arXiv research

New method detects evasive product reviews that evade traditional spam detection.

DeepCapture detects image spam emails using CNN and data augmentation.

This paper tackles spam detection on Twitter by analyzing correlated features.

Paper proposes spamGAN to detect and generate opinion spam using limited labeled data.

Defense against spam filter attacks using mixture models.

DeepQuarantine detects and quarantines suspicious emails.

Visual spoofing bypasses spam filters and plagiarism detection.

Paper quarantines unreliable Yelp users by detecting review spam.

Paper tackles spam filtering on forums using synthetic oversampling.

Novel spam filter improves e-mail classification accuracy.

To date, most studies on spam have focused only on the spamming phase of the spam cycle and have ignored the harvesting phase, which consists of the mass acquisition of email addresses. It has been observed that spammers conceal their identity to a lesser degree in the harvesting phase, so it may be possible to gain ne…

Machine learning models have been widely used in security applications such as intrusion detection, spam filtering, and virus or malware detection. However, it is well-known that adversaries are always trying to adapt their attacks to evade detection. For example, an email spammer may guess what features spam detection…

Enhances social spam detection using multi-level dependency of relational sequences.

New model improves classifier security against evasion attacks by selecting features.

Discriminative classifier for compositional data using hierarchical mixture of Generalized Dirichlet models.

We propose a nonparametric method for detecting nonlinear causal relationship within a set of multidimensional discrete time series, by using sparse additive models (SpAMs). We show that, when the input to the SpAM is a $β$-mixing time series, the model can be fitted by first approximating each unknown function with a …

Paper builds ML classifier to detect crypto-ransomware.

In an analysis of the US, the UK, and the German stock market we find a change in the behavior based on the stock's beta values. Before 2006 risky trades were concentrated on stocks in the IT and technology sector. Afterwards risky trading takes place for stocks from the financial sector. We show that an agent-based mo…

In high dimensions, most machine learning methods are brittle to even a small fraction of structured outliers. To address this, we introduce a new meta-algorithm that can take in a base learner such as least squares or stochastic gradient descent, and harden the learner to be resistant to outliers. Our method, Sever, p…

New algorithm speeds up RNN time series prediction by filtering noise.

A new distributed algorithm for fitting sparse additive models with feature division and decorrelation.

We present an agent behavior based microscopic model that induces jumps, spikes and high volatility phases in the price process of a traded asset. We transfer dynamics of thermally activated jumps of an unexcited/ excited two state system discussed in the context of quantum mechanics to agent socio-economic behavior an…

In this paper we explore the "vector semantics" problem from the perspective of "almost orthogonal" property of high-dimensional random vectors. We show that this intriguing property can be used to "memorize" random vectors by simply adding them, and we provide an efficient probabilistic solution to the set membership …

New algorithm improves convergence of AUC maximization.

Consider a multi-variate time series $(X_t)_{t=0}^{T}$ where $X_t \in \mathbb{R}^d$ which may represent spike train responses for multiple neurons in a brain, crime event data across multiple regions, and many others. An important challenge associated with these time series models is to estimate an influence network be…

A great variety of text tasks such as topic or spam identification, user profiling, and sentiment analysis can be posed as a supervised learning problem and tackle using a text classifier. A text classifier consists of several subprocesses, some of them are general enough to be applied to any supervised learning proble…

A function $f: \mathbb{R}^d \rightarrow \mathbb{R}$ is referred to as a Sparse Additive Model (SPAM), if it is of the form $f(\mathbf{x}) = \sum_{l \in \mathcal{S}}φ_{l}(x_l)$, where $\mathcal{S} \subset [d]$, $|\mathcal{S}| \ll d$. Assuming $φ_l$'s and $\mathcal{S}$ to be unknown, the problem of estimating $f$ from it…

Adversarial attacks against neural networks are a problem of considerable importance, for which effective defenses are not yet readily available. We make progress toward this problem by showing that non-negative weight constraints can be used to improve resistance in specific scenarios. In particular, we show that they…

We introduce a new algorithm, called adaptive sparse backfitting algorithm, for solving high dimensional Sparse Additive Model (SpAM) utilizing symmetric, non-negative definite smoothers. Unlike the previous sparse backfitting algorithm, our method is essentially a block coordinate descent algorithm that guarantees to …

The modified J-flow with Calabi ansatz shows convergence or blow-up behavior based on topological constants.

We develop a theoretical trading conditioning model subject to price volatility and return information in terms of market psychological behavior, based on analytical transaction volume-price probability wave distributions in which we use transaction volume probability to describe price volatility uncertainty and intens…

MatchGNet detects malware by learning program behavior graphs.

New methods needed to estimate individual treatment effects.

The quantitative aspirations of economists and financial analysts have for many years been based on the belief that it should be possible to build models of economic systems - and financial markets in particular - that are as predictive as those in physics. While this perspective has led to a number of important breakt…

This paper addresses the problem of inferring a regular expression from a given set of strings that resembles, as closely as possible, the regular expression that a human expert would have written to identify the language. This is motivated by our goal of automating the task of postmasters of an email service who use r…

A function $f: \mathbb{R}^d \rightarrow \mathbb{R}$ is a Sparse Additive Model (SPAM), if it is of the form $f(\mathbf{x}) = \sum_{l \in \mathcal{S}}φ_{l}(x_l)$ where $\mathcal{S} \subset [d]$, $|\mathcal{S}| \ll d$. Assuming $φ$'s, $\mathcal{S}$ to be unknown, there exists extensive work for estimating $f$ from its sa…

New insights into ML models' accuracy and generalization for scientific problems.

Sparse coding has shown its power as an effective data representation method. However, up to now, all the sparse coding approaches are limited within the single domain learning problem. In this paper, we extend the sparse coding to cross domain learning problem, which tries to learn from a source domain to a target dom…

The paper examines how updates to probabilistic models influence behavior based on evidence.

Survey of methods for detecting fraud in networks.

Detects anomalies in product health metrics at eBay for better alerts.

Recently, machine learning algorithms have successfully entered large-scale real-world industrial applications (e.g. search engines and email spam filters). Here, the CPU cost during test time must be budgeted and accounted for. In this paper, we address the challenge of balancing the test-time cost and the classifier …

New CTRL algorithm adapts to varying problem difficulty.

Neural network predicts nonlinear safety behavior based on personality traits.

ALMANACS benchmarks explainability methods on simulatability.

Advances in the field of inverse reinforcement learning (IRL) have led to sophisticated inference frameworks that relax the original modeling assumption of observing an agent behavior that reflects only a single intention. Instead of learning a global behavioral model, recent IRL methods divide the demonstration data i…

This paper introduces a method to find complete and interpretable concept-based explanations for deep neural networks.

RL agent learns to smoothly change lanes in a dynamic driving environment.