159,977 research outputs found
Algorithms & Fiduciaries: Existing and Proposed Regulatory Approaches to Artificially Intelligent Financial Planners
Artificial intelligence is no longer solely in the realm of science fiction. Today, basic forms of machine learning algorithms are commonly used by a variety of companies. Also, advanced forms of machine learning are increasingly making their way into the consumer sphere and promise to optimize existing markets. For financial advising, machine learning algorithms promise to make advice available 24–7 and significantly reduce costs, thereby opening the market for financial advice to lower-income individuals. However, the use of machine learning algorithms also raises concerns. Among them, whether these machine learning algorithms can meet the existing fiduciary standard imposed on human financial advisers and how responsibility and liability should be partitioned when an autonomous algorithm falls short of the fiduciary standard and harms a client. After summarizing the applicable law regulating investment advisers and the current state of robo-advising, this Note evaluates whether robo-advisers can meet the fiduciary standard and proposes alternate liability schemes for dealing with increasingly sophisticated machine learning algorithms
Schema Independent Relational Learning
Learning novel concepts and relations from relational databases is an
important problem with many applications in database systems and machine
learning. Relational learning algorithms learn the definition of a new relation
in terms of existing relations in the database. Nevertheless, the same data set
may be represented under different schemas for various reasons, such as
efficiency, data quality, and usability. Unfortunately, the output of current
relational learning algorithms tends to vary quite substantially over the
choice of schema, both in terms of learning accuracy and efficiency. This
variation complicates their off-the-shelf application. In this paper, we
introduce and formalize the property of schema independence of relational
learning algorithms, and study both the theoretical and empirical dependence of
existing algorithms on the common class of (de) composition schema
transformations. We study both sample-based learning algorithms, which learn
from sets of labeled examples, and query-based algorithms, which learn by
asking queries to an oracle. We prove that current relational learning
algorithms are generally not schema independent. For query-based learning
algorithms we show that the (de) composition transformations influence their
query complexity. We propose Castor, a sample-based relational learning
algorithm that achieves schema independence by leveraging data dependencies. We
support the theoretical results with an empirical study that demonstrates the
schema dependence/independence of several algorithms on existing benchmark and
real-world datasets under (de) compositions
Coherent control using adaptive learning algorithms
We have constructed an automated learning apparatus to control quantum
systems. By directing intense shaped ultrafast laser pulses into a variety of
samples and using a measurement of the system as a feedback signal, we are able
to reshape the laser pulses to direct the system into a desired state. The
feedback signal is the input to an adaptive learning algorithm. This algorithm
programs a computer-controlled, acousto-optic modulator pulse shaper. The
learning algorithm generates new shaped laser pulses based on the success of
previous pulses in achieving a predetermined goal.Comment: 19 pages (including 14 figures), REVTeX 3.1, updated conten
Robustness and Generalization
We derive generalization bounds for learning algorithms based on their
robustness: the property that if a testing sample is "similar" to a training
sample, then the testing error is close to the training error. This provides a
novel approach, different from the complexity or stability arguments, to study
generalization of learning algorithms. We further show that a weak notion of
robustness is both sufficient and necessary for generalizability, which implies
that robustness is a fundamental property for learning algorithms to work
Convergence of Unregularized Online Learning Algorithms
In this paper we study the convergence of online gradient descent algorithms
in reproducing kernel Hilbert spaces (RKHSs) without regularization. We
establish a sufficient condition and a necessary condition for the convergence
of excess generalization errors in expectation. A sufficient condition for the
almost sure convergence is also given. With high probability, we provide
explicit convergence rates of the excess generalization errors for both
averaged iterates and the last iterate, which in turn also imply convergence
rates with probability one. To our best knowledge, this is the first
high-probability convergence rate for the last iterate of online gradient
descent algorithms without strong convexity. Without any boundedness
assumptions on iterates, our results are derived by a novel use of two measures
of the algorithm's one-step progress, respectively by generalization errors and
by distances in RKHSs, where the variances of the involved martingales are
cancelled out by the descent property of the algorithm
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