Zobrazeno 1 - 10
of 244
pro vyhledávání: '"Patro, P. K."'
Publikováno v:
LREC-COLING 2024
The absence of explicitly tailored, accessible annotated datasets for educational purposes presents a notable obstacle for NLP tasks in languages with limited resources.This study initially explores the feasibility of using machine translation (MT) t
Externí odkaz:
http://arxiv.org/abs/2404.17194
Autor:
Ganguly, Niloy, Fazlija, Dren, Badar, Maryam, Fisichella, Marco, Sikdar, Sandipan, Schrader, Johanna, Wallat, Jonas, Rudra, Koustav, Koubarakis, Manolis, Patro, Gourab K., Amri, Wadhah Zai El, Nejdl, Wolfgang
State-of-the-art AI models largely lack an understanding of the cause-effect relationship that governs human understanding of the real world. Consequently, these models do not generalize to unseen data, often produce unfair results, and are difficult
Externí odkaz:
http://arxiv.org/abs/2302.06975
Autor:
Colmenarejo, Alejandra Bringas, Nannini, Luca, Rieger, Alisa, Scott, Kristen M., Zhao, Xuan, Patro, Gourab K., Kasneci, Gjergji, Kinder-Kurlanda, Katharina
With increasing digitalization, Artificial Intelligence (AI) is becoming ubiquitous. AI-based systems to identify, optimize, automate, and scale solutions to complex economic and societal problems are being proposed and implemented. This has motivate
Externí odkaz:
http://arxiv.org/abs/2207.01510
Autor:
Patro, Gourab K., Jana, Prithwish, Chakraborty, Abhijnan, Gummadi, Krishna P., Ganguly, Niloy
Recently, almost all conferences have moved to virtual mode due to the pandemic-induced restrictions on travel and social gathering. Contrary to in-person conferences, virtual conferences face the challenge of efficiently scheduling talks, accounting
Externí odkaz:
http://arxiv.org/abs/2204.12062
Autor:
Patro, Gourab K, Porcaro, Lorenzo, Mitchell, Laura, Zhang, Qiuyue, Zehlike, Meike, Garg, Nikhil
Ranking, recommendation, and retrieval systems are widely used in online platforms and other societal systems, including e-commerce, media-streaming, admissions, gig platforms, and hiring. In the recent past, a large "fair ranking" research literatur
Externí odkaz:
http://arxiv.org/abs/2201.12662
Many online platforms today (such as Amazon, Netflix, Spotify, LinkedIn, and AirBnB) can be thought of as two-sided markets with producers and customers of goods and services. Traditionally, recommendation services in these platforms have focused on
Externí odkaz:
http://arxiv.org/abs/2201.01180
The proliferation of smartphones has led to the increased popularity of location-based search and recommendation systems. Online platforms like Google and Yelp allow location-based search in the form of nearby feature to query for hotels or restauran
Externí odkaz:
http://arxiv.org/abs/2011.07359
Recommender systems are one of the most widely used services on several online platforms to suggest potential items to the end-users. These services often use different machine learning techniques for which fairness is a concerning factor, especially
Externí odkaz:
http://arxiv.org/abs/2011.05287
The (COVID-19) pandemic-induced restrictions on travel and social gatherings have prompted most conference organizers to move their events online. However, in contrast to physical conferences, virtual conferences face a challenge in efficiently sched
Externí odkaz:
http://arxiv.org/abs/2010.14624
Autor:
Finocchiaro, Jessie, Maio, Roland, Monachou, Faidra, Patro, Gourab K, Raghavan, Manish, Stoica, Ana-Andreea, Tsirtsis, Stratis
Decision-making systems increasingly orchestrate our world: how to intervene on the algorithmic components to build fair and equitable systems is therefore a question of utmost importance; one that is substantially complicated by the context-dependen
Externí odkaz:
http://arxiv.org/abs/2010.05434