Reinforcement Learning in Education: A Literature Review
The utilization of reinforcement learning (RL) within the field of education holds the potential
to bring about a significant shift in the way students approach and engage with learning and …
to bring about a significant shift in the way students approach and engage with learning and …
Reinforcement learning for education: Opportunities and challenges
This survey article has grown out of the RL4ED workshop organized by the authors at the
Educational Data Mining (EDM) 2021 conference. We organized this workshop as part of a …
Educational Data Mining (EDM) 2021 conference. We organized this workshop as part of a …
Investigating bi-level optimization for learning and vision from a unified perspective: A survey and beyond
Bi-Level Optimization (BLO) is originated from the area of economic game theory and then
introduced into the optimization community. BLO is able to handle problems with a …
introduced into the optimization community. BLO is able to handle problems with a …
A fully single loop algorithm for bilevel optimization without hessian inverse
In this paper, we propose a novel Hessian inverse free Fully Single Loop Algorithm (FSLA)
for bilevel optimization problems. Classic algorithms for bilevel optimization admit a double …
for bilevel optimization problems. Classic algorithms for bilevel optimization admit a double …
Identifiability and generalizability in constrained inverse reinforcement learning
Two main challenges in Reinforcement Learning (RL) are designing appropriate reward
functions and ensuring the safety of the learned policy. To address these challenges, we …
functions and ensuring the safety of the learned policy. To address these challenges, we …
Regression under human assistance
Decisions are increasingly taken by both humans and machine learning models. However,
machine learning models are currently trained for full automation—they are not aware that …
machine learning models are currently trained for full automation—they are not aware that …
Classification under human assistance
Most supervised learning models are trained for full automation. However, their predictions
are sometimes worse than those by human experts on some specific instances. Motivated by …
are sometimes worse than those by human experts on some specific instances. Motivated by …
Human assisted learning by evolutionary multi-objective optimization
Abstract Machine learning models have liberated manpower greatly in many real-world
tasks, but their predictions are still worse than humans on some specific instances. To …
tasks, but their predictions are still worse than humans on some specific instances. To …
Local stochastic bilevel optimization with momentum-based variance reduction
Bilevel Optimization has witnessed notable progress recently with new emerging efficient
algorithms and has been applied to many machine learning tasks such as data cleaning …
algorithms and has been applied to many machine learning tasks such as data cleaning …
Reward poisoning in reinforcement learning: Attacks against unknown learners in unknown environments
We study black-box reward poisoning attacks against reinforcement learning (RL), in which
an adversary aims to manipulate the rewards to mislead a sequence of RL agents with …
an adversary aims to manipulate the rewards to mislead a sequence of RL agents with …