Improved surname geocoding and name supplements enhance race imputation accuracy.
problem Census data problems affecting race imputation accuracy.
method Fully Bayesian Improved Surname Geocoding (fBISG) and name supplements.
result Significant improvement in race imputation accuracy across all racial groups.
Optimal number of voters for a voting ensemble can be estimated from the distribution of classifier errors.
problem Finding the optimal number of voters for a voting ensemble to minimize error rate.
method Estimate the distribution of classifier errors and infer error rates for different numbers of voters.
result Lower-variance estimates of error rates can be obtained by inferring them for different numbers of voters.
This paper analyzes voter coalitions in MakerDAO's decentralized governance.
problem Understanding the governance structure and influence of voter coalitions in DAOs.
method Applied clustering algorithm to voting history of MakerDAO to identify voter coalitions.
result The emergence of a dominant voter coalition signals governance centralization in DAOs.
Analyzed a generalized voter model with power-law herding intensity, revealing anomalous diffusion and long-range memory.
problem Anomalous diffusion and long-range memory in a generalized voter model.
method Derived analytical expressions for moments and first passage time distribution, confirmed numerically.
result The model exhibits long-range memory indicators despite being a Markov model.
The paper proposes methods to identify and sample from mixtures of Mallows models for top-k rankings.
problem Identifying and sampling from mixtures of Mallows models for top-k rankings in a heterogeneous population.
method Efficient sampling algorithms and identifiability proofs for both components of the mixture.
result The identifiability and learnability of the Mallows components' parameters in the mixture.
Study improves fair opinion aggregation by balancing voter attributes.
problem Aggregation of opinions can be biased by voter attributes.
method Combines majority voting and D&S model with fairness options.
result Effective combination of Soft D&S and fairness options for different data types.
In this paper we propose a boosting based multiview learning algorithm, referred to as PB-MVBoost, which iteratively learns i) weights over view-specific voters capturing view-specific information; and ii) weights over views by optimizing a PAC-Bayes multiview C-Bound that takes into account the accuracy of view-specif…
New bounds on majority voting's accuracy for multi-class classification problems.
problem Determining the accuracy of majority voting for multi-class classification.
method Analyzing the majority voting function under different voter conditions and distributions.
result The error rate of majority voting exponentially decays or grows with the number of voters under certain conditions.
New voting rules protect against strategic voting by robust statistics.
problem Strategic voting can skew election outcomes.
method Revisit Mallows model, develop robust estimator.
result Efficient estimator achieves nearly optimal robustness.
The paper uses machine learning to detect malicious executable files.
problem Detecting malicious executable files using static analysis.
method Pre-processing, cleaning, encoding, feature selection, and ensemble training of classifiers.
result An ensemble of classifiers effectively detects malicious executable files.
We present a new modeling technique for solving the problem of ecological inference, in which individual-level associations are inferred from labeled data available only at the aggregate level. We model aggregate count data as arising from the Poisson binomial, the distribution of the sum of independent but not identic…
We consider a problem of ecological inference, in which individual-level covariates are known, but labeled data is available only at the aggregate level. The intended application is modeling voter preferences in elections. In Rosenman and Viswanathan (2018), we proposed modeling individual voter probabilities via a log…
In the past few years, a lot of attention has been devoted to multimedia indexing by fusing multimodal informations. Two kinds of fusion schemes are generally considered: The early fusion and the late fusion. We focus on late classifier fusion, where one combines the scores of each modality at the decision level. To ta…
We propose a new analytical method to study stochastic, binary-state models on complex networks. Moving beyond the usual mean-field theories, this alternative approach is based on the introduction of an annealed approximation for uncorrelated networks, allowing to deal with the network structure as parametric heterogen…
We aim to model unknown file processing. As the content of log files often evolves over time, we established a dynamic statistical model which learns and adapts processing and parsing rules. First, we limit the amount of unstructured text by focusing only on those frequent patterns which lead to the desired output tabl…
Bayesian nonparametric models for data with heterogeneous particles.
problem Deconvolving data with heterogeneous particles, like voter tallies in elections.
method Nonparametric deconvolution models (NDMs) using two tiers of Dirichlet processes.
result NDMs can recover how factor distributions vary locally for each observation.
