Prescriptive machine learning for automated decision making: Challenges and opportunities

E Hüllermeier - arxiv preprint arxiv:2112.08268, 2021‏ - arxiv.org
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 …

Reciprocal learning

J Rodemann, C Jansen… - Advances in Neural …, 2025‏ - proceedings.neurips.cc
We demonstrate that numerous machine learning algorithms are specific instances of one
single paradigm: reciprocal learning. These instances range from active learning over multi …

Collective intelligence as a public good

NE Leonard, SA Levin - Collective Intelligence, 2022‏ - journals.sagepub.com
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 …

Predicting novel drug candidates against Covid-19 using generative deep neural networks

S Amilpur, R Bhukya - Journal of Molecular Graphics and Modelling, 2022‏ - Elsevier
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 …

Distributed learning over Markovian fading channels for stable spectrum access

T Gafni, K Cohen - IEEE Access, 2022‏ - ieeexplore.ieee.org
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 …

Robust bayesian satisficing

A Saday, YC Yıldırım, C Tekin - Advances in Neural …, 2023‏ - proceedings.neurips.cc
Distributional shifts pose a significant challenge to achieving robustness in contemporary
machine learning. To overcome this challenge, robust satisficing (RS) seeks a robust …

Effect of text message alerts on miners evacuation decisions

AU Rehman, T Lyche, K Awuah-Offei, VSS Nadendla - Safety Science, 2020‏ - Elsevier
This work evaluates the effect of increasing level of detail in emergency alerts on
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

Q Hu, N Zhang, X Quan, L Bai… - IET Energy Systems …, 2021‏ - Wiley Online Library
In systems with high penetration of renewables, demand side resources have been
aggregated to facilitate system operation. However, the natural uncertainty and randomness …

Non-stationary representation learning in sequential linear bandits

Y Qin, T Menara, S Oymak, SN Ching… - IEEE Open Journal of …, 2022‏ - ieeexplore.ieee.org
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 …

Learning in restless multiarmed bandits via adaptive arm sequencing rules

T Gafni, K Cohen - IEEE Transactions on Automatic Control, 2020‏ - ieeexplore.ieee.org
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 …