Abstract:  The most common machine learning algorithms operate on finite-dimensional vectorial feature representations. In many applications, however, the natural representation of the data consists of more complex objects, for example functions, distributions, and sets, rather than finite-dimensional vectors. In this …

Barnabas Poczos (CMU) Title: Distribution Regression and its Applications Read More »

Abstract: In this talk, I will present our joint measurement of cosmic infrared background (CIB) and CII line emission from large scales at redshift z=2.5 using an MCMC analysis of cross-correlations of the 3 high-frequency Planck bands with both SDSS-III …

Anthony Pullen (NYU) Title: Revealing CII Emission with LSS Cross-correlations Read More »

ABSTRACT: In this talk, I'd like to discuss the intertwining importance and connections of three principles of data science in the title in data-driven decisions. Making prediction as its central task and embracing computation as its core, machine learning has …

Bin Yu (University of California, Berkeley) "Three Principles of Data Science: Predictability, Stability, and Computability" Read More »

Abstract: Measurements of the universe's present-day expansion rate, or the Hubble constant (H0), that use a Cepheid variable star calibration of Type Ia supernovae (SNe Ia) are in >4σ disagreement with values predicted by the standard, Lambda cold dark matter …

Taylor Hoyt (University of Chicago) “Using Astrophysical Distance Indicators to Test Standard Cosmology” Read More »