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Feb 21, 2020
Robotics
Systems and Control

Upset Recovery Control for Quadrotors Subjected to a Complete Rotor …

This study has developed a fault-tolerant controller that is able to recover a quadrotor from arbitrary initial orientations and angular velocities, …
Feb 21, 2020
Machine Learning

Robustness from Simple Classifiers

Despite the vast success of Deep Neural Networks in numerous application domains, it has been shown that such models are not robust i.e., they are vu…
Feb 21, 2020
Machine Learning

A Multiclass Classification Approach to Label Ranking

In multiclass classification, the goal is to learn how to predict a random label $Y$, valued in $\mathcal{Y}=\{1,\; \ldots,\; K \}$ with $K\geq 3$, b…
Feb 21, 2020
Machine Learning
Neural and Evolutionary Computation

It's Not What Machines Can Learn, It's What We Cannot Teach

Can deep neural networks learn to solve any task, and in particular problems of high complexity? This question attracts a lot of interest, with recen…
Feb 21, 2020
Databases
Computation and Language

Crowdsourced Collective Entity Resolution with Relational Match Pro…

Knowledge bases (KBs) store rich yet heterogeneous entities and facts. Entity resolution (ER) aims to identify entities in KBs which refer to the sam…
Feb 21, 2020
Methodology
Machine Learning

Estimation of conditional mixture Weibull distribution with right-c…

In this paper, we consider survival analysis with right-censored data which is a common situation in predictive maintenance and health field. We prop…
Feb 21, 2020
Machine Learning

Robust Optimization for Fairness with Noisy Protected Groups

Many existing fairness criteria for machine learning involve equalizing or achieving some metric across \textit{protected groups} such as race or gen…
Feb 21, 2020
Statistics Theory
Machine Learning
Probability

Generalisation error in learning with random features and the hidden …

We study generalised linear regression and classification for a synthetically generated dataset encompassing different problems of interest, such as …
Feb 21, 2020
Artificial Intelligence

An Advance on Variable Elimination with Applications to Tensor-Based …

We present new results on the classical algorithm of variable elimination, which underlies many algorithms including for probabilistic inference. The…
Feb 21, 2020
Machine Learning
Computation

Efficiently sampling functions from Gaussian process posteriors

Gaussian processes are the gold standard for many real-world modeling problems, especially in cases where a model's success hinges upon its ability t…
Feb 21, 2020
Machine Learning

Transformer Hawkes Process

Modern data acquisition routinely produce massive amounts of event sequence data in various domains, such as social media, healthcare, and financial …
Feb 21, 2020
Computer Vision

Learning to Inpaint by Progressively Growing the Mask Regions

Image inpainting is one of the most challenging tasks in computer vision. Recently, generative-based image inpainting methods have been shown to prod…
Feb 21, 2020
Data Structures and Algorithms
Cryptography and Security
Machine Learning

The authors would like to thank Kunal Talwar for suggesting the study of this problem, and Adam Klivans, Frederic Koehler, Ankur Moitra, and Shanshan Wu for helpful and inspiring conversations. {CJK*}UTF8gbsn GK would like to thank Chengdu Style Res…

Privately Learning Markov Random Fields

We consider the problem of learning Markov Random Fields (including the prototypical example, the Ising model) under the constraint of differential p…
Feb 21, 2020
Statistics Theory
Applications
Methodology
Machine Learning

Aggregation of Multiple Knockoffs

We develop an extension of the Knockoff Inference procedure, introduced by Barber and Candes (2015). This new method, called Aggregation of Multiple …
Feb 21, 2020
Artificial Intelligence
Computation and Language

Language as a Cognitive Tool to Imagine Goals in Curiosity-Driven E…

Autonomous reinforcement learning agents must be intrinsically motivated to explore their environment, discover potential goals, represent them and l…
Feb 21, 2020
Machine Learning
Computer Vision

Efficient Learning of Model Weights via Changing Features During Tr…

In this paper, we propose a machine learning model, which dynamically changes the features during training. Our main motivation is to update the mode…
Feb 21, 2020
Computation and Language
Artificial Intelligence
Machine Learning

This paper presents a comprehensive analysis and comparison of the existing models for aligning embedding spaces w.r.t. the research questions mentioned in Sect. 2. This section summarizes the lessons learnt from the comparison and analysis of diffe…

Is Aligning Embedding Spaces a Challenging Task? An Analysis of the …

Representation Learning of words and Knowledge Graphs (KG) into low dimensional vector spaces along with its applications to many real-world scenario…
Feb 21, 2020
Machine Learning
Computer Vision

Exploiting the Full Capacity of Deep Neural Networks while Avoiding …

Overfitting is one of the most common problems when training deep neural networks on comparatively small datasets. Here, we demonstrate that neural n…
Feb 21, 2020
Neural and Evolutionary Computation
Methodology

Recent Trends in the Use of Statistical Tests for Comparing Swarm and…

A key aspect of the design of evolutionary and swarm intelligence algorithms is studying their performance. Statistical comparisons are also a crucia…
Feb 21, 2020
Statistics Theory
Machine Learning
Econometric

Kernel Conditional Moment Test via Maximum Moment Restriction

We propose a new family of specification tests called kernel conditional moment (KCM) tests. Our tests are built on conditional moment embeddings (CM…
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