404 research outputs found
A Contextual Bandit Approach for Value-oriented Prediction Interval Forecasting
Prediction interval (PI) is an effective tool to quantify uncertainty and
usually serves as an input to downstream robust optimization. Traditional
approaches focus on improving the quality of PI in the view of statistical
scores and assume the improvement in quality will lead to a higher value in the
power systems operation. However, such an assumption cannot always hold in
practice. In this paper, we propose a value-oriented PI forecasting approach,
which aims at reducing operational costs in downstream operations. For that, it
is required to issue PIs with the guidance of operational costs in robust
optimization, which is addressed within the contextual bandit framework here.
Concretely, the agent is used to select the optimal quantile proportion, while
the environment reveals the costs in operations as rewards to the agent. As
such, the agent can learn the policy of quantile proportion selection for
minimizing the operational cost. The numerical study regarding a two-timescale
operation of a virtual power plant verifies the superiority of the proposed
approach in terms of operational value. And it is especially evident in the
context of extensive penetration of wind power.Comment: submitted to IEEE Transactions on Smart Gri
Easy Learning from Label Proportions
We consider the problem of Learning from Label Proportions (LLP), a weakly
supervised classification setup where instances are grouped into "bags", and
only the frequency of class labels at each bag is available. Albeit, the
objective of the learner is to achieve low task loss at an individual instance
level. Here we propose Easyllp: a flexible and simple-to-implement debiasing
approach based on aggregate labels, which operates on arbitrary loss functions.
Our technique allows us to accurately estimate the expected loss of an
arbitrary model at an individual level. We showcase the flexibility of our
approach by applying it to popular learning frameworks, like Empirical Risk
Minimization (ERM) and Stochastic Gradient Descent (SGD) with provable
guarantees on instance level performance. More concretely, we exhibit a
variance reduction technique that makes the quality of LLP learning deteriorate
only by a factor of k (k being bag size) in both ERM and SGD setups, as
compared to full supervision. Finally, we validate our theoretical results on
multiple datasets demonstrating our algorithm performs as well or better than
previous LLP approaches in spite of its simplicity
Testing and Learning on Distributions with Symmetric Noise Invariance
Kernel embeddings of distributions and the Maximum Mean Discrepancy (MMD),
the resulting distance between distributions, are useful tools for fully
nonparametric two-sample testing and learning on distributions. However, it is
rarely that all possible differences between samples are of interest --
discovered differences can be due to different types of measurement noise, data
collection artefacts or other irrelevant sources of variability. We propose
distances between distributions which encode invariance to additive symmetric
noise, aimed at testing whether the assumed true underlying processes differ.
Moreover, we construct invariant features of distributions, leading to learning
algorithms robust to the impairment of the input distributions with symmetric
additive noise.Comment: 22 page
Regression with Sensor Data Containing Incomplete Observations
This paper addresses a regression problem in which output label values are
the results of sensing the magnitude of a phenomenon. A low value of such
labels can mean either that the actual magnitude of the phenomenon was low or
that the sensor made an incomplete observation. This leads to a bias toward
lower values in labels and its resultant learning because labels may have lower
values due to incomplete observations, even if the actual magnitude of the
phenomenon was high. Moreover, because an incomplete observation does not
provide any tags indicating incompleteness, we cannot eliminate or impute them.
To address this issue, we propose a learning algorithm that explicitly models
incomplete observations corrupted with an asymmetric noise that always has a
negative value. We show that our algorithm is unbiased as if it were learned
from uncorrupted data that does not involve incomplete observations. We
demonstrate the advantages of our algorithm through numerical experiments
The effects of achievement goals and feedback on performance: with a prologue on an individual search for meaning
What does it mean to be an individual? The development of an individual is not something that occurs instantaneously. Instead, over time we use our experiences and life stories to help us define and elaborate on our identities. Goals entice people toward action. Actions are given meaning, direction, and purpose by the goals we seek. Every goal is a desired outcome situated in the future. By examining goals, we better understand a person\u27s needs and their motivation for their behavior. Our needs, and thus our goals, vary based on the situations we find ourselves in throughout our lives. By identifying our traits and knowing our goals, both past and present, we are able to weave together our storied self. Our storied self is our narrative identity. It is what separates us from every single other person who is currently living, has lived, or ever will live. Along each stage of life development, we add more experiences to our life stories in the hope that by the end of our lives we will have written a story that is personally meaningful and memorable. Thus, being an individual means writing a meaningful story, having the potential to live life with purpose, and, in the author\u27s case, living to seek the magis
Classifier Calibration: A survey on how to assess and improve predicted class probabilities
This paper provides both an introduction to and a detailed overview of the
principles and practice of classifier calibration. A well-calibrated classifier
correctly quantifies the level of uncertainty or confidence associated with its
instance-wise predictions. This is essential for critical applications, optimal
decision making, cost-sensitive classification, and for some types of context
change. Calibration research has a rich history which predates the birth of
machine learning as an academic field by decades. However, a recent increase in
the interest on calibration has led to new methods and the extension from
binary to the multiclass setting. The space of options and issues to consider
is large, and navigating it requires the right set of concepts and tools. We
provide both introductory material and up-to-date technical details of the main
concepts and methods, including proper scoring rules and other evaluation
metrics, visualisation approaches, a comprehensive account of post-hoc
calibration methods for binary and multiclass classification, and several
advanced topics
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