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Best-first Enumeration Based on Bounding Conflicts, and its Application to Large-scale Hybrid Estimation
(2018-05-24)
With the rise of autonomous systems, there is a need for them to have high levels of robustness and safety. This robustness can be achieved through systems that are self-repairing. Underlying this is the ability to diagnose ...
Learning Models of Sequential Decision-Making without Complete State Specification using Bayesian Nonparametric Inference and Active Querying
(2018-05-17)
Learning models of decision-making behavior during sequential tasks is useful across a variety of applications, including human-machine interaction. In this paper, we present an approach to learning such models within ...
Risk Allocation for Temporal Risk Assessment
(2013-01-31)
Temporal uncertainty arises when performing any activity in the natural world. When activities are composed into temporal plans, then, there is a risk of not meeting the plan requirements. Currently, we do not have ...
Generalization in Deep Learning
(2018-05-01)
With a direct analysis of neural networks, this paper presents a mathematically tight generalization theory to partially address an open problem regarding the generalization of deep learning. Unlike previous bound-based ...
Data and Code for "A New Approach to Animacy Detection"
(2018-06-07)
This archive contains the code and data for the workshop article "A New Approach to Animacy Detection," published in 2018 in the the 27th International Conference on Computational Linguistics (COLING 2018), in Santa Fe, ...
Using Active Learning to Synthesize Models of Applications That Access Databases
(2018-08-28)
We present a new technique that uses active learning to infer models of
applications that manipulate relational databases. This technique
comprises a domain-specific language for modeling applications that
access ...
A Natural Language Interface for Mobile Devices
(2018-03-01)
Creating a robust, automated capability to respond to natural language requests has been a longstanding goal in the development of intelligent systems. This article describes the StartMobile system, originally developed ...
Towards Understanding Generalization via Analytical Learning Theory
(2018-10-01)
This paper introduces a novel measure-theoretic theory for machine learning
that does not require statistical assumptions. Based on this theory, a new
regularization method in deep learning is derived and shown to ...
Using Dynamic Monitoring to Synthesize Models of Applications That Access Databases
(2018-09-27)
We previously developed Konure, a tool that uses active learning to
infer the functionality of database applications. An alternative
approach is to observe the inputs, outputs, and database traffic from a
running ...
Gen: A General-Purpose Probabilistic Programming System with Programmable Inference
(2018-11-26)
Probabilistic modeling and inference are central to many fields. A key challenge for wider adoption of probabilistic programming languages is designing systems that are both flexible and performant. This paper introduces ...