Proposes a statistical test for VAE-based anomaly detection reliability.
problem Ensuring reliability of anomaly detection in high-stakes applications.
method Variance Autoencoder (VAE) Test based on selective inference.
result Validates VAE-based anomaly detection with p-values controlling false detection probability.
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.
problem Machine learning systems can unintentionally change user behavior, affecting performance.
method Introduced `unit tests` and mitigation strategy for hidden incentives in auto-induced distributional shift.
result Meta-learning and Q-learning sometimes fail unit tests but pass with mitigation strategy.
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.
problem Early detection of Alzheimer's Disease (AD) using hippocampal surface geometry.
method Spherical harmonics registration, conformality and curvature distortions quantification, t-test feature selection, SVM classification.
result 85.2% testing accuracy on ADNI data, 81.2% on aMCI progression data.
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.
problem Optimizing online ad testing to maximize click-through rates.
method Bandit-based experimentation algorithm that adapts to test results.
result Ad click-through rates increased by 46% on average.
A framework combines unsupervised and semi-supervised AD using synthetic anomalies.
problem Improving anomaly detection in both unsupervised and semi-supervised settings.
method Proposes a new framework that uses both known and synthetic anomalies for training.
result Synthetic anomalies improve anomaly modeling in low-density regions and provide optimal convergence guarantees.
FCDD improves image anomaly detection without post-hoc explainers.
problem Image anomaly detection, especially pixel-wise.
method Fully Convolutional Data Description (FCDD) directly addresses anomaly detection without post-hoc methods.
result FCDD achieves state-of-the-art results on pixel-wise AD tasks.
Paper proposes machine learning model for early Alzheimer's diagnosis.
problem Early and accurate diagnosis of Alzheimer's Disease.
method Machine learning models, demographic, biomarker, and cognitive test data.
result 90% accuracy and 87% accuracy in predicting Alzheimer's development.
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.
problem Lack of transparency and interpretability in deep CNN models for AD diagnosis.
method Swap Test method designed specifically for brain scan task.
result Proposed method provides more suitable explanations for AD diagnosis using MRI.
Study evaluates AD methods for fraud detection in online credit card payments.
problem Fraud detection in online credit card payments using anomaly detection methods.
method Assessed several recent anomaly detection methods and compared them with standard supervised learning methods.
result LightGBM outperforms other methods but is more sensitive to distribution shifts.
New framework for evaluating ad auctions using stochastic modeling.
problem Challenges in evaluating deterministic ad auctions.
method Repurposed bid landscape model to approximate propensity scores, enabling robust OPE estimators.
result Remarkable alignment with online A/B test results, achieving 92% MDA in CTR prediction.
Two new methods score stress test scenarios for risk managers.
problem Comparing and evaluating stress test scenarios for risk managers.
method Inspired by Archer-Mouy-Selmi, two methodologies for scoring stress test scenarios.
result New methods can compare and evaluate stress test scenarios.
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, H) be the group of 2×2 quaternionic matrices A=(acbd) with quaternionic determinant detA=∣ad−aca−1b∣=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.
problem Reliability of automatic differentiation for nonsmooth operations like MaxPool.
method Investigation across precision levels and architectures on various datasets.
result Lower norms of nonsmooth Jacobians help maintain stable learning.
New model detects Alzheimer's and severity from speech, cognitive, and language data.
problem Detecting Alzheimer's disease and its severity from multimodal data.
method Multimodal ensemble system using acoustic, cognitive, and linguistic features.
result State-of-the-art accuracy and robustness in AD detection and MMSE score regression.
Paper proposes a controllable RANSAC method for anomaly detection.
problem Anomaly detection in regression models to improve prediction accuracy.
method CTRL-RANSAC method using hypothesis testing and Selective Inference framework.
result The method controls the probability of misidentifying anomalies below a specified level.
Improved CNNs detect Alzheimer's with 14% accuracy boost.
problem Early detection of Alzheimer's Disease using MRI scans.
method Optimized 3D CNNs with instance normalization, spatial downsampling, model widening, and age information.
result 14% increase in test accuracy distinguishing AD, MCI, and controls.
SI-CLAD improves clustering-based anomaly detection by controlling false positives.
problem Lack of reliability in clustering-based anomaly detection.
method SI-CLAD (Statistical Inference for CLustering-based Anomaly Detection) using Selective Inference framework.
result SI-CLAD rigorously controls false detection probability below a specified significance level.
