arXiv research

Ranking items to be recommended to users is one of the main problems in large scale social media applications. This problem can be set up as a multi-objective optimization problem to allow for trading off multiple, potentially conflicting objectives (that are driven by those items) against each other. Most previous app…

New analysis improves denoising of modulo signals on graphs.

Algorithm recovers function samples from noisy modulo samples with high probability.

New algorithm reduces kernel optimization complexity.

Estimating Wasserstein distances between two high-dimensional densities suffers from the curse of dimensionality: one needs an exponential (wrt dimension) number of samples to ensure that the distance between two empirical measures is comparable to the distance between the original densities. Therefore, optimal transpo…

New conditions ensure Dantzig-Wolfe relaxation matches rank-constrained optimization problems.

Consider an unknown smooth function $f: [0,1] \rightarrow \mathbb{R}$, and say we are given $n$ noisy$\mod 1$ samples of $f$, i.e., $y_i = (f(x_i) + η_i)\mod 1$ for $x_i \in [0,1]$, where $η_i$ denotes noise. Given the samples $(x_i,y_i)_{i=1}^{n}$ our goal is to recover smooth, robust estimates of the clean samples $f…

This work uses a SI-DNN to predict AC-OPF solutions efficiently.

Consider an unknown smooth function $f: [0,1]^d \rightarrow \mathbb{R}$, and say we are given $n$ noisy mod 1 samples of $f$, i.e., $y_i = (f(x_i) + η_i)\mod 1$, for $x_i \in [0,1]^d$, where $η_i$ denotes the noise. Given the samples $(x_i,y_i)_{i=1}^{n}$, our goal is to recover smooth, robust estimates of the clean sa…