35,674 research outputs found
Active Inverse Reward Design
Designers of AI agents often iterate on the reward function in a
trial-and-error process until they get the desired behavior, but this only
guarantees good behavior in the training environment. We propose structuring
this process as a series of queries asking the user to compare between
different reward functions. Thus we can actively select queries for maximum
informativeness about the true reward. In contrast to approaches asking the
designer for optimal behavior, this allows us to gather additional information
by eliciting preferences between suboptimal behaviors. After each query, we
need to update the posterior over the true reward function from observing the
proxy reward function chosen by the designer. The recently proposed Inverse
Reward Design (IRD) enables this. Our approach substantially outperforms IRD in
test environments. In particular, it can query the designer about
interpretable, linear reward functions and still infer non-linear ones
Efficient Batch Query Answering Under Differential Privacy
Differential privacy is a rigorous privacy condition achieved by randomizing
query answers. This paper develops efficient algorithms for answering multiple
queries under differential privacy with low error. We pursue this goal by
advancing a recent approach called the matrix mechanism, which generalizes
standard differentially private mechanisms. This new mechanism works by first
answering a different set of queries (a strategy) and then inferring the
answers to the desired workload of queries. Although a few strategies are known
to work well on specific workloads, finding the strategy which minimizes error
on an arbitrary workload is intractable. We prove a new lower bound on the
optimal error of this mechanism, and we propose an efficient algorithm that
approaches this bound for a wide range of workloads.Comment: 6 figues, 22 page
An Adaptive Mechanism for Accurate Query Answering under Differential Privacy
We propose a novel mechanism for answering sets of count- ing queries under
differential privacy. Given a workload of counting queries, the mechanism
automatically selects a different set of "strategy" queries to answer
privately, using those answers to derive answers to the workload. The main
algorithm proposed in this paper approximates the optimal strategy for any
workload of linear counting queries. With no cost to the privacy guarantee, the
mechanism improves significantly on prior approaches and achieves near-optimal
error for many workloads, when applied under (\epsilon, \delta)-differential
privacy. The result is an adaptive mechanism which can help users achieve good
utility without requiring that they reason carefully about the best formulation
of their task.Comment: VLDB2012. arXiv admin note: substantial text overlap with
arXiv:1103.136
Supporting Data mining of large databases by visual feedback queries
In this paper, we describe a query system that provides visual relevance feedback in querying large databases. Our goal is to support the process of data mining by representing as many data items as possible on the display. By arranging and coloring the data items as pixels according to their relevance for the query, the user gets a visual impression of the resulting data set. Using an interactive query interface, the user may change the query dynamically and receives immediate feedback by the visual representation of the resulting data set. Furthermore, by using multiple windows for different parts of a complex query, the user gets visual feedback for each part of the query and, therefore, may easier understand the overall result. Our system allows to represent the largest amount of data that can be visualized on current display technology, provides valuable feedback in querying the database, and allows the user to find results which, otherwise, would remain hidden in the database
A Complete Characterization of Statistical Query Learning with Applications to Evolvability
Statistical query (SQ) learning model of Kearns (1993) is a natural
restriction of the PAC learning model in which a learning algorithm is allowed
to obtain estimates of statistical properties of the examples but cannot see
the examples themselves. We describe a new and simple characterization of the
query complexity of learning in the SQ learning model. Unlike the previously
known bounds on SQ learning our characterization preserves the accuracy and the
efficiency of learning. The preservation of accuracy implies that that our
characterization gives the first characterization of SQ learning in the
agnostic learning framework. The preservation of efficiency is achieved using a
new boosting technique and allows us to derive a new approach to the design of
evolutionary algorithms in Valiant's (2006) model of evolvability. We use this
approach to demonstrate the existence of a large class of monotone evolutionary
learning algorithms based on square loss performance estimation. These results
differ significantly from the few known evolutionary algorithms and give
evidence that evolvability in Valiant's model is a more versatile phenomenon
than there had been previous reason to suspect.Comment: Simplified Lemma 3.8 and it's application
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