Optimizing Human Learning

2017-12-05 · Behzad Tabibian, Utkarsh Upadhyay, Abir De, Ali Zarezade, Bernhard Schoelkopf, Manuel Gomez-Rodriguez

Spaced repetition is a technique for efficient memorization which uses repeated, spaced review of content to improve long-term retention. Can we find the optimal reviewing schedule to maximize the benefits of spaced repetition? In this paper, we introduce a novel, flexible representation of spaced repetition using the framework of marked temporal point processes and then address the above question as an optimal control problem for stochastic differential equations with jumps. For two well-known human memory models, we show that the optimal reviewing schedule is given by the recall probability of the content to be learned. As a result, we can then develop a simple, scalable online algorithm, Memorize, to sample the optimal reviewing times. Experiments on both synthetic and real data gathered from Duolingo, a popular language-learning online platform, show that our algorithm may be able to help learners memorize more effectively than alternatives.

📄 PDF Abstract BibTeX arXiv:1712.01856

Code (0)

등록된 구현이 없습니다.

Tasks

Memorization Point Processes

Fragility of Value under Imperfect Alignment

2026-07-30 · Winter Cross arxiv

As more responsibility is placed upon AI systems, it becomes increasingly important to guarantee that these systems are aligned with humanity. A common fear in AI safety is that human value is fragile -- that is, optimiz…

Optimizing Autonomous Driving for Safety: A Human-Centric Approach with LLM-Enhanced RLHF

2024-06-06 · Yuan Sun, Navid Salami Pargoo, Peter J. Jin, Jorge Ortiz

Reinforcement Learning from Human Feedback (RLHF) is popular in large language models (LLMs), whereas traditional Reinforcement Learning (RL) often falls short. Current autonomous driving methods typically utilize either…

Autonomous Driving reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Optimizing human-interpretable dialog management policy using Genetic Algorithm

2016-05-12 · Hang Ren, Weiqun Xu, Yonghong Yan

Automatic optimization of spoken dialog management policies that are robust to environmental noise has long been the goal for both academia and industry. Approaches based on reinforcement learning have been proved to be …

Management reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative Evaluation

2023-09-15 · Laura Ferrarotti, Massimiliano Luca, Gabriele Santin, Giorgio Previati 외

Optimizing traffic dynamics in an evolving transportation landscape is crucial, particularly in scenarios where autonomous vehicles (AVs) with varying levels of autonomy coexist with human-driven cars. While optimizing R…

Autonomous Vehicles reinforcement-learning Reinforcement Learning (RL)

VVC+M: Plug and Play Scalable Image Coding for Humans and Machines

2023-05-17 · Alon Harell, Yalda Foroutan, Ivan V. Bajic

Compression for machines is an emerging field, where inputs are encoded while optimizing the performance of downstream automated analysis. In scalable coding for humans and machines, the compressed representation used fo…

Image Compression