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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
Robotics
Systems and Control

An upset recovery control method for a quadrotor with one rotor failure has been proposed and tested in this research. The controller can stabilize the quadrotor from arbitrary initial orientations and a wide range of angular velocities to the relax…

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

In this paper, we investigate the connection between robustness and simplicity and show an inverse relationship, i.e., it is increasingly difficult to perturb an example when decreasing the complexity of the classification task. Motivated by this ob…

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

In this paper, a statistical problem halfway between multiclass classification and posterior probability estimation, referred to as label ranking here, is considered. The goal is to design a method to rank, for any test observation X, all the labels…

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 20, 2020
Machine Learning

Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via …

Common Stochastic Gradient MCMC methods approximate gradients by stochastic ones via uniformly subsampled data points. We propose that a non-uniform …
Feb 20, 2020
Machine Learning
Artificial Intelligence

I-SPEC: An End-to-End Framework for Learning Transportable, Shift-Sta…

Shifts in environment between development and deployment cause classical supervised learning to produce models that fail to generalize well to new ta…
Feb 20, 2020
Robotics

Reactive Navigation in Partially Familiar Planar Environments Using …

This paper solves the planar navigation problem by recourse to an online reactive scheme that exploits recent advances in SLAM and visual object reco…
Feb 20, 2020
Computer Vision

Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction

Reasoning over visual data is a desirable capability for robotics and vision-based applications. Such reasoning enables forecasting of the next event…
Feb 20, 2020
Machine Learning

Implicit differentiation of Lasso-type models for hyperparameter op…

Setting regularization parameters for Lasso-type estimators is notoriously difficult, though crucial in practice. The most popular hyperparameter opt…
Feb 20, 2020
Machine Learning

Nyström Subspace Learning for Large-scale SVMs

As an implementation of the Nystr\"{o}m method, Nystr\"{o}m computational regularization (NCR) imposed on kernel classification and kernel ridge regr…
Feb 20, 2020
Machine Learning

Meta-learning for mixed linear regression

In modern supervised learning, there are a large number of tasks, but many of them are associated with only a small amount of labeled data. These inc…
Feb 20, 2020
Computer Vision

Strategy to Increase the Safety of a DNN-based Perception for HAD S…

Safety is one of the most important development goals for highly automated driving (HAD) systems. This applies in particular to the perception functi…
Feb 20, 2020
Machine Learning

Online high rank matrix completion

Recent advances in matrix completion enable data imputation in full-rank matrices by exploiting low dimensional (nonlinear) latent structure. In this…
Feb 20, 2020
Audio and Speech Processing
Computation and Language
Machine Learning
Sound

Wavesplit: End-to-End Speech Separation by Speaker Clustering

We introduce Wavesplit, an end-to-end speech separation system. From a single recording of mixed speech, the model infers and clusters representation…
Feb 20, 2020
Machine Learning

Multi-step Online Unsupervised Domain Adaptation

In this paper, we address the Online Unsupervised Domain Adaptation (OUDA) problem, where the target data are unlabelled and arriving sequentially. T…
Feb 20, 2020
Machine Learning

Regularized Autoencoders via Relaxed Injective Probability Flow

Invertible flow-based generative models are an effective method for learning to generate samples, while allowing for tractable likelihood computation…
Feb 20, 2020
Computer Vision

The results presented in this paper allow us to provide the following answers to two questions posed in the introduction:

Deep Learning-Based Feature Extraction in Iris Recognition: Use Exist…

Modern deep learning techniques can be employed to generate effective feature extractors for the task of iris recognition. The question arises: shoul…
Feb 20, 2020
Optimization and Control
Machine Learning

Second-order Conditional Gradients

Constrained second-order convex optimization algorithms are the method of choice when a high accuracy solution to a problem is needed, due to the qua…
Feb 20, 2020
Computation and Language

The Fluidity of Concept Representations in Human Brain Signals

Cognitive theories of human language processing often distinguish between concrete and abstract concepts. In this work, we analyze the discriminabili…
Feb 20, 2020
Machine Learning

Fast Differentiable Sorting and Ranking

The sorting operation is one of the most basic and commonly used building blocks in computer programming. In machine learning, it is commonly used fo…
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