In genomic analysis, biomarker discovery, image recognition, and other systems involving machine learning, input variables can often be organized into different groups by their source or semantic category. Eliminating some groups of variables can expedite the process of data acquisition and avoid over-fitting. Researchers have used the group lasso to ensure group sparsity in linear models and have extended it to create compact neural networks in meta-learning. Different from previous studies, we use multi-layer non-linear neural networks to find sparse groups for input variables. We propose a new loss function to regularize parameters for grouped input variables, design a new optimization algorithm for this loss function, and test these methods in three real-world settings. We achieve group sparsity for three datasets, maintaining satisfying results while excluding one nucleotide position from an RNA splicing experiment, excluding 89.9% of stimuli from an eye-tracking experiment, and excluding 60% of image rows from an experiment on the MNIST dataset.

Epilepsy is a n important public health issue. An appropriate epileptiform discharge pattern detectio n of this neurological disease is a typical problem in biomedical engineering. In this paper, a new method is proposed for spike - and - wave discharge pattern dete ction based on Kendall's Tau - b c oefficient. The proposed approach is demonstrated on a real data set containing spike - and - wave discharge signals, where our performance is evaluated in terms of high Specificity, rule in (SpPIn) with 94% for patient - specific spike - and - wave discharge detection and 83% for a general spike - and - wave discharge detection. Key words: Spike - and - wave discharge; Kendall's Tau - b c oefficient; Electroencephalography ( EEG); Epilepsy; high Specificity, rule in ( SpPIn) Introduction Electroencephalography (EEG) is widely used to record the electrical activity of the brain in neurological health centers.

A shorter version of this article was originally published by Scientific American. With articles like these, the press will have you believe that machine learning can reliably predict whether you're gay, whether you'll develop psychosis, whether you'll have a heart attack, and whether you're a criminal – as well as other ambitious predictions such as when you'll die and whether your unpublished book will be a bestseller. Machine learning can't confidently tell such things about each individual. In most cases, these things are simply too difficult to predict with certainty. Researchers report high "accuracy," but then later reveal – buried within the details of a technical paper – that they were actually misusing the word "accuracy" to mean another measure of performance related to accuracy but in actuality not nearly as impressive.

Fueled by mobster movies and international espionage thrillers, the phrase has a mysterious, exciting edge to it. But as is often the case, the truth is far less appealing than the glitzy Hollywood version. In reality, money laundering is an activity that traps 40.3 million people in modern slavery, fuels political unrest, and finances terrorism across the globe. Considering the consequences, it's no wonder governments enact AML regulations. And just as money laundering crime grows more sophisticated, so too do the regulations. These regulations have honorable and important intentions, but there's no denying the ever-evolving compliance headaches they create for financial institutions.

The importance of incorporating ethics and legal compliance into machine-assisted decision-making is broadly recognized. Further, several lines of recent work have argued that critical opportunities for improving data quality and representativeness, controlling for bias, and allowing humans to oversee and impact computational processes are missed if we do not consider the lifecycle stages upstream from model training and deployment. Yet, very little has been done to date to provide system-level support to data scientists who wish to develop and deploy responsible machine learning methods. We aim to fill this gap and present FairPrep, a design and evaluation framework for fairness-enhancing interventions. FairPrep is based on a developer-centered design, and helps data scientists follow best practices in software engineering and machine learning. As part of our contribution, we identify shortcomings in existing empirical studies for analyzing fairness-enhancing interventions. We then show how FairPrep can be used to measure the impact of sound best practices, such as hyperparameter tuning and feature scaling. In particular, our results suggest that the high variability of the outcomes of fairness-enhancing interventions observed in previous studies is often an artifact of a lack of hyperparameter tuning. Further, we show that the choice of a data cleaning method can impact the effectiveness of fairness-enhancing interventions.

