High-Dimensional Forecasting in the Presence of Unit Roots and Cointegration. (arXiv:1911.10552v1 [econ.EM])

We investigate how the possible presence of unit roots and cointegration affects forecasting with Big Data. As most macroeoconomic time series are very persistent and may contain unit roots, a proper handling of unit roots and cointegration is of paramount importance for macroeconomic forecasting. The high-dimensional nature of Big Data complicates the analysis of unit…

Scalable sim-to-real transfer of soft robot designs. (arXiv:1911.10290v1 [cs.RO])

The manual design of soft robots and their controllers is notoriously challenging, but it could be augmented—or, in some cases, entirely replaced—by automated design tools. Machine learning algorithms can automatically propose, test, and refine designs in simulation, and the most promising ones can then be manufactured in reality (sim2real). However, it is currently not known…

Predicting bubble bursts in oil prices using mixed causal-noncausal models. (arXiv:1911.10916v1 [econ.EM])

This paper investigates oil price series using mixed causal-noncausal autoregressive (MAR) models, namely dynamic processes that depend not only on their lags but also on their leads. MAR models have been successfully implemented on commodity prices as they allow to generate nonlinear features such as speculative bubbles. We estimate the probabilities that bubbles in oil…

Invert and Defend: Model-based Approximate Inversion of Generative Adversarial Networks for Secure Inference. (arXiv:1911.10291v1 [cs.CV])

Inferring the latent variable generating a given test sample is a challenging problem in Generative Adversarial Networks (GANs). In this paper, we propose InvGAN – a novel framework for solving the inference problem in GANs, which involves training an encoder network capable of inverting a pre-trained generator network without access to any training data. Under…

Identification of hedonic equilibrium and nonseparable simultaneous equations. (arXiv:1709.09570v3 [econ.EM] UPDATED)

This paper derives conditions under which preferences and technology are nonparametrically identified in hedonic equilibrium models, where products are differentiated along more than one dimension and agents are characterized by several dimensions of unobserved heterogeneity. With products differentiated along a quality index and agents characterized by scalar unobserved heterogeneity, single crossing conditions on preferences and…

A Domain Adaptive Density Clustering Algorithm for Data with Varying Density Distribution. (arXiv:1911.10293v1 [cs.LG])

As one type of efficient unsupervised learning methods, clustering algorithms have been widely used in data mining and knowledge discovery with noticeable advantages. However, clustering algorithms based on density peak have limited clustering effect on data with varying density distribution (VDD), equilibrium distribution (ED), and multiple domain-density maximums (MDDM), leading to the problems of sparse…

Autoregressive Wild Bootstrap Inference for Nonparametric Trends. (arXiv:1807.02357v2 [stat.ME] UPDATED)

In this paper we propose an autoregressive wild bootstrap method to construct confidence bands around a smooth deterministic trend. The bootstrap method is easy to implement and does not require any adjustments in the presence of missing data, which makes it particularly suitable for climatological applications. We establish the asymptotic validity of the bootstrap method…

CoverNet: Multimodal Behavior Prediction using Trajectory Sets. (arXiv:1911.10298v1 [cs.LG])

We present CoverNet, a new method for multimodal, probabilistic trajectory prediction in urban driving scenarios. Previous work has employed a variety of methods, including multimodal regression, occupancy maps, and 1-step stochastic policies. We instead frame the trajectory prediction problem as classification over a diverse set of trajectories. The size of this set remains manageable, due…

The route to chaos in routing games: When is Price of Anarchy too optimistic?. (arXiv:1906.02486v2 [cs.GT] UPDATED)

Routing games are amongst the most studied classes of games. Their two most well-known properties are that learning dynamics converge to equilibria and that all equilibria are approximately optimal. In this work, we perform a stress test for these classic results by studying the ubiquitous dynamics, Multiplicative Weights Update, in different classes of congestion games,…

Learning a Representation with the Block-Diagonal Structure for Pattern Classification. (arXiv:1911.10301v1 [cs.CV])

Sparse-representation-based classification (SRC) has been widely studied and developed for various practical signal classification applications. However, the performance of a SRC-based method is degraded when both the training and test data are corrupted. To counteract this problem, we propose an approach that learns Representation with Block-Diagonal Structure (RBDS) for robust image recognition. To be more…