Optimal and robust memoryless discrimination from dependent observations

Optimal and robust memoryless discrimination from dependent observations

Learning from dependent observations

In most papers establishing consistency for learning algorithms it is assumed that the observations used for training are realizations of an i.i.d. process. In this paper we go far beyond this classical framework by showing that support vector machines (SVMs) essentially only require that the data-generating process satisfies a certain law of large numbers. We then consider the learnability of ...

Learning from Dependent Observations

09:00 – 09:55 Registration and coffee-break 09:55 – 10:00 Opening remarks 10:00 – 10:50 Emmanuel Candes (California Institute of Technology) The Dantzig Selector: Statistical Estimation when p is Larger than n 11:00 – 11:30 Coffee-break 11:30 – 12:20 Franck Barthe (Université Toulouse III) About Talagrand’s Concentration Inequality for Exponential Measures 12:30 – 14:30 Lunch break 14:30 – 15:2...

Optimal Change-Point Estimation from Indirect Observations

We study nonparametric change-point estimation from indirect noisy observations. Focusing on the white noise convolution model, we consider two classes of functions that are smooth apart from the change-point. We establish lower bounds on the minimax risk in estimating the change-point and develop rate optimal estimation procedures. The results demonstrate that the best achievable rates of conv...

Robust discrimination designs

We study the construction of experimental designs, the purpose of which is to aid in the discrimination between two possibly non-linear regression models, each of which might be only approximately specified. A rough description of our approach is that we impose neighbourhood structures on each regression response and determine the members of these neighbourhoods which are least favourable in th...