Cost-minimising strategies for data labelling: optimal stopping and active learning

Supervised learning deals with the inference of a distribution over an output or label space Y conditioned on points in an observation space X, given a training dataset D of pairs in X × Y. However, in a lot of applications of interest, acquisition of large amounts of observations is easy, while the process of generating labels is time-consuming or costly. One way to deal with this problem is active learning, where points to be labelled are selected with the aim of creating a model with better performance than that of an model trained on an equal number of randomly sampled points. In this paper, we instead propose to deal with the labelling cost directly: The learning goal is defined as the minimisation of a cost which is a function of the expected model performance and the total cost of the labels used. This allows the development of general strategies and specific algorithms for (a) optimal stopping, where the expected cost dictates whether label acquisition should continue (b) empirical evaluation, where the cost is used as a performance metric for a given combination of inference, stopping and sampling methods. Though the main focus of the paper is optimal stopping, we also aim to provide the background for further developments and discussion in the related field of active learning.

Cost-minimizing strategies for data labeling: Optimal stopping and active learning

Supervised learning deals with the inference of a distribution over an output or label space Y conditioned on points in an observation space X, given a training dataset D of pairs in X × Y. However, in a lot of applications of interest, acquisition of large amounts of observations is easy, while the process of generating labels is time-consuming or costly. One way to deal with this problem is active learning, where points to be labelled are selected with the aim of creating a model with better performance than that of an model trained on an equal number of randomly sampled points. Furthermore, given a fixed set of labelled examples, one may use semi-supervised learning methods to discover regularities in the data using the unlabelled examples. In contrast to these two approaches, this paper proposes to deal with the labelling cost directly: The learning goal is defined as the minimisation of a cost which is a function of the expected model performance and the total cost of the labels used. This allows the development of general strategies and specific algorithms for (a) optimal stopping, where the expected cost dictates whether label acquisition should continue (b) active learning, where the sampling is guided by the expected cost (c) empirical evaluation, where the cost is used as a performance metric for a given combination of inference, stopping and sampling methods. Though the main focus of the paper is optimal stopping, we also aim to provide the background for further developments and discussion in the related field of active learning.

Building Models of Global Supply Chains Basic Principles and Requirements

In a market environment of increasing complexity, managing the entire supply chain becomes a critical factor for success in logistics. The requirements on simulation are as ambitious as wide-spread. Simulation models are implemented to evaluate concepts and system designs, analyse, modify and optimise existing systems and control material flows. This work covers two main topics. First, we analyse the properties of existing concepts for simulation models, so as to consolidate present knowledge on the modelling of a supply chain. Therefore, theoretical developments from the field of supply chain optimisation by simulation are revisited. Secondly, we describe and analyse the different key terms and concepts of discrete-event simulation. We characterise and distinguish the key terms for a supply chain simulation model, like objects, states, timeframes and flows and the specific requirements on different kinds of supply chains. Generally, the reasons for modelling a supply chain are quite divergent. Models are often case-based or focus on selected aspects or subsystems of a global network. Finally, we summarise potentials and shortcomings of supply chain simulation and delineate issues for further research

Search for Scalar Diphoton Resonances in the Mass Range 65−60065-600 GeV with the ATLAS Detector in pppp Collision Data at s\sqrt{s} = 8 TeVTeV

A search for scalar particles decaying via narrow resonances into two photons in the mass range 65–600 GeV is performed using $20.3\text{}\text{}{\mathrm{fb}}^{-1}$ of $\sqrt{s}=8\text{}\text{}\mathrm{TeV}$ $pp$ collision data collected with the ATLAS detector at the Large Hadron Collider. The recently discovered Higgs boson is treated as a background. No significant evidence for an additional signal is observed. The results are presented as limits at the 95% confidence level on the production cross section of a scalar boson times branching ratio into two photons, in a fiducial volume where the reconstruction efficiency is approximately independent of the event topology. The upper limits set extend over a considerably wider mass range than previous searches