Weak consistency of the 1-nearest neighbor measure with applications to missing data

When data is partially missing at random, imputation and importance weighting are often used to estimate moments of the unobserved population. In this paper, we study 1-nearest neighbor (1NN) importance weighting, which estimates moments by replacing missing data with the complete data that is the nearest neighbor in the non-missing covariate space. We define an empirical measure, the 1NN measure, and show that it is weakly consistent for the measure of the missing data. The main idea behind this result is that the 1NN measure is performing inverse probability weighting in the limit. We study applications to missing data and mitigating the impact of covariate shift in prediction tasks

Ensemble Committees for Stock Return Classification and Prediction

This paper considers a portfolio trading strategy formulated by algorithms in the field of machine learning. The profitability of the strategy is measured by the algorithm's capability to consistently and accurately identify stock indices with positive or negative returns, and to generate a preferred portfolio allocation on the basis of a learned model. Stocks are characterized by time series data sets consisting of technical variables that reflect market conditions in a previous time interval, which are utilized produce binary classification decisions in subsequent intervals. The learned model is constructed as a committee of random forest classifiers, a non-linear support vector machine classifier, a relevance vector machine classifier, and a constituent ensemble of k-nearest neighbors classifiers. The Global Industry Classification Standard (GICS) is used to explore the ensemble model's efficacy within the context of various fields of investment including Energy, Materials, Financials, and Information Technology. Data from 2006 to 2012, inclusive, are considered, which are chosen for providing a range of market circumstances for evaluating the model. The model is observed to achieve an accuracy of approximately 70% when predicting stock price returns three months in advance.Comment: 15 pages, 4 figures, Neukom Institute Computational Undergraduate Research prize - second plac

Neural Nearest Neighbors Networks

Non-local methods exploiting the self-similarity of natural signals have been well studied, for example in image analysis and restoration. Existing approaches, however, rely on k-nearest neighbors (KNN) matching in a fixed feature space. The main hurdle in optimizing this feature space w.r.t. application performance is the non-differentiability of the KNN selection rule. To overcome this, we propose a continuous deterministic relaxation of KNN selection that maintains differentiability w.r.t. pairwise distances, but retains the original KNN as the limit of a temperature parameter approaching zero. To exploit our relaxation, we propose the neural nearest neighbors block (N3 block), a novel non-local processing layer that leverages the principle of self-similarity and can be used as building block in modern neural network architectures. We show its effectiveness for the set reasoning task of correspondence classification as well as for image restoration, including image denoising and single image super-resolution, where we outperform strong convolutional neural network (CNN) baselines and recent non-local models that rely on KNN selection in hand-chosen features spaces.Comment: to appear at NIPS*2018, code available at https://github.com/visinf/n3net

A memory-based classification approach to marker-based EBMT

We describe a novel approach to example-based machine translation that makes use of marker-based chunks, in which the decoder is a memory-based classifier. The classifier is trained to map trigrams of source-language chunks onto trigrams of target-language chunks; then, in a second decoding step, the predicted trigrams are rearranged according to their overlap. We present the first results of this method on a Dutch-to-English translation system using Europarl data. Sparseness of the class space causes the results to lag behind a baseline phrase-based SMT system. In a further comparison, we also apply the method to a word-aligned version of the same data, and report a smaller difference with a word-based SMT system. We explore the scaling abilities of the memory-based approach, and observe linear scaling behavior in training and classification speed and memory costs, and loglinear BLEU improvements in the amount of training examples