Measuring the similarity of two files is an important task in malware analysis, with fuzzy hash functions being a popular approach. Traditional fuzzy hash functions are data agnostic: they do not learn from a particular dataset how to determine similarity; their behavior is fixed across all datasets. In this paper, we …
Model predicts trade volume changes from financial filings.
problem Improving financial market understanding through machine learning.
method Hierarchical Reformer model trained on SEDAR filings.
result Model can predict trade volume changes without explicit training.
In the spirit of behavioral finance, we study the process of opinion formation among investors using a variant of the 2D Voter Model with a tunable social temperature. Further, a feedback acting on the temperature is introduced, such that social temperature reacts to market imbalances and thus becomes time dependent. I…
In record linkage (RL), or exact file matching, the goal is to identify the links between entities with information on two or more files. RL is an important activity in areas including counting the population, enhancing survey frames and data, and conducting epidemiological and follow-up studies. RL is challenging when…
This paper examines how voter concentration affects election outcomes in district-based systems.
problem How does the spatial concentration of electors impact election results?
method The authors frame the spatial distribution of electors in a probabilistic setting and explore models to capture intra-district polarization. They use Likelihood-free Inference under the Approximate Bayesian Computation framework and supervised regression methods to estimate parameters.
result The models can capture statistical properties of real elections and show how voter distributions can change election results.
Improved race prediction model outperforms existing methods.
problem Improving race prediction using voter registration data.
method Trained BiLSTM model on voter registration data and created an ensemble.
result Achieved up to 36.8% higher OOS F1 scores than previous models.
Solves the challenge of retrieving item-specific financial information from Form 10-Q filings.
problem Retrieving item-specific information from Form 10-Q filings with varying formats and machine-readable hierarchy.
method Complements a rule-based algorithm with a Convolutional Neural Network (CNN) image classifier to itemize 10-Q files.
result Demonstrates a generalized pipeline for rapid data retrieval from a large volume of textual data.
AI analyzes corporate ESG filings to identify key dimensions and investor reactions.
problem Lack of reliable ESG ratings systems in corporate filings.
method AI techniques to separate and measure ESG dimensions and investor responses.
result AI can improve ESG ratings systems by identifying key dimensions and investor reactions.
Proposes a Latent Block Model for analyzing missing data.
problem Missing data can lead to misleading conclusions if not properly accounted for.
method Co-clustering model based on Latent Block Model, with variational EM algorithm for inference and model selection criterion.
result The proposed model reveals meaningful groups and insights from non-voters in French Parliament voting records.
Study shows social reinforcement learning can lead to persistent but metastable polarization.
problem Understanding the persistence and dynamics of opinion polarization.
method Simulation and mathematical analysis of a reinforcement learning model of opinion dynamics.
result Polarization observed in the model is metastable and will eventually lead to consensus.
The study improves sentiment analysis of 10-K filings, revealing aggregation effects on accuracy and correlation with market outcomes.
problem Lack of sentiment analysis for 10-K filings, particularly for risk disclosures.
method Supervised lexicon-learning approach applied to 10-K filings and Item 1A risk-factor sections, trained against return and volatility labels at different levels of aggregation.
result Sentiment analysis of Item 1A sections performs better at the individual-firm level, while full-filing text is more accurate at sector and portfolio levels.
New architecture separates object state and behavior for better game dynamics.
problem Lack of systematicity in black-box models for dynamic environments.
method Factorizes declarative and procedural knowledge into object files and schemata.
result Improves generalization on environments with multiple object tokens.
A lot of attention has been devoted to multimedia indexing over the past few years. In the literature, we often consider two kinds of fusion schemes: The early fusion and the late fusion. In this paper we focus on late classifier fusion, where one combines the scores of each modality at the decision level. To tackle th…
The development of models for learning music similarity and feature extraction from audio media files is an increasingly important task for the entertainment industry. This work proposes a novel music classification model based on metric learning and feature extraction from MP3 audio files. The metric learning process …
A new method for energy-efficient file delivery in small cell networks.
problem Efficient resource management in femto-caching with time-variant statistical properties.
method Formulates a resource allocation problem as a stochastic knapsack problem and a multi-armed bandit problem, developing solutions for each.
result The proposed method maximizes the accumulated utility over the horizon, especially suitable for networks with time-variant statistical properties.
cMCA uses contrastive learning to identify latent subgroups in political party data.
problem Identifying latent subgroups within political party data.
method Contrastive learning applied to multiple correspondence analysis (MCA).
result cMCA identifies latent subgroups not seen by traditional methods.
