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Found 14 papers, 2 papers with code
Federated Learning with Matched Averaging
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.
Bayesian Nonparametric Federated Learning of Neural Networks
In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited.
Probabilistic Federated Neural Matching
In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited.
A Bandit Approach to Posterior Dialog Orchestration Under a Budget
Building multi-domain AI agents is a challenging task and an open problem in the area of AI.
D3BA: A Tool for Optimizing Business Processes Using Non-Deterministic Planning
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.
A Unified Conversational Assistant Framework for Business Process Automation
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.
A Snooze-less User-Aware Notification System for Proactive Conversational Agents
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.
Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case
Usage of automated controllers which make decisions on an environment are widespread and are often based on black-box models.
Contextual Bandit with Missing Rewards
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").
From Robotic Process Automation to Intelligent Process Automation: Emerging Trends
In this survey, we study how recent advances in machine intelligence are disrupting the world of business processes.
A Conversational Digital Assistant for Intelligent Process Automation
Robotic process automation (RPA) has emerged as the leading approach to automate tasks in business processes.
Double-Linear Thompson Sampling for Context-Attentive Bandits
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.
Online Semi-Supervised Learning with Bandit Feedback
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.
Explainable Composition of Aggregated Assistants
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.
