518 research outputs found
Generating Artificial Data for Private Deep Learning
In this paper, we propose generating artificial data that retain statistical
properties of real data as the means of providing privacy with respect to the
original dataset. We use generative adversarial network to draw
privacy-preserving artificial data samples and derive an empirical method to
assess the risk of information disclosure in a differential-privacy-like way.
Our experiments show that we are able to generate artificial data of high
quality and successfully train and validate machine learning models on this
data while limiting potential privacy loss.Comment: Privacy-Enhancing Artificial Intelligence and Language Technologies,
AAAI Spring Symposium Series, 201
Partial Truthfulness in Minimal Peer Prediction Mechanisms with Limited Knowledge
We study minimal single-task peer prediction mechanisms that have limited
knowledge about agents' beliefs. Without knowing what agents' beliefs are or
eliciting additional information, it is not possible to design a truthful
mechanism in a Bayesian-Nash sense. We go beyond truthfulness and explore
equilibrium strategy profiles that are only partially truthful. Using the
results from the multi-armed bandit literature, we give a characterization of
how inefficient these equilibria are comparing to truthful reporting. We
measure the inefficiency of such strategies by counting the number of dishonest
reports that any minimal knowledge-bounded mechanism must have. We show that
the order of this number is , where is the number of
agents, and we provide a peer prediction mechanism that achieves this bound in
expectation
Learning to cope with an open world
Science has developed detailed and well-founded theories for analyzing the behavior of artifacts. For example, Boeing was able to correctly verify an entirely new airplane, the Boeing 777, before any prototype was even built. However, there are few theories, and no computer systems, that would allow us to design structures with a similar degree of automatio
Exploring case-Based building designâCADRE
Case-based design promises important advantages over rule-based design systems. However, the actual implementation of the paradigm poses many problems which put the advantages into question. In our work on CADRE, a case-based building design system, we have encountered seven fundamental problems which we think are common to most case-based design systems. We describe the problems and the ways we either solved or worked around them in the CADRE system. This leads us to conclusions about the general applicability of case-based reasoning to building desig
Courtesy as a Means to Coordinate
We investigate the problem of multi-agent coordination under rationality
constraints. Specifically, role allocation, task assignment, resource
allocation, etc. Inspired by human behavior, we propose a framework (CA^3NONY)
that enables fast convergence to efficient and fair allocations based on a
simple convention of courtesy. We prove that following such convention induces
a strategy which constitutes an -subgame-perfect equilibrium of the
repeated allocation game with discounting. Simulation results highlight the
effectiveness of CA^3NONY as compared to state-of-the-art bandit algorithms,
since it achieves more than two orders of magnitude faster convergence, higher
efficiency, fairness, and average payoff.Comment: Accepted at AAMAS 2019 (International Conference on Autonomous Agents
and Multiagent Systems
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