Detecting PE malware files is now commonly approached using statistical and machine learning models. While these models commonly use features extracted from the structure of PE files, we propose that icons from these files can also help better predict malware. We propose an innovative machine learning approach to extra…
We propose an extensive analysis of the behavior of majority votes in binary classification. In particular, we introduce a risk bound for majority votes, called the C-bound, that takes into account the average quality of the voters and their average disagreement. We also propose an extensive PAC-Bayesian analysis that …
Fine-grained event tagging system for SEC 8-K filings improves precision to 96%.
problem Coarse SEC item codes mislabel routine and significant events.
method Two-stage system tagging 8-K disclosures against a 119-event taxonomy.
result LLM judge finds precision rises to 96% with quality scores.
The paper uses Black-Scholes model to analyze political support and coalition agreements.
problem Determining the minimum support level for a minor party in a pre-electoral coalition.
method Modeling political support as a stochastic process with a deterministic growth rate and applying Black-Scholes option pricing theory.
result The minimum support level for a minor party to gain a representative in a pre-electoral coalition.
IVF k-means algorithm improves performance on large sparse data sets.
problem Efficiently clustering large-scale sparse data sets with numerous classes.
method Sparse data representation and inverted-file structure for high-speed and low-memory clustering.
result IVF achieves better performance than other algorithms on real document data sets.
Among the central tenets of globalization is free migration of labor. Although much has been written about its benefits, little is known about the limitations of globalization, including how immigration affects the anti-globalist sentiment. Analyzing polls data, we find that over the last three years in a group of EU c…
Storage has become a constrained resource on smartphones. Gaming is a popular activity on mobile devices and the explosive growth in the number of games coupled with their growing size contributes to the storage crunch. Even where storage is plentiful, it takes a long time to download and install a heavy app before it …
Study uses LLM to extract and compare segment disclosures from financial filings.
problem Challenges in completeness and comparability of segment disclosures in financial reports.
method Developed a large language model framework to extract and preserve segment information from Form 10-K filings.
result The LLM accurately extracts segment-level information and addresses cross-period knowledge questions.
Nowadays, CAPTCHAs are computer generated tests that human can pass but current computer systems can not. They have common usage in various web services in order to be able to detect a human from computer programs autonomously. In this way, owners can protect their web services from bots. In addition to visual CAPTCHAs…
Detects bots in code commits and characterizes their activity.
problem Identifying and separating bot activity from human developers in code commits.
method BIMAN approach using author names, commit messages, files modified, and project associations.
result AUC-ROC value of 0.9 for bot detection.
This work proposes a meta-learning approach for better adaptation of source code models.
problem Adapting source code models to unseen local contexts.
method Formulated as a meta-learning problem, selecting targeted information for adaptation.
result Improved performance in code auto-completion tasks, especially for identifiers and literals.
FinReflectKG - EvalBench benchmarks financial KG extraction from SEC 10-K filings.
problem Lack of universal benchmark and evaluation framework for financial KG construction.
method Agentic and holistic evaluation principles, deterministic commit-then-justify judging protocol, binary and ordinal evaluations.
result Reflection-based extraction outperforms single-pass extraction in comprehensiveness, precision, and relevance.
IPO Finance Agent extends Finance Agent v2 for SpaceX S-1 filings, improving accuracy and cost-efficiency.
problem Evaluating IPO due diligence tasks with long-form documents.
method Extended task domain, improved agentic harness with contextual retrieval, automated rubric generation.
result Best-performing model reaches 79.8% accuracy, cost-efficient model at 77.2% with 0.05 USD per query.
Large language models learn company embeddings from SEC filings.
problem Lack of a rigorous definition of company similarity.
method Pre-trained and finetuned large language models (LLMs) to learn embeddings from SEC filings.
result LLMs can reproduce GICS classifications and indicate similar financial performance.
A new stochastic method handles ensemble creation with cost constraints.
problem Creating ensembles under cost limitations in decision-making.
method Introducing a novel stochastic approach to solve the knapsack problem.
result The approach efficiently incorporates ensemble accuracy and cost constraints.
This paper provides a holistic study of how stock prices vary in their response to financial disclosures across different topics. Thereby, we specifically shed light into the extensive amount of filings for which no a priori categorization of their content exists. For this purpose, we utilize an approach from data mini…