Optimal Learning for Sequential Decisions in Laboratory Experimentation

• URL: http://arxiv.org/abs/2004.05417v2
• Date: Tue, 14 Apr 2020 00:54:16 GMT
• Title: Optimal Learning for Sequential Decisions in Laboratory Experimentation
• Authors: Kristopher Reyes and Warren B Powell
• Abstract summary: This tutorial is aimed to provide experimental scientists with a foundation in the science of making decisions. We introduce the concept of a learning policy, and review the major categories of policies. We then introduce a policy, known as the knowledge gradient, that maximizes the value of information from each experiment.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The process of discovery in the physical, biological and medical sciences can be painstakingly slow. Most experiments fail, and the time from initiation of research until a new advance reaches commercial production can span 20 years. This tutorial is aimed to provide experimental scientists with a foundation in the science of making decisions. Using numerical examples drawn from the experiences of the authors, the article describes the fundamental elements of any experimental learning problem. It emphasizes the important role of belief models, which include not only the best estimate of relationships provided by prior research, previous experiments and scientific expertise, but also the uncertainty in these relationships. We introduce the concept of a learning policy, and review the major categories of policies. We then introduce a policy, known as the knowledge gradient, that maximizes the value of information from each experiment. We bring out the importance of reducing uncertainty, and illustrate this process for different belief models.

Related papers

• Hypothesizing Missing Causal Variables with LLMs [55.28678224020973]
  We formulate a novel task where the input is a partial causal graph with missing variables, and the output is a hypothesis about the missing variables to complete the partial graph. We show the strong ability of LLMs to hypothesize the mediation variables between a cause and its effect. We also observe surprising results where some of the open-source models outperform the closed GPT-4 model.
  arXiv  Detail & Related papers  (2024-09-04T10:37:44Z)
• LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery [141.39722070734737]
  We propose to enhance the knowledge-driven, abstract reasoning abilities of Large Language Models with the computational strength of simulations. We introduce Scientific Generative Agent (SGA), a bilevel optimization framework. We conduct experiments to demonstrate our framework's efficacy in law discovery and molecular design.
  arXiv  Detail & Related papers  (2024-05-16T03:04:10Z)
• Large Language Models for Automated Open-domain Scientific Hypotheses Discovery [50.40483334131271]
  This work proposes the first dataset for social science academic hypotheses discovery. Unlike previous settings, the new dataset requires (1) using open-domain data (raw web corpus) as observations; and (2) proposing hypotheses even new to humanity. A multi- module framework is developed for the task, including three different feedback mechanisms to boost performance.
  arXiv  Detail & Related papers  (2023-09-06T05:19:41Z)
• A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why? [84.46288849132634]
  We propose a systematic framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques. We define three variables to encompass diverse facets of the evolution of research topics within NLP. We utilize a causal discovery algorithm to unveil the causal connections among these variables using observational data.
  arXiv  Detail & Related papers  (2023-05-22T11:08:00Z)
• Empirical Design in Reinforcement Learning [23.873958977534993]
  It is now common to benchmark agents with millions of parameters against dozens of tasks, each using the equivalent of 30 days of experience. The scale of these experiments often conflict with the need for proper statistical evidence, especially when comparing algorithms. This manuscript represents both a call to action, and a comprehensive resource for how to do good experiments in reinforcement learning.
  arXiv  Detail & Related papers  (2023-04-03T19:32:24Z)
• GFlowNets for AI-Driven Scientific Discovery [74.27219800878304]
  We present a new probabilistic machine learning framework called GFlowNets. GFlowNets can be applied in the modeling, hypotheses generation and experimental design stages of the experimental science loop. We argue that GFlowNets can become a valuable tool for AI-driven scientific discovery.
  arXiv  Detail & Related papers  (2023-02-01T17:29:43Z)
• Learning One Abstract Bit at a Time Through Self-Invented Experiments Encoded as Neural Networks [8.594140167290098]
  We present an empirical analysis of the automatic generation of interesting experiments. In the first setting, we investigate self-invented experiments in a reinforcement-providing environment. In the second setting, pure thought experiments are implemented as the weights of recurrent neural networks.
  arXiv  Detail & Related papers  (2022-12-29T17:11:49Z)
• Sources of Irreproducibility in Machine Learning: A Review [3.905855359082687]
  There exist no theoretical framework that relates experiment design choices to potential effects on the conclusions. The objective of this paper is to develop a framework that enables applied data science practitioners and researchers to understand which experiment design choices can lead to false findings.
  arXiv  Detail & Related papers  (2022-04-15T18:26:03Z)
• Observing Interventions: A logic for thinking about experiments [62.997667081978825]
  This paper makes a first step towards a logic of learning from experiments. Crucial for our approach is the idea that the notion of an intervention can be used as a formal expression of a (real or hypothetical) experiment. For all the proposed logical systems, we provide a sound and complete axiomatization.
  arXiv  Detail & Related papers  (2021-11-25T09:26:45Z)
• Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic Kernels [1.976226676686868]
  A majority of experimental disciplines face the challenge of exploring large and high-dimensional parameter spaces in search of new scientific discoveries. Recent advances have led to an increase in efficiency of materials discovery by increasingly automating the exploration processes. Gamma process regression (GPR) techniques have emerged as the method of choice for steering many classes of experiments.
  arXiv  Detail & Related papers  (2020-06-03T19:18:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.