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Found 8 papers, 0 papers with code
Exact and efficient phylodynamic simulation from arbitrarily large populations
Here, we address this challenge by proving that, for any partially ascertained process from a general multi-type birth-death-mutation-sampling model, there exists an equivalent process with complete sampling and no death, a property which we leverage to develop a highly efficient algorithm for simulating trees.
Mean-field variational inference with the TAP free energy: Geometric and statistical properties in linear models
In settings where it is conjectured that no efficient algorithm can find this local neighborhood, we prove analogous geometric properties for a local minimizer of the TAP free energy reachable by AMP, and show that posterior inference based on this minimizer remains correctly calibrated.
Maximum Mean Discrepancy Meets Neural Networks: The Radon-Kolmogorov-Smirnov Test
Maximum mean discrepancy (MMD) refers to a general class of nonparametric two-sample tests that are based on maximizing the mean difference over samples from one distribution $P$ versus another $Q$, over all choices of data transformations $f$ living in some function space $\mathcal{F}$.
Sudakov-Fernique post-AMP, and a new proof of the local convexity of the TAP free energy
As an example of its use, we provide a new, and arguably simpler, proof of some of the results of Celentano et al. (2021), which establishes that the so-called TAP free energy in the $\mathbb{Z}_2$-synchronization problem is locally convex in the region to which AMP converges.
Local convexity of the TAP free energy and AMP convergence for Z2-synchronization
This provides a rigorous foundation for variational inference in high dimensions via minimization of the TAP free energy.
Minimum complexity interpolation in random features models
We study random features approximations to these norms and show that, for $p>1$, the number of random features required to approximate the original learning problem is upper bounded by a polynomial in the sample size.
The Lasso with general Gaussian designs with applications to hypothesis testing
On the other hand, the Lasso estimator can be precisely characterized in the regime in which both $n$ and $p$ are large and $n/p$ is of order one.
The estimation error of general first order methods
These lower bounds are optimal in the sense that there exist algorithms whose estimation error matches the lower bounds up to asymptotically negligible terms.
