Spectral clustering has become a popular technique due to its high performance in many contexts. It comprises three main steps: create a similarity graph between N objects to cluster, compute the first k eigenvectors of its Laplacian matrix to define a feature vector for each object, and run k-means on these features to separate objects into k classes. Each of these three steps becomes computationally intensive for large N and/or k. We propose to speed up the last two steps based on recent results in the emerging field of graph signal processing: graph filtering of random signals, and random sampling of bandlimited graph signals. We prove that our method, with a gain in computation time that can reach several orders of magnitude, is in fact an approximation of spectral clustering, for which we are able to control the error. We test the performance of our method on artificial and real-world network data.

We study the information-theoretic lower bound of the sample complexity of the correct recovery of diffusion network structures. We introduce a discrete-time diffusion model based on the Independent Cascade model for which we obtain a lower bound of order $\Omega(k \log p)$, for directed graphs of $p$ nodes, and at most $k$ parents per node. Next, we introduce a continuous-time diffusion model, for which a similar lower bound of order $\Omega(k \log p)$ is obtained. Our results show that the algorithm of Pouget-Abadie et al. is statistically optimal for the discrete-time regime. Our work also opens the question of whether it is possible to devise an optimal algorithm for the continuous-time regime.

The future predictive performance of a Bayesian model can be estimated using Bayesian cross-validation. In this article, we consider Gaussian latent variable models where the integration over the latent values is approximated using the Laplace method or expectation propagation (EP). We study the properties of several Bayesian leave-one-out (LOO) cross-validation approximations that in most cases can be computed with a small additional cost after forming the posterior approximation given the full data. Our main objective is to assess the accuracy of the approximative LOO cross-validation estimators. That is, for each method (Laplace and EP) we compare the approximate fast computation with the exact brute force LOO computation. Secondarily, we evaluate the accuracy of the Laplace and EP approximations themselves against a ground truth established through extensive Markov chain Monte Carlo simulation. Our empirical results show that the approach based upon a Gaussian approximation to the LOO marginal distribution (the so-called cavity distribution) gives the most accurate and reliable results among the fast methods.

Subspace recovery from corrupted and missing data is crucial for various applications in signal processing and information theory. To complete missing values and detect column corruptions, existing robust Matrix Completion (MC) methods mostly concentrate on recovering a low-rank matrix from few corrupted coefficients w.r.t. standard basis, which, however, does not apply to more general basis, e.g., Fourier basis. In this paper, we prove that the range space of an $m\times n$ matrix with rank $r$ can be exactly recovered from few coefficients w.r.t. general basis, though $r$ and the number of corrupted samples are both as high as $O(\min\{m,n\}/\log^3 (m+n))$. Our model covers previous ones as special cases, and robust MC can recover the intrinsic matrix with a higher rank. Moreover, we suggest a universal choice of the regularization parameter, which is $\lambda=1/\sqrt{\log n}$. By our $\ell_{2,1}$ filtering algorithm, which has theoretical guarantees, we can further reduce the computational cost of our model. As an application, we also find that the solutions to extended robust Low-Rank Representation and to our extended robust MC are mutually expressible, so both our theory and algorithm can be applied to the subspace clustering problem with missing values under certain conditions. Experiments verify our theories.

How would you perform accurate classification on a very large dataset, by just looking at a sample of it? One of his recent papers is about big data and similarity metrics. In this work Rocco proposes a deterministic method to obtain subsets from Big Data which are a good representative of the inherent structure in the data itself. This allows one to consider only a subset of the entire dataset, still performing at high accuracy if not better than traditional (eg. As you can see, there is always a solution in Big Data.

Volkswagen, acknowledging the growing automotive importance of machine learning, has acquired a stake in the German Research Center for Artificial Intelligence (DFKI). "Artificial intelligence (AI) is a key technology for autonomous driving and therefore an investment in our future," VW Group CEO Matthias Mueller said in a press statement. "We want to forge ahead with AI research in the automotive industry and beyond." VW also hopes its involvement in DFKI, one of the world's biggest AI research institutes, will help it with the digitalization of plants and a range of corporate processes. Many carmakers have been expanding their AI activities as an industry-wide effort to build autonomous vehicles is stretching current computer capacity to its limits.

Martha Kim is an Associate Professor of Computer Science at Columbia University. She holds a PhD in Computer Science and Engineering from the University of Washington and a bachelors in Computer Science from Harvard University. Martha's research interests are in computer architecture, parallel programming, compilers, and low-power computing. Her current research focuses on hardware and software techniques to improve the usability of hardware accelerators, data-centric accelerator design, and application-level power management. This work has been funded by the National Science Foundation, DARPA, the Center for Future Architectures Research (C-FAR), and Intel Corporation.

Washington: The world's first artificial intelligence lawyer has been employed by a law firm in the US, which will use the robot to assist its various teams in legal research. The robot called'ROSS' is built upon Watson, IBM's cognitive computer. With the support of Watson's cognitive computing and natural language processing capabilities, lawyers can ask ROSS their research question and the robot reads through the law, gathers evidence, draws inferences and returns highly relevant, evidence-based answers. ROSS also monitors the law around the clock to notify users of new court decisions that can affect a case. The programme continually learns from the lawyers who use it to bring back better results each time.

The University of Washington will be hosting the first of four White House Office of Science and Technology Policy workshops on artificial intelligence. The session in Seattle on Tuesday, involving the UW School of Law and the UW Tech Policy Lab, will focus on legal and policy issues around artificial intelligence. Speakers include UW professors, White House staff and the chief executive officer of the Allen Institute for Artificial Intelligence. Oren Etzioni, who is also a UW computer science and engineering professor, will provide an overview on the current state of artificial intelligence, followed by two panel discussions. The first will examine issues around making decisions in the private or public sector using artificial intelligence.

The killing of Afghan Taliban leader Mullah Mohammed Akhtar Mansour in a U.S. drone strike was greeted Sunday by Kabul's political leadership as a game-changer in efforts to end the long insurgent war plaguing Afghanistan. In a rare show of unity, President Ashraf Ghani and Chief Executive Abdullah Abdullah both welcomed the news of Mansour's death as the removal of a man who unleashed violence against innocent civilians in Afghanistan and was widely regarded as an obstacle to peace within the militant group. Mansour, believed to be in his 50s, was killed when a U.S. drone fired on his vehicle in the southwestern Pakistan province of Baluchistan, although there were conflicting accounts whether the airstrike occurred Friday or Saturday. He had emerged as the successor to Taliban founder Mullah Mohammad Omar, whose 2013 death was only revealed last summer. Mansour "engaged in deception, concealment of facts, drug-smuggling and terrorism while intimidating, maiming and killing innocent Afghans," Ghani said in a statement on his official Twitter account.