TIMME detects Twitter users' ideology from sparse, heterogeneous data.
problem Predicting political tendencies from Twitter data.
method TIMME, a multi-task multi-relational embedding model.
result TIMME outperforms state-of-the-art models for ideology detection.
New method predicts political ideology from online activity.
problem Predicting political ideology from digital footprints.
method Statistical learning approaches applied to reddit data.
result Activity in non-political forums can predict political ideology with high accuracy.
Model predicts political ideology using context vectors to mitigate bias and scarcity.
problem Scarcity and selection bias in political ideology prediction.
method Proposes a statistical model decomposing embeddings into context and position vectors, training an end-to-end model for deployment.
result Model can predict ideological labels even with minimal biased data, outperforming state-of-the-art methods.
In the context of fake news, bias, and propaganda, we study two important but relatively under-explored problems: (i) trustworthiness estimation (on a 3-point scale) and (ii) political ideology detection (left/right bias on a 7-point scale) of entire news outlets, as opposed to evaluating individual articles. In partic…
The 2016 United States presidential election has been characterized as a period of extreme divisiveness that was exacerbated on social media by the influence of fake news, trolls, and social bots. However, the extent to which the public became more polarized in response to these influences over the course of the electi…
Social Media has influenced the way people socially connect, interact and opinionize. The growth in technology has enhanced communication and dissemination of information. Unfortunately,many terror groups like jihadist communities have started consolidating a virtual community online for various purposes such as recrui…
Probabilistic methods for classifying text form a rich tradition in machine learning and natural language processing. For many important problems, however, class prediction is uninteresting because the class is known, and instead the focus shifts to estimating latent quantities related to the text, such as affect or id…
Contemporary debates on filter bubbles and polarization in public and social media raise the question to what extent news media of the past exhibited biases. This paper specifically examines bias related to gender in six Dutch national newspapers between 1950 and 1990. We measure bias related to gender by comparing loc…
The problem of ranking a set of objects given some measure of similarity is one of the most basic in machine learning. Recently Agarwal proposed a method based on techniques in semi-supervised learning utilizing the graph Laplacian. In this work we consider a novel application of this technique to ranking binary choice…
This paper opens the series of articles supplemental to the series (hep-th/9405050,q-alg/9610026,q-alg/9611003,q-alg/9611019,funct-an/9611003), which also lies in lines of general ideology exposed in the review (mp_arc/96-477). The main purpose of the activity, which has its origin and motivation presumably in the auth…
Activists align with large fund preferences for success.
problem Aligning with large fund preferences increases activist success.
method Analyzed previous proxy voting behavior to estimate preferences and correlated them with activist success.
result Campaigns with higher alignment receive more votes and are more successful.
This article is a prologue to the article "Why Markets are Inefficient: A Gambling 'Theory' of Financial Markets for Practitioners and Theorists." It presents important background for that article --- why gambling is important, even necessary, for real-world traders --- the reason for the superiority of the strategic/g…
In this paper we bring to bear some new tools from statistical learning on the analysis of roll call data. We present a new data-driven model for roll call voting that is geometric in nature. We construct the model by adapting the "Partition Decoupling Method," an unsupervised learning technique originally developed fo…
Bayesian framework explains diverse explanatory values.
problem Understanding and predicting human preferences for explanations.
method Developed a Bayesian account to integrate various explanatory values.
result Core values from psychology, statistics, and philosophy emerge from a common framework.
The paper highlights legal misunderstandings in ML fairness definitions.
problem Misalignment between ML fairness definitions and legal concepts.
method Examples and comparative analysis of legal and ML terminology.
result Both communities need to learn from these tensions.
A fundamental issue in reinforcement learning algorithms is the balance between exploration of the environment and exploitation of information already obtained by the agent. Especially, exploration has played a critical role for both efficiency and efficacy of the learning process. However, Existing works for explorati…
Proposes a comprehensive framework for financial product lead recommendations using graph representation learning and link prediction.
problem Challenges in surface lead recommendations for financial products due to changing market scenarios and difficulty in capturing holder's mindset.
method Bi-partite graph representation of financial holders and funds, GraphSage model for learning representations, link prediction model for ranking recommendations.
result The proposed graph ML solution outperforms baseline by 42%, 22%, and 14% in hit rate for top-k recommendations (50, 100, 200) and 18%, 19%, and 18% on unseen holders.
