Artem Lenskiy

Xintao Xiang, Artem Lenskiy

Many existing methods of counterfactual explanations ignore the intrinsic relationships between data attributes and thus fail to generate realistic counterfactuals. Moreover, the existing models that account for relationships require domain knowledge, which limits their applicability in complex real-world applications. In this paper, we propose a novel approach to realistic counterfactual explanations that preserve the relationships and minimise experts' interventions. The model directly learns the relationships by a variational auto-encoder with minimal domain knowledge and then learns to perturb the latent space accordingly. We conduct extensive experiments on both synthetic and real-world datasets. The experimental results demonstrate that the proposed model learns relationships from the data and preserves these relationships in generated counterfactuals. In particular, it outperforms other methods in terms of Mahalanobis distance, and the constraint feasibility score.

Haotian Weng, Artem Lenskiy

Algorithmic trading has proliferated the area of quantitative finance for already over a decade. The decisions are made without human intervention using the data provided by brokerage firms and exchanges. There is an emerging intermediate layer of financial players that are placed in between a broker and algorithmic traders. The role of these players is to aggregate market decisions from the algorithmic traders and send a final market order to a broker. In return, the quantitative analysts receive incentives proportional to the correctness of their predictions. In such a setup, the intermediate player - an aggregator - does not provide the market data in plaintext but encrypts it. Encrypting market data prevents quantitative analysts from trading on their own, as well as keeps valuable financial data private. This paper proposes an implementation of a popular trend-following indicator with two different homomorphic encryption libraries - SEAL and HEAAN - and compares it to the trading indicator implemented for plaintext. Then an attempt to implement a trading strategy is presented and analysed. The trading indicator implemented with SEAL and HEAAN is almost identical to that implemented on the plaintext, the percentage error is of 0.14916% and 0.00020% respectively. Despite many limitations that homomorphic encryption imposes on this algorithm's implementation, quantitative finance has a high potential of benefiting from the methods of homomorphic encryption.

Artem Lenskiy

Computer vision has become a major source of information for autonomous navigation of robots of various types, self-driving cars, military robots and mars/lunar rovers are some examples. Nevertheless, the majority of methods focus on analysing images captured in visible spectrum. In this manuscript we elaborate on the problem of segmenting cross-country scenes captured in IR spectrum. For this purpose we proposed employing salient features. Salient features are robust to variations in scale, brightness and view angle. We suggest the Speeded-Up Robust Features as a basis for our salient features for a number of reasons discussed in the paper. We also provide a comparison of two SURF implementations. The SURF features are extracted from images of different terrain types. For every feature we estimate a terrain class membership function. The membership values are obtained by means of either the multi-layer perceptron or nearest neighbours. The features' class membership values and their spatial positions are then applied to estimate class membership values for all pixels in the image. To decrease the effect of segmentation blinking that is caused by rapid switching between different terrain types and to speed up segmentation, we are tracking camera position and predict features' positions. The comparison of the multi-layer perception and the nearest neighbour classifiers is presented in the paper. The error rate of the terrain segmentation using the nearest neighbours obtained on the testing set is 16.6+-9.17%.

Eric Makita, Artem Lenskiy

Movie ratings play an important role both in determining the likelihood of a potential viewer to watch the movie and in reflecting the current viewer satisfaction with the movie. They are available in several sources like the television guide, best-selling reference books, newspaper columns, and television programs. Furthermore, movie ratings are crucial for recommendation engines that track the behavior of all users and utilize the information to suggest items they might like. Movie ratings in most cases, thus, provide information that might be more important than movie feature-based data. It is intuitively appealing that information about the viewing preferences in movie genres is sufficient for predicting a genre of an unlabeled movie. In order to predict movie genres, we treat ratings as a feature vector, apply the Bernoulli event model to estimate the likelihood of a movies given genre, and evaluate the posterior probability of the genre of a given movie using the Bayes rule. The goal of the proposed technique is to efficiently use the movie ratings for the task of predicting movie genres. In our approach we attempted to answer the question: "Given the set of users who watched a movie, is it possible to predict the genre of a movie based on its ratings?" Our simulation results with MovieLens 100k data demonstrated the efficiency and accuracy of our proposed technique, achieving 59% prediction rate for exact prediction and 69% when including correlated genres.

Eric Makita, Artem Lenskiy

This paper proposes a movie genre-prediction based on multinomial probability model. To the best of our knowledge, this problem has not been addressed yet in the field of recommender system. The prediction of a movie genre has many practical applications including complementing the items categories given by experts and providing a surprise effect in the recommendations given to a user. We employ mulitnomial event model to estimate a likelihood of a movie given genre and the Bayes rule to evaluate the posterior probability of a genre given a movie. Experiments with the MovieLens dataset validate our approach. We achieved 70% prediction rate using only 15% of the whole set for training.