arXiv research

Proposes a statistical test for VAE-based anomaly detection reliability.

The diagnosis of Alzheimer's disease (AD) in routine clinical practice is most commonly based on subjective clinical interpretations. Quantitative electroencephalography (QEEG) measures have been shown to reflect neurodegenerative processes in AD and might qualify as affordable and thereby widely available markers to f…

New term ADS describes how machine learning can change user behavior.

In online display advertising, selecting the most effective ad creative (ad image) for each impression is a crucial task for DSPs (Demand-Side Platforms) to fulfill their goals (click-through rate, number of conversions, revenue, and brand improvement). As widely recognized in the marketing literature, the effect of ad…

QC-SPHARM detects Alzheimer's Disease early using hippocampal surface geometry.

For precision medicine and personalized treatment, we need to identify predictive markers of disease. We focus on Alzheimer's disease (AD), where magnetic resonance imaging scans provide information about the disease status. By combining imaging with genome sequencing, we aim at identifying rare genetic markers associa…

Comparison Lift uses bandit algorithms to optimize online ad testing.

A framework combines unsupervised and semi-supervised AD using synthetic anomalies.

FCDD improves image anomaly detection without post-hoc explainers.

Paper proposes machine learning model for early Alzheimer's diagnosis.

Click-through rate (CTR) prediction is a critical task in online advertising systems. A large body of research considers each ad independently, but ignores its relationship to other ads that may impact the CTR. In this paper, we investigate various types of auxiliary ads for improving the CTR prediction of the target a…

Proposes a new method for explaining deep CNNs used in MRI-based AD diagnosis.

Study evaluates AD methods for fraud detection in online credit card payments.

New framework for evaluating ad auctions using stochastic modeling.

Two new methods score stress test scenarios for risk managers.

Alzheimer's disease (AD) is the most common neurodegenerative disease in older people. Despite considerable efforts to find a cure for AD, there is a 99.6% failure rate of clinical trials for AD drugs, likely because AD patients cannot easily be identified at early stages. This project investigated machine learning app…

Let SL(2, $\mathbb H$) be the group of $2 \times 2$ quaternionic matrices $A=\begin{pmatrix} a & b \\ c & d \end{pmatrix}$ with quaternionic determinant $\det A=|ad-aca^{-1} b|=1$. This group acts by the orientation-preserving isometries of the five dimensional (real) hyperbolic space. We obtain discreteness criteria f…

Study on numerical reliability of AD for MaxPool in neural nets.

Early detection is a crucial goal in the study of Alzheimer's Disease (AD). In this work, we describe several techniques to boost the performance of 3D deep convolutional neural networks (CNNs) trained to detect AD using structural brain MRI scans. Specifically, we provide evidence that (1) instance normalization outpe…

New model detects Alzheimer's and severity from speech, cognitive, and language data.

Paper proposes a controllable RANSAC method for anomaly detection.

SI-CLAD improves clustering-based anomaly detection by controlling false positives.

There is great potential for damage from adversarial learning (AL) attacks on machine-learning based systems. In this paper, we provide a contemporary survey of AL, focused particularly on defenses against attacks on statistical classifiers. After introducing relevant terminology and the goals and range of possible kno…

Study on test risk dynamics in learning theory with stochastic gradient flow.

CAD-DA controls anomaly detection under domain adaptation.

This paper proposes a novel optimization principle and its implementation for unsupervised anomaly detection in sound (ADS) using an autoencoder (AE). The goal of unsupervised-ADS is to detect unknown anomalous sound without training data of anomalous sound. Use of an AE as a normal model is a state-of-the-art techniqu…

Optimizes ad pruning in sponsored search systems using reinforcement learning.

Deep network optimizes ad bidding for first-price auctions.

Recent advances in smart cities applications enforce security threads such as node replication attacks. Such attack is take place when the attacker plants a replicated network node within the network. Vehicular Ad hoc networks are connecting sensors that have limited resources and required the response time to be as lo…

Develops a framework to test excessive influence of small data subsets.

Develops a deep metric learning approach for detecting bugs in video games.

We study the effect of a relevant double-trace deformation on the partition function (and conformal anomaly) of a CFT at large N and its dual picture in AdS. Three complementary previous results are brought into full agreement with each other: bulk and boundary computations, as well as their formal identity. We show th…

In anomaly detection (AD), one seeks to identify whether a test sample is abnormal, given a data set of normal samples. A recent and promising approach to AD relies on deep generative models, such as variational autoencoders (VAEs), for unsupervised learning of the normal data distribution. In semi-supervised AD (SSAD)…

We investigated the impact of noisy linguistic features on the performance of a Japanese speech synthesis system based on neural network that uses WaveNet vocoder. We compared an ideal system that uses manually corrected linguistic features including phoneme and prosodic information in training and test sets against a …

Deep Bayesian Bandits improve personalized ads by balancing exploration and exploitation.

We propose a simple and efficient method for ranking features in multi-label classification. The method produces a ranking of features showing their relevance in predicting labels, which in turn allows to choose a final subset of features. The procedure is based on Markov Networks and allows to model the dependencies b…

Proposes a new method to improve Bayesian computation accuracy using flexible classification.

A very simple heuristic approach to the unfolding problem will be described. An iterative algorithm starts with an empty histogram and every iteration aims to add one entry to this histogram. The entry to be added is selected according to a criteria which includes a $χ^2$ test and a regularization. After a relatively s…

This paper presents a novel algorithm, based upon the dependent Dirichlet process mixture model (DDPMM), for clustering batch-sequential data containing an unknown number of evolving clusters. The algorithm is derived via a low-variance asymptotic analysis of the Gibbs sampling algorithm for the DDPMM, and provides a h…

The Black-Litterman model combines investors' personal views with historical data and gives optimal portfolio weights. In this paper we will introduce the original Black-Litterman model (section 1), we will modify the model such that it fits in a Bayesian framework by considering the investors' personal views to be a d…

Models predict Alzheimer's Dementia from spontaneous speech with high accuracy.

New test detects sparse alternatives in Gaussian random fields.

Develops a new bidding system to maximize advertiser profit.

Improves reinforcement learning extrapolation in Gridworlds.

Study asymptotic behavior of second Chern forms on degenerating Kähler-Einstein surfaces.

Hybrid systems are characterized by having an interaction between continuous dynamics and discrete events. The contribution of this paper is to provide hybrid systems with a novel geometric formulation so that controls can be added. Using this framework we describe some new global controllability tests for hybrid contr…

Adaptive RL optimizes testing resource allocation for dynamic software environments.

Paper presents a machine learning method to improve significance tests for misspecified linear models.