijun Tan, Hansa Srinivasan, Ben Packer, Ahmad Beirami, Alex Beutel, Jilin Chen:
Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting. CoRR abs/2310.16523 (2023) - [i9]Bhaktipriya Radharapu, Kevin Robinson, Lora Aroyo, Preethi Lahoti:
AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications. CoRR abs/2311.08592 (2023) - [i8]Alexandru Tifrea, Preethi Lahoti, Ben Packer, Yoni Halpern, Ahmad Beirami, Flavien Prost:
FRAPPÉ: A Post-Processing Framework for Group Fairness Regularization. CoRR abs/2312.02592 (2023) - 2021
- [b1]Preethi Lahoti:
Operationalizing fairness for responsible machine learning. Saarland University, Saarbrücken, Germany, 2021 - [c7]Junaid Ali, Preethi Lahoti, Krishna P. Gummadi:
Accounting for Model Uncertainty in Algorithmic Discrimination. AIES 2021: 336-345 - [c6]Preethi Lahoti, Krishna P. Gummadi, Gerhard Weikum:
Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning. ICDM 2021: 1174-1179 - [i7]Junaid Ali, Preethi Lahoti, Krishna P. Gummadi:
Accounting for Model Uncertainty in Algorithmic Discrimination. CoRR abs/2105.04249 (2021) - [i6]Preethi Lahoti, Krishna P. Gummadi, Gerhard Weikum:
Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning. CoRR abs/2109.04432 (2021) - 2020
- [c5]Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed H. Chi:
Fairness without Demographics through Adversarially Reweighted Learning. NeurIPS 2020 - [i5]Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed H. Chi:
Fairness without Demographics through Adversarially Reweighted Learning. CoRR abs/2006.13114 (2020)
2010 – 2019
- 2019
- [j2]Preethi Lahoti, Krishna P. Gummadi, Gerhard Weikum:
Operationalizing Individual Fairness with Pairwise Fair Representations. Proc. VLDB Endow. 13(4): 506-518 (2019) - [j1]Alexandra Olteanu, Jean Garcia-Gathright, Maarten de Rijke, Michael D. Ekstrand, Adam Roegiest, Aldo Lipani, Alex Beutel, Ana Lucic, Ana-Andreea Stoica, Anubrata Das, Asia Biega, Bart Voorn, Claudia Hauff, Damiano Spina, David D. Lewis, Douglas W. Oard, Emine Yilmaz, Faegheh Hasibi, Gabriella Kazai, Graham McDonald, Hinda Haned, Iadh Ounis, Ilse van der Linden, Joris Baan, Kamuela N. Lau, Krisztian Balog, Mahmoud F. Sayed, Maria Panteli, Mark Sanderson, Matthew Lease, Preethi Lahoti, Toshihiro Kamishima:
FACTS-IR: fairness, accountability, confidentiality, transparency, and safety in information retrieval. SIGIR Forum 53(2): 20-43 (2019) - [c4]Preethi Lahoti, Krishna P. Gummadi, Gerhard Weikum:
iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making. ICDE 2019: 1334-1345 - [i4]Preethi Lahoti, Krishna P. Gummadi, Gerhard Weikum:
Operationalizing Individual Fairness with Pairwise Fair Representations. CoRR abs/1907.01439 (2019) - [i3]Hanchen Wang, Nina Grgic-Hlaca, Preethi Lahoti, Krishna P. Gummadi, Adrian Weller:
An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervision. CoRR abs/1910.10255 (2019) - 2018
- [c3]Preethi Lahoti, Kiran Garimella, Aristides Gionis:
Joint Non-negative Matrix Factorization for Learning Ideological Leaning on Twitter. WSDM 2018: 351-359 - [i2]Preethi Lahoti, Gerhard Weikum, Krishna P. Gummadi:
iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making. CoRR abs/1806.01059 (2018) - 2017
- [c2]Preethi Lahoti, Patrick K. Nicholson, Bilyana Taneva:
Efficient Set Intersection Counting Algorithm for Text Similarity Measures. ALENEX 2017: 146-158 - [c1]Preethi Lahoti, Gianmarco De Francisci Morales, Aristides Gionis:
Finding topical experts in Twitter via query-dependent personalized PageRank. ASONAM 2017: 155-162 - [i1]Preethi Lahoti, Kiran Garimella, Aristides Gionis:
Joint Non-negative Matrix Factorization for Learning Ideological Leaning on Twitter. CoRR abs/1711.10251 (2017)
Coauthor Index
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