On the Stability of Structured Prediction

Many important applications of artificial intelligence---such as image segmentation, part-of-speech tagging and network classification---are framed as multiple, interdependent prediction tasks. These structured prediction problems are typically modeled using some form of joint inference over the outputs, to exploit the relational dependencies. Joint reasoning can significantly improve predictive accuracy, but it introduces a complication in the analysis of structured models: the stability of inference. In optimizations involving multiple interdependent variables, such as joint inference, a small change to the input or parameters could induce drastic changes in the solution. In this dissertation, I investigate the impact of stability in structured prediction. I explore two topics, connected by the stability of inference. First, I provide generalization bounds for learning from a limited number of examples with large internal structure. The effective learning rate can be significantly sharper than rates given in related work. Under certain conditions on the data distribution and stability of the predictor, the bounds decrease with both the number of examples and the size of each example, meaning one could potentially learn from a single giant example. Secondly, I investigate the benefits of learning with strongly convex variational inference. Using the duality between strong convexity and stability, I demonstrate, both theoretically and empirically, that learning with a strongly convex free energy can result in significantly more accurate marginal probabilities. One consequence of this work is a new technique that ``strongly convexifies" many free energies used in practice. These two seemingly unrelated threads are tied by the idea that stable inference leads to lower error, particularly in the limited example setting, thereby demonstrating that inference stability is of critical importance to the study and practice of structured prediction

Open data from the first and second observing runs of Advanced LIGO and Advanced Virgo

Advanced LIGO and Advanced Virgo are monitoring the sky and collecting gravitational-wave strain data with sufficient sensitivity to detect signals routinely. In this paper we describe the data recorded by these instruments during their first and second observing runs. The main data products are gravitational-wave strain time series sampled at 16384 Hz. The datasets that include this strain measurement can be freely accessed through the Gravitational Wave Open Science Center at http://gw-openscience.org, together with data-quality information essential for the analysis of LIGO and Virgo data, documentation, tutorials, and supporting software

Search for intermediate-mass black hole binaries in the third observing run of Advanced LIGO and Advanced Virgo

International audienceIntermediate-mass black holes (IMBHs) span the approximate mass range 100−105 M⊙, between black holes (BHs) that formed by stellar collapse and the supermassive BHs at the centers of galaxies. Mergers of IMBH binaries are the most energetic gravitational-wave sources accessible by the terrestrial detector network. Searches of the first two observing runs of Advanced LIGO and Advanced Virgo did not yield any significant IMBH binary signals. In the third observing run (O3), the increased network sensitivity enabled the detection of GW190521, a signal consistent with a binary merger of mass ∼150 M⊙ providing direct evidence of IMBH formation. Here, we report on a dedicated search of O3 data for further IMBH binary mergers, combining both modeled (matched filter) and model-independent search methods. We find some marginal candidates, but none are sufficiently significant to indicate detection of further IMBH mergers. We quantify the sensitivity of the individual search methods and of the combined search using a suite of IMBH binary signals obtained via numerical relativity, including the effects of spins misaligned with the binary orbital axis, and present the resulting upper limits on astrophysical merger rates. Our most stringent limit is for equal mass and aligned spin BH binary of total mass 200 M⊙ and effective aligned spin 0.8 at 0.056 Gpc−3 yr−1 (90% confidence), a factor of 3.5 more constraining than previous LIGO-Virgo limits. We also update the estimated rate of mergers similar to GW190521 to 0.08 Gpc−3 yr−1.Key words: gravitational waves / stars: black holes / black hole physicsCorresponding author: W. Del Pozzo, e-mail: [email protected]† Deceased, August 2020