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Financial Risk Management with Machine Learning

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Deep(ly) Learning the Depth

Xun Victor Suo

We report on the use of deep learning algorithms to perform depth recovery in multiview imaging. We show that if enough training data are provided, a neural network such as multilayer perceptron can be trained to recover the depth in multiview imaging as a ...

2016

Stealing Machine Learning Models via Prediction APIs

Florian Tramèr, Fan Zhang, Ari Juels

Machine learning (ML) models may be deemed confidential due to their sensitive training data, commercial value, or use in security applications. Increasingly often, confidential ML models are being deployed with publicly accessible query interfaces. ML-as- ...

Usenix Assoc2016

Infrequent Rebalancing, Return Autocorrelation, and Seasonality

Vincent Jean Bogousslavsky

A model of infrequent rebalancing can explain specific predictability patterns in the time series and cross-section of stock returns. First, infrequent rebalancing produces return autocorrelations that are consistent with empirical evidence from intraday r ...

Wiley-Blackwell2016

Implementing Neural Networks Efficiently

Ronan Collobert

Neural networks and machine learning algorithms in general require a flexible environment where new algorithm prototypes and experiments can be set up as quickly as possible with best possible computational performance. To that end, we provide a new framew ...

Springer2012

Multi-Level Error-Resilient Neural Networks

Amin Karbasi, Amir Hesam Salavati

The problem of neural network association is to retrieve a previously memorized pattern from its noisy version using a network of neurons. An ideal neural network should include three components simultaneously: a learning algorithm, a large pattern retriev ...

IEEE2012

Been There, Done That: What Your Mobility Traces Reveal about Your Behavior

Ehsan Kazemi, Vincent Etter, Mohamed Kafsi

Mobility is a central aspect of our life; the locations we visit reflect our tastes and lifestyle and shape our social relationships. The ability to foresee the places a user will visit is therefore beneficial to numerous applications, ranging from forecas ...

2012

Incomplete information, idiosyncratic volatility and stock returns

Julien Hugonnier, Tony Berrada

When investors have incomplete information, expected returns, as measured by an econometrician, deviate from those predicted by standard asset pricing models by including a term that is the product of the stock's idiosyncratic volatility and the investors' ...

2012

Continuous-time distributed estimation with asymmetric mixing

Ali H. Sayed

Discrete-time mobile adaptive networks have been successfully used to model self-organization in biological networks. We recently introduced a continuous-time adaptive diffusion strategy with the goal of better modeling physical phenomena governed by conti ...

IEEE2012

Corporate Finance, Asset Returns, and Credit Risk

Philip Valta

This dissertation consists of three chapters. The first chapter empirically investigates how the intensity of product market competition affects the cost of debt. Using a large sample of loans to publicly traded US manufacturing firms, the chapter provides ...

EPFL2010

Modelling human perception of static facial expressions

Michel Bierlaire, Jean-Philippe Thiran, Matteo Sorci, Gianluca Antonini, Javier Cruz Mota, Thomas Robin

A recent internet based survey of over 35'000 samples has shown that when different human observers are asked to assign labels to static human facial expressions, different individuals categorize differently the same image. This fact results in a lack of a ...

2010