Discovering additive structure is an important step towards understanding a complex multi-dimensional function because it allows the function to be expressed as the sum of lower-d...
We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...
Unlike the top-down photolithographic CMOS VLSI process, cost-effective bulk fabrication of nanodevices calls for a bottom-up approach, generally called self-assembly. Selfassembl...
James Dardig, Haralampos-G. D. Stratigopoulos, Eri...
We propose statistical predicate invention as a key problem for statistical relational learning. SPI is the problem of discovering new concepts, properties and relations in struct...
Temporal causal modeling has been a highly active research area in the last few decades. Temporal or time series data arises in a wide array of application domains ranging from med...