We propose a pool-based non-parametric active learning algorithm for general metric spaces, called MArgin Regularized Metric Active Nearest Neighbor (MARMANN), which outputs a nearest-neighbor classifier. We give prediction error guarantees that depend on the noisy-margin properties of the input sample, and are competitive with those obtained by previously proposed passive learners. We prove that the label complexity of MARMANN is significantly lower than that of any passive learner with similar error guarantees. Our algorithm is based on a generalized sample compression scheme and a new label-efficient active model-selection procedure.

Estimating optimal phylogenetic trees or hierarchical clustering trees from metric data is an important problem in evolutionary biology and data analysis. Intuitively, the goodness-of-fit of a metric space to a tree depends on its inherent treeness, as well as other metric properties such as intrinsic dimension. Existing algorithms for embedding metric spaces into tree metrics provide distortion bounds depending on cardinality. Because cardinality is a simple property of any set, we argue that such bounds do not fully capture the rich structure endowed by the metric. We consider an embedding of a metric space into a tree proposed by Gromov. By proving a stability result, we obtain an improved additive distortion bound depending only on the hyperbolicity and doubling dimension of the metric. We observe that Gromov's method is dual to the well-known single linkage hierarchical clustering (SLHC) method. By means of this duality, we are able to transport our results to the setting of SLHC, where such additive distortion bounds were previously unknown.

Stochastic structured prediction under bandit feedback follows a learning protocol where on each of a sequence of iterations, the learner receives an input, predicts an output structure, and receives partial feedback in form of a task loss evaluation of the predicted structure. We present applications of this learning scenario to convex and non-convex objectives for structured prediction and analyze them as stochastic first-order methods. We present an experimental evaluation on problems of natural language processing over exponential output spaces, and compare convergence speed across different objectives under the practical criterion of optimal task performance on development data and the optimization-theoretic criterion of minimal squared gradient norm. Best results under both criteria are obtained for a non-convex objective for pairwise preference learning under bandit feedback.

We propose and analyze a regularization approach for structured prediction problems. We characterize a large class of loss functions that allows to naturally embed structured outputs in a linear space. We exploit this fact to design learning algorithms using a surrogate loss approach and regularization techniques. We prove universal consistency and finite sample bounds characterizing the generalization properties of the proposed method. Experimental results are provided to demonstrate the practical usefulness of the proposed approach.

New record set for the world's fastest BED with motorised mattress clocking 84mph on a race track Engineers were commissioned by a hotel booking site to build a motorised bed British racing diver Tom Onslow-Cole, 29, took the piece of furniture for a spin He broke the Guinness World Record for the World's Fastest Bed at 83.8mph He broke the Guinness World Record for the World's Fastest Bed at 83.8mph British racing diver Tom Onslow-Cole, 29, took the piece of furniture for a spin and broke the Guinness World Record for the World's Fastest Bed, clocking 84mph The do's and don'ts of après-ski revealed (including why... Aviation expert reveals how to travel in luxury on a... The do's and don'ts of après-ski revealed (including why... Aviation expert reveals how to travel in luxury on a... Crossing the finish line: Adjudicators clocked it whooshing forwards at 83.8 mph A wheely great sleep: Onslow-Cole said his speedy snooze was an'unforgettable experience'. He added: 'I hope it'll stand the test of time – it'll take some beating!' Woman goes on racist rant while waiting in line at J.C. Penney Black blues musician explores racism by befriending the KKK A young thug is filmed fly kicking a lady in the back Dramatic moment man removed from flight for'speaking Arabic' GRAPHIC: Robber is left writhing on the pavement after shot out Syrian police injured after girl blows herself up inside station Male guests in a Chinese wedding flock to harass a bridesmaid Angela Rye shares video of her invasive ordeal with TSA agent Body cam footage shows moments before two Georgia cops are shot Boeing cargo plane overshoots runway before crashing in Colombia Shocking video shows a Texas mother hitting her daughter Adorable moment puppy excitedly unwraps Christmas present Woman goes on racist rant while waiting in line at J.C. Penney Dramatic moment man removed from flight for'speaking Arabic' Is resting your head on a BOX the best way to sleep on a... Shocking pictures reveal how some of the most picturesque... Choose the right seat, alter your watch and drink alcohol:... Fascinating images capture the... Should you be worried about flying in the snow? When photographers were asked to submit their best holiday... 'Is this a real picture?

