Weaknesses: In my opinion, comparing Nystrom for kernel ridge regression to variational GPs is apples to oranges in a lot of ways that are frankly unfair to variational GPs. In my view, a much more appropriate comparison would be a KeOps based implementation of SGPR or FITC with fixed inducing points. Variational GPs introduce a very large number of parameters in the form of the variational distribution and inducing point locations that require optimization and significantly increase the total amount of time spent in optimization. Methods that train GPs through the marginal likelihood with fixed inducing locations (e.g., as in Nystrom) may have as few as 3 parameters to fit. By contrast, SVGP learns (1) a variational distribution q(u) including a variational covariance matrix, and (2) the inducing point locations.

Tuomas Sandholm, a computer scientist at Carnegie Mellon University, is not a poker player--or much of a poker fan, in fact--but he is fascinated by the game for much the same reason as the great game theorist John von Neumann before him. Von Neumann, who died in 1957, viewed poker as the perfect model for human decision making, for finding the balance between skill and chance that accompanies our every choice. He saw poker as the ultimate strategic challenge, combining as it does not just the mathematical elements of a game like chess but the uniquely human, psychological angles that are more difficult to model precisely--a view shared years later by Sandholm in his research with artificial intelligence. "Poker is the main benchmark and challenge program for games of imperfect information," Sandholm told me on a warm spring afternoon in 2018, when we met in his offices in Pittsburgh. The game, it turns out, has become the gold standard for developing artificial intelligence.

Tuomas Sandholm, a computer scientist at Carnegie Mellon University, is not a poker player -- or much of a poker fan, in fact -- but he is fascinated by the game for much the same reason as the great game theorist John von Neumann before him. Von Neumann, who died in 1957, viewed poker as the perfect model for human decision making, for finding the balance between skill and chance that accompanies our every choice. He saw poker as the ultimate strategic challenge, combining as it does not just the mathematical elements of a game like chess but the uniquely human, psychological angles that are more difficult to model precisely -- a view shared years later by Sandholm in his research with artificial intelligence. WHAT I LEFT OUT is a recurring feature in which book authors are invited to share anecdotes and narratives that, for whatever reason, did not make it into their final manuscripts. In this installment, Maria Konnikova shares a story that was left out of "The Biggest Bluff: How I Learned to Pay Attention, Master Myself, and Win" (Penguin Press). "Poker is the main benchmark and challenge program for games of imperfect information," Sandholm told me on a warm spring afternoon in 2018, when we met in his offices in Pittsburgh.

The Pittsburgh Supercomputing Center received five @HPCwire awards, including one for poker AI'Libratus' The Pittsburgh Supercomputing Center (PSC) received not one, but five HPCwire awards at the 2017 International Conference for High-Performance Computing (HPC), Networking, Storage and Analysis (SC17) on Sunday, Nov. 12. One of the three Readers' Choice Awards that PSC received was for Best Use of AI: CMU School of Computer Science "Libratus" AI on PSC's "Bridges" wins Brains vs. AI competition. HPCwire represents the leading trade publication in the supercomputing community and their annual Readers' and Editors' Choice Awards, given out at the start of the annual supercomputing conference, are well respected in that community. The awards are determined based on a nomination and voting process among the HPCwire community as well as selections from the publication's editors. In addition to Best Use of AI, PSC received two more Readers' Choice Awards -- Outstanding Leadership in HPC (Nick Nystrom, Interim Director, PSC) and Best Use of HPC in Energy (PSC with Texas A&M uses OpenFOAM on PSC Bridges & Texas Advanced Computing Center's Stampede to better understand coolant & heat transfer in high-temperature-jet reactors).

Determinantal point process (DPP) is an important probabilistic model that has extensive applications in artificial intelligence. The exact sampling algorithm of DPP requires the full eigenvalue decomposition of the kernel matrix which has high time and space complexities. This prohibits the applications of DPP from large-scale datasets. Previous work has applied the Nystrom method to speedup the sampling algorithm of DPP, and error bounds have been established for the approximation. In this paper we employ the matrix ridge approximation (MRA) to speedup the sampling algorithm of DPP, showing that our approach MRA-DPP has stronger error bound than the Nystrom-DPP. In certain circumstances our MRA-DPP is provably exact, whereas the Nystrom-DPP is far from the ground truth. Finally, experiments on several real-world datasets show that our MRA-DPP is more accurate than the other approximation approaches.