Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Perets, Binyamin"'
There is a well known intrinsic trade-off between the fairness of a representation and the performance of classifiers derived from the representation. Due to the complexity of optimisation algorithms in most modern representation learning approaches,
Externí odkaz:
http://arxiv.org/abs/2409.17643
We propose a new approach to non-parametric density estimation that is based on regularizing a Sobolev norm of the density. This method is statistically consistent, and makes the inductive bias of the model clear and interpretable. While there is no
Externí odkaz:
http://arxiv.org/abs/2307.13763
The Hidden Markov Model (HMM) is one of the most widely used statistical models for sequential data analysis. One of the key reasons for this versatility is the ability of HMM to deal with missing data. However, standard HMM learning algorithms rely
Externí odkaz:
http://arxiv.org/abs/2203.06527