Generating adversarial examples is a critical step for evaluating and improving the robustness of learning machines. So far, most existing methods only work for classification and are not designed to alter the true performance measure of the problem at hand. We introduce a novel flexible approach named Houdini for generating adversarial examples specifically tailored for the final performance measure of the task considered, be it combinatorial and non-decomposable. We successfully apply Houdini to a range of applications such as speech recognition, pose estimation and semantic segmentation. In all cases, the attacks based on Houdini achieve higher success rate than those based on the traditional surrogates used to train the models while using a less perceptible adversarial perturbation.

Structured Prediction Energy Networks (SPENs) are a simple, yet expressive family of structured prediction models (Belanger and McCallum, 2016). An energy function over candidate structured outputs is given by a deep network, and predictions are formed by gradient-based optimization. This paper presents end-to-end learning for SPENs, where the energy function is discriminatively trained by back-propagating through gradient-based prediction. In our experience, the approach is substantially more accurate than the structured SVM method of Belanger and McCallum (2016), as it allows us to use more sophisticated non-convex energies. We provide a collection of techniques for improving the speed, accuracy, and memory requirements of end-to-end SPENs, and demonstrate the power of our method on 7-Scenes image denoising and CoNLL-2005 semantic role labeling tasks. In both, inexact minimization of non-convex SPEN energies is superior to baseline methods that use simplistic energy functions that can be minimized exactly.

Swansea's Gylfi Sigurdsson has been valued at £50m, Everton have spent £90m and Manchester United bought Romelu Lukaku for £75m - so is Newcastle boss Rafael Benitez right to call this summer's transfer window "a little bit crazy"? Premier League clubs' spending has already surpassed £500m since the end of last season - and business analysts Deloitte say they are on course to set another new record by 31 August. Teams spent a record £1.165bn last summer, rising to £1.38bn after the January window. Football finance expert Rob Wilson says the market "hyper-inflation" means anyone selling to an English club is adding "at least 40%, if not 50%, to the deal". And football agent Jon Smith says a £30m transfer - such as goalkeeper Jordan Pickford's move from Sunderland to Everton - is "the new norm".

Charlie Hill, the managing director of Major League Baseball for Europe, says it's possible that some official games will be played in London during the 2019 season. He said the Independence Day exhibition is an attempt to "lay down roots" in Britain.

Many supervised learning tasks are emerged in dual forms, e.g., English-to-French translation vs. French-to-English translation, speech recognition vs. text to speech, and image classification vs. image generation. Two dual tasks have intrinsic connections with each other due to the probabilistic correlation between their models. This connection is, however, not effectively utilized today, since people usually train the models of two dual tasks separately and independently. In this work, we propose training the models of two dual tasks simultaneously, and explicitly exploiting the probabilistic correlation between them to regularize the training process. For ease of reference, we call the proposed approach \emph{dual supervised learning}. We demonstrate that dual supervised learning can improve the practical performances of both tasks, for various applications including machine translation, image processing, and sentiment analysis.

One of the things we haven't talked about much is the concept of human intervention when it comes to the dangers of artificial intelligence, especially in its early stages, where we are now with the technology. This may be far less of a "doomsday" or apocalyptic scenario, but the consequences could still be quite devastating on a person by person basis, or even affect larger groups depending on where a machine learning algorithm is deployed. THE SCENARIO We want to look at very specific cases where reinforcement learning is deployed with public access, much like how you can tell Google Translate that a translation is incorrect, and submit your improvements to them. Another good example would be marking an email that is not in your spam folder, but definitely belongs there, as such so the machine learning algorithm will become better over time. THE EXPLOIT Exploiting these technologies can be done in a variety of ways, and while I initially thought it would take a large group to skew the learning of a machine by flooding it with many badly labeled training examples, it would not be impossible to have this done by some kind of botnet. See, most machine learning algorithms learn by training them on a so-called "labeled" data set, which is a large set of input data, and a label which is the desired perfect output of that input data.

Several `edge-discovery' applications over graph-based data models are known to have worst-case quadratic time complexity in the nodes, even if the discovered edges are sparse. One example is the generic link discovery problem between two graphs, which has invited research interest in several communities. Specific versions of this problem include link prediction in social networks, ontology alignment between metadata-rich RDF data, approximate joins, and entity resolution between instance-rich data. As large datasets continue to proliferate, reducing quadratic complexity to make the task practical is an important research problem. Within the entity resolution community, the problem is commonly referred to as blocking. A particular class of learnable blocking schemes is known as Disjunctive Normal Form (DNF) blocking schemes, and has emerged as state-of-the art for homogeneous (i.e. same-schema) tabular data. Despite the promise of these schemes, a formalism or learning framework has not been developed for them when input data instances are generic, attributed graphs possessing both node and edge heterogeneity. With such a development, the complexity-reducing scope of DNF schemes becomes applicable to a variety of problems, including entity resolution and type alignment between heterogeneous graphs, and link prediction in networks represented as attributed graphs. This paper presents a graph-theoretic formalism for DNF schemes, and investigates their learnability in an optimization framework. We also briefly describe an empirical case study encapsulating some of the principles in this paper.

Diego leads the design and implementation of supervised learning systems at Hello Digit. He specializes in building scalable perpetual-learning pipelines leveraging human-in-the-loop techniques. Randi is a storyteller, mom, knitting enthusiast, Kellogg and Dartmouth grad, and VP of Marketing at CrowdFlower, the human-in-the-loop platform for data science and machine learning teams making AI work.

The ability to perform effective off-policy learning would revolutionize the process of building better interactive systems, such as search engines and recommendation systems for e-commerce, computational advertising and news. Recent approaches for off-policy evaluation and learning in these settings appear promising. With this paper, we provide real-world data and a standardized test-bed to systematically investigate these algorithms using data from display advertising. In particular, we consider the problem of filling a banner ad with an aggregate of multiple products the user may want to purchase. This paper presents our test-bed, the sanity checks we ran to ensure its validity, and shows results comparing state-of-the-art off-policy learning methods like doubly robust optimization, POEM, and reductions to supervised learning using regression baselines. Our results show experimental evidence that recent off-policy learning methods can improve upon state-of-the-art supervised learning techniques on a large-scale real-world data set.

In summer 2007, the Los Angeles County median home price hit an all-time high of $550,000. It soon plunged as the housing bubble burst and the national economy crashed. Now the median, the point where half the homes sold for more and half for less, has finally passed the heights of 10 years ago -- the result of an improving economy, historically low mortgage rates and a shortage of listings. According to a report released Wednesday from real estate firm CoreLogic, the county's median price in May rose 6.8% from a year earlier to reach $560,500 as sales jumped 4.8%. When adjusted for inflation, May's median remains 11% below the 2007 high, though the nominal record comes amid fresh concerns over the high cost of housing in California.