Abstract: From clinical trials to corporate strategy, randomized experiments are a reliable methodological tool for estimating causal effects. In recent years, there has been a growing interest in causal inference under interference, where treatment given to one unit can affect outcomes of other units. While the literature on interference has focused primarily on unbiased and consistent estimation, designing randomized network experiments to insure tight rates of convergence is relatively under-explored. Not only are the optimal rates of estimation for different…

Abstract: Neural networks are often studied analytically through scaling limits: regimes in which taking to infinity structural network parameters such as depth, width, and number of training datapoints results in simplified models of learning. I will survey several such approaches with the goal of illustrating the rich and still not fully understood space of possible behaviors when some or all of the network’s structural parameters are large. Bio: Boris Hanin is an Assistant Professor at Princeton Operations Research and Financial…

Abstract:  In statistical inference problems, we are typically given a limited number of samples from some underlying distribution, and we wish to estimate some property of that distribution, under a given measure of risk. We are usually interested in characterizing and achieving the best possible risk as a function of the number of available samples. Thus, it is often implicitly assumed that samples are co-located, and that communication bandwidth as well as computational power are not a bottleneck, essentially making the number…