This thesis renovates classic models for pricing inflation derivatives.
problem Improving models for pricing inflation derivatives.
method Analysis and renovation of classic interest rate models.
result Renewed HJM framework for inflation derivatives pricing.
Model clusters authors and topics in short texts like social media posts.
problem Analysis of short texts is difficult due to brevity and lack of context.
method Expands Latent Dirichlet Allocation to model word dependencies and cluster users.
result Improves topic and user clustering, outperforming traditional methods.
Scalable NAS by factorizing operators into subspaces.
problem Scaling up NAS search space while avoiding operator competition.
method Factorizing a large set of candidate operators into smaller subspaces.
result Achieved state-of-the-art performance on CIFAR10 and ImageNet.
We investigate the problem of wealth distribution from the viewpoint of asset exchange. Robust nature of Pareto's law across economies, ideologies and nations suggests that this could be an outcome of trading strategies. However, the simple asset exchange models fail to reproduce this feature. A yardsale(YS) model in w…
This paper systematizes knowledge on synthetic assets in crypto.
problem Disparate academic literature on synthetic assets in crypto.
method Broad perspective, general framework, data-driven analyses.
result Highlights risks and areas of research interest in synthetic assets.
The concept of mathematical modeling is widespread across almost all of the fields of contemporary science and engineering. Because of the existing necessity of predictions the behavior of natural phenomena, the researchers develop more and more complex models. However, despite their ability to better forecasting, the …
Paper argues the bear case for Bitcoin is bounded and terminal states are neutral to positive.
problem The identity of Bitcoin's creator and the associated overhang risk.
method Quantitative analysis of Satoshi's 1.148 million BTC position, considering various preference sets.
result The terminal states most consistent with observed behavior are neutral to slightly positive for Bitcoin's effective supply.
Adaptive uncertainty quantification improves black-box model predictions in generative AI.
problem Improving uncertainty quantification for black-box models in generative AI.
method Adaptive partitioning and local calibration of conformity scores.
result Local tightening of uncertainty sets with adaptive bands.
New method uses small perturbations to improve representation learning from few labels.
problem Stability issues and label scarcity in representation learning.
method Introduces small-perturbation ideology on representation probability distribution models.
result Proposed models show better performance in clustering compared to baseline methods.
New method predicts dynamic relationships in terrorist networks.
problem Dynamic co-evolution of multiplex graphs and nodal attributes in terrorism networks.
method Time-varying stochastic latent factor models with neural network Gaussian processes.
result Superior performance in predicting unobserved dynamic relationships.
New link detection results using closures of 3-braids.
problem Link detection using homology theories.
method Closure operations on 3-braids and homology theories.
result Detection of specific links using link Floer homology, Khovanov homology, and annular Khovanov homology.
Deep learning improves anomaly detection across various fields.
problem Detecting anomalies in data with advanced approaches.
method Survey of deep learning methods for anomaly detection.
result Advancements in deep anomaly detection address unique challenges.
Graph energy helps detect communities in networks better than traditional methods.
problem Detecting communities in sparse networks where traditional methods fail.
method Using graph energy based on the full spectrum of adjacency matrices.
result The difference in graph energy between a planted partition model and an Erdős--Rényi network has a distinct transition at the detectability threshold.
Monitoring gas turbine combustors health, in particular, early detecting abnormal behaviors and incipient faults, is critical in ensuring gas turbines operating efficiently and in preventing costly unplanned maintenance. One popular means of detecting combustor abnormalities is through continuously monitoring exhaust g…
New ML-based detection improves PMH signal detection in load-modulated MIMO systems.
problem Detecting PMH signals without prior CSI is challenging and computationally expensive.
method Proposes HEM-ML and HEM-KD schemes using EM and KD-tree for efficient detection.
result Achieves comparable detection results to optimal ML detector with reduced complexity.
Develops slope detection for 3-manifolds with torus boundaries.
problem Determining slopes on the boundary of 3-manifolds with torus boundaries.
method Introduces order-detection and representation-detection of slopes, proving their equivalence.
result Shows how slopes' behavior changes with cabling, improving previous results.
