-
A constrained minimum criterion for model selection
We propose a hypothesis test based model selection criterion for the best subset selection of sparse linear models. We show it is consistent in that the probability of its choosing the true model approaches one and the parameter ... Read More >
-
ISLET: Fast and Optimal Low-rank Tensor Regression via Importance Sketching
In this paper, we develop a novel procedure for low-rank tensor regression, namely \emph{\underline{I}mportance \underline{S}ketching \underline{L}ow-rank \underline{E}stimation for \underline{T}ensors} (ISLET). The central idea ... Read More >
-
Impact of internal migration on population redistribution in Europe: Urbanisatio ...
The classical foundations of migration research date from the 1880s with Ravenstein's Laws of migration, which represent the first comparative analyses of internal migration. While his observations remain largely valid, the ensu ... Read More >
-
Hypothesis testing for populations of networks
It has become an increasingly common practice for scientists in modern science and engineering to collect samples of multiple network data in which a network serves as a basic data object. The increasing prevalence of multiple n ... Read More >
-
The Bias-Expressivity Trade-off
Learning algorithms need bias to generalize and perform better than random guessing. We examine the flexibility (expressivity) of biased algorithms. An expressive algorithm can adapt to changing training data, altering its outco ... Read More >
-
Bayesian Active Learning for Structured Output Design
In this paper, we propose an active learning method for an inverse problem that aims to find an input that achieves a desired structured-output. The proposed method provides new acquisition functions for minimizing the error bet ... Read More >
-
Influence of single observations on the choice of the penalty parameter in ridge ...
Penalized regression methods such as ridge regression heavily rely on the choice of a tuning or penalty parameter, which is often computed via cross-validation. Discrepancies in the value of the penalty parameter may lead to sub ... Read More >
-
Optimal Shape Control via $L_\infty$ Loss for Composite Fuselage Assembly
Shape control is critical to ensure the quality of composite fuselage assembly. In current practice, the structures are adjusted to the design shape in terms of the $\ell_2$ loss for further assembly without considering the exis ... Read More >
-
Degrees of freedom for off-the-grid sparse estimation
A central question in modern machine learning and imaging sciences is to quantify the number of effective parameters of vastly over-parameterized models. The degrees of freedom is a mathematically convenient way to define this n ... Read More >
-
Dealing With Ratio Metrics in A/B Testing at the Presence of Intra-User Correlat ...
We study ratio metrics in A/B testing at the presence of correlation among observations coming from the same user and provides practical guidance especially when two metrics contradict each other. We propose new estimating metho ... Read More >