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.
problem Understanding test risk in stochastic gradient flow dynamics.
method Path integral formulation for small learning rates, explicit computation for weak features.
result Explicit corrections due to stochastic term in dynamics, good agreement with simulations.
CAD-DA controls anomaly detection under domain adaptation.
problem Valid statistical inference after domain adaptation.
method Conditional Selective Inference to handle domain adaptation effects.
result Valid statistical inference under domain adaptation achieved.
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.
problem How to efficiently select top K ads from N candidates to maximize revenue.
method Model-free reinforcement learning approach considering downstream as a black-box environment.
result Remarkable improvements in revenue achieved through reinforcement learning.
Deep network optimizes ad bidding for first-price auctions.
problem Optimizing bid prices for first-price auctions in online advertising.
method Introduced a deep distribution network for optimal bidding.
result Algorithm outperforms previous methods in terms of surplus and eCPX metrics.
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.
problem Identifying when small data subsets significantly impact model conclusions.
method Formalizes the concept of most influential sets, deriving influence formulas and extreme value distributions.
result Allows rigorous hypothesis testing for excessive influence, resolving contested findings.
Develops a deep metric learning approach for detecting bugs in video games.
problem Automated detection of bugs in video games.
method State-State Siamese Networks (S3N) for deep metric learning.
result S3N learns meaningful embeddings to identify various types of bugs.
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…
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.
problem Recommender systems favor items already engaged by users, causing new campaigns to remain unexplored.
method Formulated as a contextual bandit, implemented exploration techniques using posterior distribution and bootstrapped models.
result Demonstrated a positive gain in exploration model in offline simulation and online AB setting.
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.
problem Bayesian computations accuracy check using rank-based simulation-based calibration has limitations.
method Replaces marginal rank test with a flexible classification approach that learns from data.
result Improves statistical power and provides an interpretable divergence measure of miscalibration.
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 χ2 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…
Deep learning models misclassify malware with added benign features.
problem Detecting malware with deep learning when it's mixed with benign code.
method Trained a deep neural network classifier using benign and malware features. Demonstrated the impact of adding benign features to malware. Used data augmentation to improve classifier robustness.
result Adding benign features to malware significantly increases false negatives.
Models predict Alzheimer's Dementia from spontaneous speech with high accuracy.
problem Early diagnosis of Alzheimer's Dementia (AD) through spontaneous speech analysis.
method Compared natural language processing techniques including SVM, GBDT, CRFs, and Transformer-based models.
result Top models achieve 0.81-0.82 test set scores for AD vs controls and 4.58 RMSE for Mental Mini State Exam scores.
New test detects sparse alternatives in Gaussian random fields.
problem Detecting sparse alternatives in Gaussian random fields.
method Ad-hoc Kac Rice formula for second maximum distribution, exact spacing test.
result Exact t-spacing test for high power in detecting sparse alternatives. Develops a new bidding system to maximize advertiser profit.
problem Inaccurate prediction of ad lift-effect due to biased log data.
method Unbiased Lift-based Bidding System that predicts lift-effect from biased log data.
result Demonstrates superior and practical high-performing lift-based bidding strategy.
Improves reinforcement learning extrapolation in Gridworlds.
problem Generalizing to unseen states in reinforcement learning.
method Avoiding deterministic action choice, using ego-centric representation, incorporating symmetry, and adding an entropy term.
result Significant improvement in extrapolation performance in Gridworld environments.
Study asymptotic behavior of second Chern forms on degenerating Kähler-Einstein surfaces.
problem Asymptotic behavior of second Chern forms on degenerating Kähler-Einstein surfaces with ADE singularities.
method Investigates a function on the unit disc defined by fiber integrals of the forms with a smooth test function, showing a lower bound of Hölder exponent at the origin for both cscK-metrics and Ricci-flat metrics.
result Shows bounds of Hölder exponent for both cscK-metrics and Ricci-flat metrics.
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…
This paper accelerates D-PSGD and AD-PSGD for large-scale deep learning tasks.
problem Decreasing spectral gap with increasing number of learners hampers convergence in D-PSGD and AD-PSGD.
method Improves spectral gap while minimizing communication cost through new techniques.
result Demonstrates faster training times and lower error rates on large-scale tasks.
Two VAE-based methods improve anomaly detection in semi-supervised settings.
problem Anomaly detection with limited labeled data.
method Intuitive idea of separating latent vectors for normal and outlier data, derived from probabilistic formulations.
result Marked improvement in outlier detection compared to state-of-the-art methods.