Free-riders in Federated Learning: Attacks and Defenses Jierui Lin, Min Du, and Jian Liu University of California, Berkeley Abstract--Federated learning is a recently proposed paradigm that enables multiple clients to collaboratively train a joint model. It allows clients to train models locally, and leverages the parameter server to generate a global model by aggregating the locally submitted gradient updates at each round. Although the incentive model for federated learning has not been fully developed, it is supposed that participants are able to get rewards or the privilege to use the final global model, as a compensation for taking efforts to train the model. Therefore, a client who does not have any local data has the incentive to construct local gradient updates in order to deceive for rewards. In this paper, we are the first to propose the notion of free rider attacks, to explore possible ways that an attacker may construct gradient updates, without any local training data. Furthermore, we explore possible defenses that could detect the proposed attacks, and propose a new high dimensional detection method called STD-DAGMM, which particularly works well for anomaly detection of model parameters. We extend the attacks and defenses to consider more free riders as well as differential privacy, which sheds light on and calls for future research in this field. I NTRODUCTION F EDERA TED learning [1], [2], [3] has been proposed to facilitate a joint model training leveraging data from multiple clients, where the training process is coordinated by a parameter server. In the whole process, clients' data stay local, and only model parameters are communicated among clients through the parameter server. A typical training iteration works as follows. First, the parameter server sends the newest global model to each client. Then, each client locally updates the model using local data and reports updated gradients to the parameter server. Finally, the server performs model aggregation on all submitted local updates to form a new global model, which has better performance than models trained using any single client's data. Compared with an alternative approach which simply collects all data from the clients and trains a model on those data, federated learning is able to save the communication overhead by only transmitting model parameters, as well as protect privacy since all data stay local.

Few assets in financial history have been as notoriously volatile as cryptocurrencies. While the long term outlook for this asset class remains unclear, we are successful in making short term price predictions for several major crypto assets. Using historical data from July 2015 to November 2019, we develop a large number of technical indicators to capture patterns in the cryptocurrency market. We then test various classification methods to forecast short-term future price movements based on these indicators. On both PPV and NPV metrics, our classifiers do well in identifying up and down market moves over the next 1 hour. Beyond evaluating classification accuracy, we also develop a strategy for translating 1-hour-ahead class predictions into trading decisions, along with a backtester that simulates trading in a realistic environment. We find that support vector machines yield the most profitable trading strategies, which outperform the market on average for Bitcoin, Ethereum and Litecoin over the past 22 months, since January 2018.

Our aim was to create simple and largely scalable machine learning-based algorithms that could predict mortality in a real-time fashion during intensive care after traumatic brain injury. We performed an observational multicenter study including adult TBI patients that were monitored for intracranial pressure (ICP) for at least 24 h in three ICUs. We used machine learning-based logistic regression modeling to create two algorithms (based on ICP, mean arterial pressure [MAP], cerebral perfusion pressure [CPP] and Glasgow Coma Scale [GCS]) to predict 30-day mortality. We used a stratified cross-validation technique for internal validation. Of 472 included patients, 92 patients (19%) died within 30 days.

After implementing these we can choose to expand the feature space with polynomial (eg X²) or interaction features (eg XY) by using sklearn's PolynomialFeatures() Note: The choice of feature scaling technique made quite a big difference to the performance of the classifier, I tried RobustScaler, StandardScaler, Normalizer and MinMaxScaler and found that MinMaxScaler worked the best.

We describe the design, the evaluation setup, and the results of the 2016 WMT shared task on cross-lingual pronoun prediction. This is a classification task in which participants are asked to provide predictions on what pronoun class label should replace a placeholder value in the target-language text, provided in lemma-tised and PoS-tagged form. We provided four subtasks, for the English-French and English-German language pairs, in both directions. Eleven teams participated in the shared task; nine for the English-French subtask, five for French-English, nine for English-German, and six for German-English. Most of the submissions outperformed two strong language-model- based baseline systems, with systems using deep recurrent neural networks outperforming those using other architectures for most language pairs.