We present a general theoretical analysis of structured prediction with a series of new results. We give new data-dependent margin guarantees for structured prediction for a very wide family of loss functions and a general family of hypotheses, with an arbitrary factor graph decomposition. These are the tightest margin bounds known for both standard multi-class and general structured prediction problems. Our guarantees are expressed in terms of a data-dependent complexity measure, factor graph complexity, which we show can be estimated from data and bounded in terms of familiar quantities. We further extend our theory by leveraging the principle of Voted Risk Minimization (VRM) and show that learning is possible even with complex factor graphs. We present new learning bounds for this advanced setting, which we use to design two new algorithms, Voted Conditional Random Field (VCRF) and Voted Structured Boosting (StructBoost). These algorithms can make use of complex features and factor graphs and yet benefit from favorable learning guarantees. We also report the results of experiments with VCRF on several datasets to validate our theory.

Hundreds of the nation's top female soccer players are expected to gather in Norco on Friday for the first day of a three-day college showcase. More than 140 registered teams from all over the western U.S. are scheduled to compete before more than 100 coaches from 16 conferences and more than three dozen states. Among the elite clubs who have confirmed their participation are Slammers FC, Legends FC, Eagles SC, Sereno Soccer Club of Arizona, LA Premier FC and Pateadores SC. The event kicks off at 8 a.m. For information, go to the tournament's website at silverlakestournaments.com.

Many machine learning problems, especially multi-modal learning problems, have two sets of distinct features (e.g., image and text features in news story classification, or neuroimaging data and neurocognitive data in cognitive science research). This paper addresses the joint dimensionality reduction of two feature vectors in supervised learning problems. In particular, we assume a discriminative model where low-dimensional linear embeddings of the two feature vectors are sufficient statistics for predicting a dependent variable. We show that a simple algorithm involving singular value decomposition can accurately estimate the embeddings provided that certain sample complexities are satisfied, without specifying the nonlinear link function (regressor or classifier). The main results establish sample complexities under multiple settings. Sample complexities for different link functions only differ by constant factors.

A key barrier to making phonetic studies scalable and replicable is the need to rely on subjective, manual annotation. To help meet this challenge, a machine learning algorithm was developed for automatic measurement of a widely used phonetic measure: vowel duration. Manually-annotated data were used to train a model that takes as input an arbitrary length segment of the acoustic signal containing a single vowel that is preceded and followed by consonants and outputs the duration of the vowel. The model is based on the structured prediction framework. The input signal and a hypothesized set of a vowel's onset and offset are mapped to an abstract vector space by a set of acoustic feature functions. The learning algorithm is trained in this space to minimize the difference in expectations between predicted and manually-measured vowel durations. The trained model can then automatically estimate vowel durations without phonetic or orthographic transcription. Results comparing the model to three sets of manually annotated data suggest it out-performed the current gold standard for duration measurement, an HMM-based forced aligner (which requires orthographic or phonetic transcription as an input).

A former solicitor general has said she is concerned the Ched Evans rape case could discourage victims of sexual offences from coming forward. The 27-year-old footballer was cleared on Friday of raping a 19-year-old woman in a hotel room. Vera Baird told the BBC that details of the woman's sexual past should not have been heard in court. Mr Evans was found guilty of rape in 2012, but that conviction was quashed in April. The Chesterfield striker was accused of attacking the woman at a Premier Inn in Rhuddlan, Denbighshire, on 30 May 2011.