activity
20162019
most citedGrowth-Optimal Portfolio Selection under CVaR Constraints

5 citations · 8 across the 4 of their papers we have counts for

collaborators

6 papers

cs.LG2019

Nonparametric Online Learning Using Lipschitz Regularized Deep Neural Networks

Guy Uziel

Deep neural networks are considered to be state of the art models in many offline machine learning tasks. However, their performance and generalization abilities in online learning…

cs.LG20191 cited

Deep Online Learning with Stochastic Constraints

Guy Uziel

Deep learning models are considered to be state-of-the-art in many offline machine learning tasks. However, many of the techniques developed are not suitable for online learning ta…

q-fin.MF20175 cited

Growth-Optimal Portfolio Selection under CVaR Constraints

Guy Uziel, Ran El-Yaniv

Online portfolio selection research has so far focused mainly on minimizing regret defined in terms of wealth growth. Practical financial decision making, however, is deeply concer…

cs.LG20172 cited

Multi-Objective Non-parametric Sequential Prediction

Guy Uziel, Ran El-Yaniv

Online-learning research has mainly been focusing on minimizing one objective function. In many real-world applications, however, several objective functions have to be considered…

cs.AI2016

Online Learning of Commission Avoidant Portfolio Ensembles

Guy Uziel, Ran El-Yaniv

We present a novel online ensemble learning strategy for portfolio selection. The new strategy controls and exploits any set of commission-oblivious portfolio selection algorithms.…

cs.LG2016

Online Learning of Portfolio Ensembles with Sector Exposure Regularization

Guy Uziel, Ran El-Yaniv

We consider online learning of ensembles of portfolio selection algorithms and aim to regularize risk by encouraging diversification with respect to a predefined risk-driven groupi…