3 accepted papers
We study the task of learning the structure of a $d$-sparse Gaussian graphical model on $n$ variables from a single trajectory of Glauber dynamics. Beyond algorithmic considerations, many applications present temporally correlated observations rather than i.i.d. samples. Moreover, in the classical i…
Self-supervised learning methods that mask parts of the input data and train models to predict the missing components have led to significant advances in machine learning. These approaches learn conditional distributions $p(x_T \mid x_S)$ simultaneously, where $x_S$ and $x_T$ are subsets of the obse…
We study the problem of learning mixtures of Gaussians with approximate differential privacy. We prove that roughly $kd^2 + k^{1.5} d^{1.75} + k^2 d$ samples suffice to learn a mixture of $k$ arbitrary $d$-dimensional Gaussians up to low total variation distance, with differential privacy. Our work…