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Prescriptive machine learning for automated decision making: Challenges and opportunities
Recent applications of machine learning (ML) reveal a noticeable shift from its use for
predictive modeling in the sense of a data-driven construction of models mainly used for the …
predictive modeling in the sense of a data-driven construction of models mainly used for the …
Reciprocal learning
We demonstrate that numerous machine learning algorithms are specific instances of one
single paradigm: reciprocal learning. These instances range from active learning over multi …
single paradigm: reciprocal learning. These instances range from active learning over multi …
Collective intelligence as a public good
We discuss measures of collective intelligence in evolved and designed self-organizing
ensembles, defining collective intelligence in terms of the benefits to be gained through the …
ensembles, defining collective intelligence in terms of the benefits to be gained through the …
Predicting novel drug candidates against Covid-19 using generative deep neural networks
The novel Coronavirus outbreak has created a massive economic crisis, and many succumb
to death, disturbing the lives of mankind all over the world. Currently, there are no viable …
to death, disturbing the lives of mankind all over the world. Currently, there are no viable …
Distributed learning over Markovian fading channels for stable spectrum access
We consider the problem of multi-user spectrum access in wireless networks. The bandwidth
is divided into orthogonal channels, and users aim to access the spectrum. Each user …
is divided into orthogonal channels, and users aim to access the spectrum. Each user …
Robust bayesian satisficing
Distributional shifts pose a significant challenge to achieving robustness in contemporary
machine learning. To overcome this challenge, robust satisficing (RS) seeks a robust …
machine learning. To overcome this challenge, robust satisficing (RS) seeks a robust …
Effect of text message alerts on miners evacuation decisions
This work evaluates the effect of increasing level of detail in emergency alerts on
underground miners' emergency evacuation decisions. An offline survey was administered …
underground miners' emergency evacuation decisions. An offline survey was administered …
A user selection algorithm for aggregating electric vehicle demands based on a multi‐armed bandit approach
In systems with high penetration of renewables, demand side resources have been
aggregated to facilitate system operation. However, the natural uncertainty and randomness …
aggregated to facilitate system operation. However, the natural uncertainty and randomness …
Non-stationary representation learning in sequential linear bandits
In this paper, we study representation learning for multi-task decision-making in non-
stationary environments. We consider the framework of sequential linear bandits, where the …
stationary environments. We consider the framework of sequential linear bandits, where the …
Learning in restless multiarmed bandits via adaptive arm sequencing rules
We consider a class of restless multiarmed bandit (RMAB) problems with unknown arm
dynamics. At each time, a player chooses an arm out of arms to play, referred to as an active …
dynamics. At each time, a player chooses an arm out of arms to play, referred to as an active …