2DSig-Detect detects adversarial perturbations in images.
problem Adversarial attacks degrade image model performance.
method 2D-signature embedded semi-supervised framework using rough path theory.
result 2DSig-Detect outperforms other methods in detecting adversarial perturbations.
Develops a method to detect changes in linear systems with temporal correlations.
problem Detect abrupt changes in time series data with temporal correlations.
method Data-dependent threshold for online change point detection in linear dynamical systems.
result Achieves a pre-specified upper bound on the probability of false alarms and provides a finite-sample-based bound for detection probability.
Tackles the computational hardness of HPC detection, conjecturing equivalence to PC detection.
problem Computational hardness of hypergraphic planted clique detection.
method No specific method mentioned; focuses on conjecturing equivalence.
result Equivalence of computational hardness between HPC and PC detection.
Real-time fuel leakage detection framework MOCPD improves accuracy.
problem Early detection of fuel leakage to prevent hazards and losses.
method Memory-based Online Change Point Detection (MOCPD) framework.
result MOCPD outperforms baseline methods in detection accuracy.
Study on detecting hierarchical community structures in networks.
problem Detecting hierarchical community structures in networks.
method Analysis of planted hierarchies of partitions in networks, identifying additional detectability phases.
result There are additional phases in which the presence of multiple consistent partitions can either help or hinder detection of hierarchical structures.
Deep object detection improves mitotic nucleus detection in breast cancer biopsies.
problem Challenges in automated mitotic nucleus detection in breast cancer histopathological images.
method Adapted Mask R-CNN for deep object detection, initially selects candidate regions with maximum recall, refines them with multi-object loss function.
result Improved discrimination ability (F-score of 0.86) and significant precision (0.86) for mitotic nuclei compared to two-stage models.
ECAD detects anomalies without data exchangeability, improving traffic flow detection.
problem Detecting anomalies in spatio-temporal data with missing values.
method ECAD uses conformal prediction to wrap around any regression algorithm, controlling Type-I error without data exchangeability.
result ECAD outperforms other methods in detecting anomalous traffic flow.
Autonomous driving requires 3D perception of vehicles and other objects in the in environment. Much of the current methods support 2D vehicle detection. This paper proposes a flexible pipeline to adopt any 2D detection network and fuse it with a 3D point cloud to generate 3D information with minimum changes of the 2D d…
PAC-Wrap provides provable guarantees for semi-supervised anomaly detection.
problem Ensuring reliable anomaly detection in safety-critical applications.
method PAC-Wrap wraps around existing anomaly detection methods to provide PAC guarantees.
result PAC-Wrap effectively provides rigorous guarantees for various anomaly detectors.
This paper offers a distribution-free method for post-detection changepoint localization.
problem Locating the exact time of a change in distribution after a sequential detection procedure.
method A distribution-free framework using conformal test martingales for sequential change detection and post-detection inference.
result Valid post-detection coverage guarantees and non-asymptotic bounds on confidence set size.
The study defines backdoor detection in ML and proves its infeasibility.
problem Backdoor detection in machine learning systems.
method Formal statistical definition and analysis of feasibility.
result Backdoor detection is impossible except for very small alphabet sizes.
Detects data drift in deep learning models using neural embeddings.
problem Detecting changes in data distribution in deep learning models.
method Formulates drift detection in a sequential decision framework and introduces a loss function to balance false alarms and quick detection.
result Demonstrates improved ability to balance false alarms and quick detection in change detection.
It follows from earlier work of Silver-Williams and the authors that twisted Alexander polynomials detect the unknot and the Hopf link. We now show that twisted Alexander polynomials also detect the trefoil and the figure-8 knot, that twisted Alexander polynomials detect whether a link is split and that twisted Alexand…
This research identifies flaws in drift detection methods and creates adversarial data streams to exploit them.
problem The challenge of detecting data distribution changes (drift) in real-time systems.
method Developed adversarial data streams to show weaknesses in existing drift detection schemes.
result Demonstrated that common drift detection methods can be fooled by adversarial data streams.
Multimodal deep learning improves flaw detection in software programs.
problem Current flaw detection relies on single software representations.
method Adapted multimodal deep learning models for flaw detection.
result Multimodal models outperform traditional deep learning models.