707,587 research outputs found
Online Causal Structure Learning in the Presence of Latent Variables
We present two online causal structure learning algorithms which can track
changes in a causal structure and process data in a dynamic real-time manner.
Standard causal structure learning algorithms assume that causal structure does
not change during the data collection process, but in real-world scenarios, it
does often change. Therefore, it is inappropriate to handle such changes with
existing batch-learning approaches, and instead, a structure should be learned
in an online manner. The online causal structure learning algorithms we present
here can revise correlation values without reprocessing the entire dataset and
use an existing model to avoid relearning the causal links in the prior model,
which still fit data. Proposed algorithms are tested on synthetic and
real-world datasets, the latter being a seasonally adjusted commodity price
index dataset for the U.S. The online causal structure learning algorithms
outperformed standard FCI by a large margin in learning the changed causal
structure correctly and efficiently when latent variables were present.Comment: 16 pages, 9 figures, 2 table
The Practice of Telecommuting: A Fresh Perspective
Telecommuting has been a popular practice for an increasing number of firms and governmental bodies over the past decade or more. This research paper reviews antecedents, implementation considerations, known consequences, barriers, and recommendations that need to be determined prior to the adoption of telecommuting practices. The paper demonstrates that the phenomenon of telecommuting is the result of historical, sociological, and technological shifts and advancements. While firms have successfully implemented various elements of telecommuting practices, challenges along the way have yielded insights and lessons that merit further examination and discussion. This paper asserts that with selected individuals, proper structure, and sufficient feedback mechanisms in place, the adoption of telecommuting has the capacity to strengthen a firm’s bottom line and provide tangible benefit for its employees. As a case in point, online learning, developed in parallel with the growth of telecommuting, yields substantial benefits for employees and the companies in which they serve. For employees, online learning is convenient, accommodates multiple learning styles, and is an engaging learning mechanism. For corporations, online learning encourages cost-effectiveness, uniformity in quality and flexibility, and enhanced cross-cultural and cross-disciplinary communications, all necessary to meet the challenges of the ever-changing global marketplace.telecommuting; technology; online learning; social media; innovation; institutional learning; cross-cultural communications.
Machine Learning Methods for Attack Detection in the Smart Grid
Attack detection problems in the smart grid are posed as statistical learning
problems for different attack scenarios in which the measurements are observed
in batch or online settings. In this approach, machine learning algorithms are
used to classify measurements as being either secure or attacked. An attack
detection framework is provided to exploit any available prior knowledge about
the system and surmount constraints arising from the sparse structure of the
problem in the proposed approach. Well-known batch and online learning
algorithms (supervised and semi-supervised) are employed with decision and
feature level fusion to model the attack detection problem. The relationships
between statistical and geometric properties of attack vectors employed in the
attack scenarios and learning algorithms are analyzed to detect unobservable
attacks using statistical learning methods. The proposed algorithms are
examined on various IEEE test systems. Experimental analyses show that machine
learning algorithms can detect attacks with performances higher than the attack
detection algorithms which employ state vector estimation methods in the
proposed attack detection framework.Comment: 14 pages, 11 Figure
Online Adaptive Mahalanobis Distance Estimation
Mahalanobis metrics are widely used in machine learning in conjunction with
methods like -nearest neighbors, -means clustering, and -medians
clustering. Despite their importance, there has not been any prior work on
applying sketching techniques to speed up algorithms for Mahalanobis metrics.
In this paper, we initiate the study of dimension reduction for Mahalanobis
metrics. In particular, we provide efficient data structures for solving the
Approximate Distance Estimation (ADE) problem for Mahalanobis distances. We
first provide a randomized Monte Carlo data structure. Then, we show how we can
adapt it to provide our main data structure which can handle sequences of
\textit{adaptive} queries and also online updates to both the Mahalanobis
metric matrix and the data points, making it amenable to be used in conjunction
with prior algorithms for online learning of Mahalanobis metrics
The Fast and the Flexible: training neural networks to learn to follow instructions from small data
Learning to follow human instructions is a long-pursued goal in artificial
intelligence. The task becomes particularly challenging if no prior knowledge
of the employed language is assumed while relying only on a handful of examples
to learn from. Work in the past has relied on hand-coded components or manually
engineered features to provide strong inductive biases that make learning in
such situations possible. In contrast, here we seek to establish whether this
knowledge can be acquired automatically by a neural network system through a
two phase training procedure: A (slow) offline learning stage where the network
learns about the general structure of the task and a (fast) online adaptation
phase where the network learns the language of a new given speaker. Controlled
experiments show that when the network is exposed to familiar instructions but
containing novel words, the model adapts very efficiently to the new
vocabulary. Moreover, even for human speakers whose language usage can depart
significantly from our artificial training language, our network can still make
use of its automatically acquired inductive bias to learn to follow
instructions more effectively
Prior knowledge and preferential structures in gradient descent learning algorithms
A family of gradient descent algorithms for learning linear functions in an online setting is
considered. The family includes the classical LMS algorithm as well as new variants such as
the Exponentiated Gradient (EG) algorithm due to Kivinen and Warmuth. The algorithms
are based on prior distributions defined on the weight space. Techniques from differential
geometry are used to develop the algorithms as gradient descent iterations with respect to
the natural gradient in the Riemannian structure induced by the prior distribution. The
proposed framework subsumes the notion of "link-functions"
Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning
In goal-reaching reinforcement learning (RL), the optimal value function has
a particular geometry, called quasimetric structure. This paper introduces
Quasimetric Reinforcement Learning (QRL), a new RL method that utilizes
quasimetric models to learn optimal value functions. Distinct from prior
approaches, the QRL objective is specifically designed for quasimetrics, and
provides strong theoretical recovery guarantees. Empirically, we conduct
thorough analyses on a discretized MountainCar environment, identifying
properties of QRL and its advantages over alternatives. On offline and online
goal-reaching benchmarks, QRL also demonstrates improved sample efficiency and
performance, across both state-based and image-based observations.Comment: Project Page: https://www.tongzhouwang.info/quasimetric_rl
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