1 code implementation • ICLR 2020 • Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, Yasaman Khazaeni
Federated learning allows edge devices to collaboratively learn a shared model while keeping the training data on device, decoupling the ability to do model training from the need to store the data in the cloud.
1 code implementation • 28 May 2019 • Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Trong Nghia Hoang, Yasaman Khazaeni
In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited.
no code implementations • ICLR 2019 • Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, Yasaman Khazaeni
In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited.
no code implementations • 22 Jun 2019 • Sohini Upadhyay, Mayank Agarwal, Djallel Bounneffouf, Yasaman Khazaeni
Building multi-domain AI agents is a challenging task and an open problem in the area of AI.
no code implementations • 8 Jan 2020 • Tathagata Chakraborti, Yasaman Khazaeni
This paper builds upon recent work in the declarative design of dialogue agents and proposes an exciting new tool -- D3BA -- Declarative Design for Digital Business Automation, built to optimize business processes using the power of AI planning.
no code implementations • 7 Jan 2020 • Yara Rizk, Abhishek Bhandwalder, Scott Boag, Tathagata Chakraborti, Vatche Isahagian, Yasaman Khazaeni, Falk Pollock, Merve Unuvar
Business process automation is a booming multi-billion-dollar industry that promises to remove menial tasks from workers' plates -- through the introduction of autonomous agents -- and free up their time and brain power for more creative and engaging tasks.
no code implementations • 4 Mar 2020 • Yara Rizk, Vatche Isahagian, Merve Unuvar, Yasaman Khazaeni
The ubiquity of smart phones and electronic devices has placed a wealth of information at the fingertips of consumers as well as creators of digital content.
no code implementations • 9 Jul 2020 • Salomón Wollenstein-Betech, Christian Muise, Christos G. Cassandras, Ioannis Ch. Paschalidis, Yasaman Khazaeni
Usage of automated controllers which make decisions on an environment are widespread and are often based on black-box models.
no code implementations • 13 Jul 2020 • Djallel Bouneffouf, Sohini Upadhyay, Yasaman Khazaeni
We consider a novel variant of the contextual bandit problem (i. e., the multi-armed bandit with side-information, or context, available to a decision-maker) where the reward associated with each context-based decision may not always be observed("missing rewards").
no code implementations • 27 Jul 2020 • Tathagata Chakraborti, Vatche Isahagian, Rania Khalaf, Yasaman Khazaeni, Vinod Muthusamy, Yara Rizk, Merve Unuvar
In this survey, we study how recent advances in machine intelligence are disrupting the world of business processes.
no code implementations • 27 Jul 2020 • Yara Rizk, Vatche Isahagian, Scott Boag, Yasaman Khazaeni, Merve Unuvar, Vinod Muthusamy, Rania Khalaf
Robotic process automation (RPA) has emerged as the leading approach to automate tasks in business processes.
no code implementations • 15 Oct 2020 • Djallel Bouneffouf, Raphaël Féraud, Sohini Upadhyay, Yasaman Khazaeni, Irina Rish
In this paper, we analyze and extend an online learning framework known as Context-Attentive Bandit, motivated by various practical applications, from medical diagnosis to dialog systems, where due to observation costs only a small subset of a potentially large number of context variables can be observed at each iteration;however, the agent has a freedom to choose which variables to observe.
no code implementations • ICLR Workshop LLD 2019 • Sohini Upadhyay, Mikhail Yurochkin, Mayank Agarwal, Yasaman Khazaeni, DjallelBouneffouf
We formulate a new problem at the intersectionof semi-supervised learning and contextual bandits, motivated by several applications including clini-cal trials and ad recommendations.
no code implementations • 21 Nov 2020 • Sarath Sreedharan, Tathagata Chakraborti, Yara Rizk, Yasaman Khazaeni
A new design of an AI assistant that has become increasingly popular is that of an "aggregated assistant" -- realized as an orchestrated composition of several individual skills or agents that can each perform atomic tasks.