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Found 12 papers, 4 papers with code
A Bayesian theory of market impact
The available liquidity at any time in financial markets falls largely short of the typical size of the orders that institutional investors would trade.
A simple probabilistic neural network for machine understanding
We discuss probabilistic neural networks with a fixed internal representation as models for machine understanding.
Quantifying Relevance in Learning and Inference
This identifies samples obeying Zipf's law as the most compressed loss-less representations that are optimal in the sense of maximal relevance.
A random energy approach to deep learning
We study a generic ensemble of deep belief networks which is parametrized by the distribution of energy levels of the hidden states of each layer.
Statistical Inference of Minimally Complex Models
These are spin models, with interactions of arbitrary order, that are composed of independent components of minimal complexity (Beretta et al., 2018).
On the complexity of logistic regression models
We investigate the complexity of logistic regression models which is defined by counting the number of indistinguishable distributions that the model can represent (Balasubramanian, 1997).
Minimum Description Length codes are critical
We show that the codes that achieve optimal compression in MDL are critical in a very precise sense.
Methodology Data Analysis, Statistics and Probability
Resolution and Relevance Trade-offs in Deep Learning
The fraction of inputs that are associated to the same state is a natural measure of similarity and is simply related to the cost in bits required to represent these inputs.
The Stochastic complexity of spin models: Are pairwise models really simple?
We thus found that the complexity (or simplicity) of a model is not determined by the order of the interactions, but rather by their mutual arrangements.
Sparse model selection in the highly under-sampled regime
We propose a method for recovering the structure of a sparse undirected graphical model when very few samples are available.
$L_p$ regularized portfolio optimization
Investors who optimize their portfolios under any of the coherent risk measures are naturally led to regularized portfolio optimization when they take into account the impact their trades make on the market.
Portfolio Management Risk Management
Data clustering and noise undressing for correlation matrices
We discuss a new approach to data clustering.
Statistical Mechanics Disordered Systems and Neural Networks
