In this paper we consider the problem of finding stable maxima of expensive (to evaluate) functions. We are motivated by the optimisation of physical and industrial processes where, for some input ranges, small and unavoidable variations in inputs lead to unacceptably large variation in outputs. Our approach uses multiple gradient Gaussian Process models to estimate the probability that worst-case output variation for specified input perturbation exceeded the desired maxima, and these probabilities are then used to (a) guide the optimisation process toward solutions satisfying our stability criteria and (b) post-filter results to find the best stable solution. We exhibit our algorithm on synthetic and real-world problems and demonstrate that it is able to effectively find stable maxima.

We revisit the proof by Qin et al. (2014) of bounded regret of the C$^2$UCB contextual combinatorial bandit. We demonstrate an error in the proof of volumetric expansion of the moment matrix, used in upper bounding a function of context vector norms. We prove a relaxed inequality that yields the originally-stated regret bound.

Practically, we are often in the dilemma that the labeled data at hand are inadequate to train a reliable classifier, and more seriously, some of these labeled data may be mistakenly labeled due to the various human factors. Therefore, this paper proposes a novel semi-supervised learning paradigm that can handle both label insufficiency and label inaccuracy. To address label insufficiency, we use a graph to bridge the data points so that the label information can be propagated from the scarce labeled examples to unlabeled examples along the graph edges. To address label inaccuracy, Graph Trend Filtering (GTF) and Smooth Eigenbase Pursuit (SEP) are adopted to filter out the initial noisy labels. GTF penalizes the l_0 norm of label difference between connected examples in the graph and exhibits better local adaptivity than the traditional l_2 norm-based Laplacian smoother. SEP reconstructs the correct labels by emphasizing the leading eigenvectors of Laplacian matrix associated with small eigenvalues, as these eigenvectors reflect real label smoothness and carry rich class separation cues. We term our algorithm as `Semi-supervised learning under Inadequate and Incorrect Supervision' (SIIS). Thorough experimental results on image classification, text categorization, and speech recognition demonstrate that our SIIS is effective in label error correction, leading to superior performance to the state-of-the-art methods in the presence of label noise and label scarcity.

Deep neural networks are usually trained with stochastic gradient descent (SGD), which optimizes $\theta\in\mathbb{R}^D$ parameters to minimize objective function using very rough approximations of gradient, only averaging to the real gradient. Standard approaches like momentum or ADAM only consider single direction, and do not try to model distance from extremum - neglecting valuable information from calculated gradients. It can be improved by second order methods, but they are costly, need inverse of Hessian - problematic especially in the stochastic setting. Proposed general framework should overcome these difficulties by directly evolving local second order parametrization in $d\ll D$ directions: as $\sum_{i=1}^d \lambda_{i}(\theta\cdot v_{i}-p_{i})^2$ modelling local information we are interested in, and relatively simple to update for better agreement with calculated gradients. It allows for $\theta$ update by simultaneously attracting toward modelled directional minima $(\lambda_i>0)$, and repulsing from maxima $(\lambda_i<0)$, correspondingly to distances from $p_i$ (and uncertainty), what allows to also handle problematic saddles. Calculated gradients can be used to slowly evolve this parametrization to improve agreement with local behavior of objective function, accumulating their statistical trends: 1) update $\lambda, p$ parameters for more accurate description of parabola in corresponding directions (also uncertainty), 2) rotate considered subspace toward recently statistically significant directions (replacing the less frequent ones), and 3) rotate $(v_i)$ inside the subspace to improve diagonal form of Hessian in this basis. Presented general framework leaves many customization options for optimizations to specific tasks.

Last week, Kenneth Hayne QC handed down his royal commission final report that recommended banning banks from paying trail commissions to mortgage brokers from mid-next year. Instead, the borrower will likely be required to pay an upfront fee for the service. UBank, a subsidiary of NAB, doesn't pay mortgage brokers, but its new robot-like home loan aid gives a glimpse into how the service could be provided in the future. Many commentators are speculating only the wealthy will be able to afford a broker, while regular Aussies will have to rely on an automated service. The artificial loan aid, named Mia (My Interactive Assistant) and powered by AI start-up FaceMe, will speak directly to customers through a desktop or smartphone advising on questions such as what's a variable rate to what classifies as an expense, the bank says.

SABRA LANE: Much of the traditional car making industry around the world could go bust during the next decade, as the cost of electric cars plunge and become affordable for average motorists. That's the prediction of a top researcher and investor who thinks a world of self-driving electric cars, including autonomous taxis, is only a few years away. Brett Winton, the director of research at the US based Ark Invest, believes a disruptive day of reckoning is coming for complacent car making giants, who've failed to adapt their manufacturing models to confront new challengers like Tesla. Mr Winton is visiting Australia and he spoke about a not too distant "new world" of artificial intelligence with Senior Business Correspondent, Peter Ryan. You can look at some of the advances in artificial intelligence and see that computers are going to have the capability to solve the game of driving the car, and likely a lot safer than humans.

There's been some interest in the Explorer Challenge which is a 1 million dollar competition combining machine learning and geology to come up with the best prospect. A funny video and detailed slides have been released. I was also at the presentation in Perth, Australia so feel free to clarify things with me if the slides aren't clear. This is an interesting competition because geology seems like it could be disrupted by the application of ML, but it's also challenging because of the large amount of contextual and qualitative data that goes into making decisions.

In 1950, Norbert Wiener's The Human Use of Human Beings was at the cutting edge of vision and speculation in proclaiming: But this was his book's denouement, and it has left us hanging now for 68 years, lacking not only prescriptions and proscriptions but even a well-articulated "problem statement." We have since seen similar warnings about the threat of our machines, even in the form of outreach to the masses, via films like Colossus: The Forbin Project (1970), The Terminator (1984), The Matrix (1999), and Ex Machina (2015). But now the time is ripe for a major update with fresh, new perspectives -- notably focused on generalizations of our "human" rights and our existential needs. Concern has tended to focus on "us versus them" (robots) or "gray goo" (nanotech) or "monocultures of clones" (bio). To extrapolate current trends: What if we could make or grow almost anything and engineer any level of safety and efficacy desired?

The vast amount of information encoded in an individual's DNA tells great tales of one's health and disease conditions. When the first human genome was sequenced, the project that began in 1990 took over 10 years and cost around $2.7 billion. According to Andrew Underwood, CTO, HPC & Artificial Intelligence, Dell EMC, Australia and New Zealand, data intensive computing is fast becoming a dominant approach. Especially in R&D, it is a rapidly growing field of research built on data that is generated from scientific instruments, people, machines and IoT devices. Data comes in high velocities and in large volumes – requiring scientists to harness the power of high performance computing to analyze data faster for timely insights in their field of research.

Dynamic paired comparison models, such as Elo and Glicko, are frequently used for sports prediction and ranking players or teams. We present an alternative dynamic paired comparison model which uses a Gaussian Process (GP) as a prior for the time dynamics rather than the Markovian dynamics usually assumed. In addition, we show that the GP model can easily incorporate covariates. We derive an efficient approximate Bayesian inference procedure based on the Laplace Approximation and sparse linear algebra. We select hyperparameters by maximising their marginal likelihood using Bayesian Optimisation, comparing the results against random search. Finally, we fit and evaluate the model on the 2018 season of ATP tennis matches, where it performs competitively, outperforming Elo and Glicko on log loss, particularly when surface covariates are included.