FinReflectKG builds a comprehensive financial knowledge graph from SEC filings, improving extraction quality.
problem Lack of large-scale, open-source financial knowledge graph datasets.
method Intelligent document parsing, table-aware chunking, schema-guided iterative extraction, reflection-driven feedback loop.
result Reflection-agent-based mode achieves best balance of efficiency, accuracy, and reliability.
The operational space of an autonomous vehicle (AV) can be diverse and vary significantly. This may lead to a scenario that was not postulated in the design phase. Due to this, formulating a rule based decision maker for selecting maneuvers may not be ideal. Similarly, it may not be effective to design an a-priori cost…
Dynamic rule-based investment strategies outperform static ones in pension schemes.
problem Managing retirement income with dynamic investment strategies.
method Rule-based investment strategies compared to dynamic programming.
result Rule-based strategies achieve higher probability of meeting retirement income targets.
Enhances Bayesian learning with rule-based evolutionary techniques.
problem Improving Bayesian inference with expert knowledge and data patterns.
method Combines Bayesian inference with rule-based systems and grammatical evolution.
result Automatically derives rules from data, improving point predictions and uncertainty quantification.
Autonomous driving decision-making is a great challenge due to the complexity and uncertainty of the traffic environment. Combined with the rule-based constraints, a Deep Q-Network (DQN) based method is applied for autonomous driving lane change decision-making task in this study. Through the combination of high-level …
Advances rule-based multi-label classification using conformal prediction.
problem Improving accuracy and decision making in multi-label classification.
method Combines conformal prediction with rule-based learning to provide natural conformity scores and calibrate rule assessments.
result Calibrated conformity scores enhance prediction accuracy and decision making.
Introduces a rule-based Bayesian regression for better uncertainty quantification and expert knowledge integration.
problem Handling regression problems with uncertainty quantification and expert intuition.
method Combines Bayesian inference and rule-based systems for better model performance.
result Improves model performance with better uncertainty quantification and point predictions.
Develops ML-DQA for healthcare data quality assurance.
problem Inconsistent use of real-world data in machine learning projects.
method Develops ML-DQA framework based on RWD best practices.
result Five generalizable practices emerge from ML-DQA implementation.
Rule-based classifiers quantify uncertainty using Bernoulli random variables.
problem Quantifying the uncertainty of precision estimates for rule-based text classifiers.
method Treat partitions of sub-strings as Bernoulli random variables, compare means using statistical tests, and combine classifiers using Dempster-Shafer theory.
result The approach can be used to combine binary classifiers into a multi-label classifier.
Proposes a score to compare rule-based algorithms' interpretability.
problem Lack of consensus on interpretability for predictive models.
method Defines a score with three terms: predictivity, stability, and simplicity, each quantified by simple formulas.
result Compares interpretability of rule-based and tree-based algorithms for regression and classification.
New rule-based method for classification with scalability, interpretability, and fairness.
problem Developing a scalable and fair classification method.
method Column generation for linear programming, decision tree-based heuristic, and rule-based optimization.
result The method returns interpretable rules with optimal weights and addresses fairness constraints.
DTOR explains anomalies with rule-based explanations.
problem Need to explain anomalies in data effectively.
method Applies Decision Tree Regressor to estimate anomaly scores and generate rule-based explanations.
result DTOR produces robust and consistent rule-based explanations.
Hybrid AI and rule-based framework de-identifies medical imaging data.
problem De-identifying medical imaging data to protect PHI and PII.
method Combines rule-based and AI techniques with uncertainty quantification.
result Robust performance across benchmark datasets and regulatory standards.
Improved negation detection in Dutch clinical texts using machine learning.
problem Extracting negation from clinical text for better model development.
method Comparison of rule-based and machine learning methods (biLSTM, RoBERTa).
result BiLSTM and RoBERTa models outperform rule-based method in F1 score, precision, and recall.
We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder-decoder architecture that consists of two recurrent neural networks, which has previously shown great success in…
PLANS synthesizes programs from noisy inputs using neural specs and filtering.
problem Synthesizing robust programs from noisy, raw inputs.
method Hybrid model combining neural extraction and rule-based synthesis with noise filtering.
result State-of-the-art performance in diverse environments with no ground-truth training.
We give a definition of an integer-valued function ∑iαixi∗ derived from arrow diagrams for the ambient isotopy classes of oriented spherical curves. Then, we introduce certain elements of the free Z-module generated by the arrow diagrams with at most l arrows, called relators of Type~($\check{…
Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employe…
Two approaches detect EV charging patterns at stations.
problem Identify charging patterns at electric vehicle charging stations.
method Two approaches: rule-based and hierarchical clustering.
result Hierarchical clustering revealed unexpected charging patterns.
Proposes RPG-RT for red-teaming T2I models without internal access.
problem Evaluating T2I models' security through red-teaming is challenging due to their closed-source nature and unknown defense mechanisms.
method Integrates LLM and rule-based preference modeling to dynamically adapt to unknown defense mechanisms.
result Demonstrates superior and practical approach for red-teaming T2I models.
SigD2 reduces noisy rules in rule-based classifiers for better accuracy and readability.
problem Redundant and noisy rules in rule-based classifiers reduce model accuracy and readability.
method Two-stage pruning strategy and ensemble methods (bagging and boosting) to reduce noise and improve model performance.
result SigD2 and ACboost ensemble models outperform state-of-the-art classifiers in terms of accuracy and rule count.
This paper deforms complex tori and their mirrors using gerbes.
problem Deforming complex tori and their mirror partners.
method Using flat gerbes to deform complex tori and their mirrors, constructing holomorphic line bundles over deformed objects.
result Deformed complex tori and their mirrors can be studied using flat gerbes.
Statistical model checking for PCTL on MDPs using reinforcement learning.
problem Model checking PCTL specifications on MDPs with statistical methods.
method Reinforcement learning for policy search, statistical model checking with UCB-based Q-learning.
result Provably guaranteed statistical model checking method for PCTL specifications on MDPs.
Research aims to make fact-checking models more transparent.
problem Making fact-checking models explainable in a complex field.
method Combines fact-checking methods with explainable AI techniques.
result Developed initial solutions for explainable fact-checking.
Rule-based models are often used for data analysis as they combine interpretability with predictive power. We present RuleKit, a versatile tool for rule learning. Based on a sequential covering induction algorithm, it is suitable for classification, regression, and survival problems. The presence of a user-guided induc…
In this paper, we confront the problem of deep learning's big labeled data requirements, offer a rule based strategy for extreme augmentation of small data sets and apply that strategy with the image to image translation model by Isola et al. (2016) to automate cel style cartoon coloring with very limited training data…
Mining relationships between treatment(s) and medical problem(s) is vital in the biomedical domain. This helps in various applications, such as decision support system, safety surveillance, and new treatment discovery. We propose a deep learning approach that utilizes both word level and sentence-level representations …
In a recent paper [1] we introduced the Fuzzy Bayesian Learning (FBL) paradigm where expert opinions can be encoded in the form of fuzzy rule bases and the hyper-parameters of the fuzzy sets can be learned from data using a Bayesian approach. The present paper extends this work for selecting the most appropriate rule b…
As we rely more and more on machine learning models for real-life decision-making, being able to understand and trust the predictions becomes ever more important. Local explainer models have recently been introduced to explain the predictions of complex machine learning models at the instance level. In this paper, we p…
We explore training an automatic modality tagger. Modality is the attitude that a speaker might have toward an event or state. One of the main hurdles for training a linguistic tagger is gathering training data. This is particularly problematic for training a tagger for modality because modality triggers are sparse for…
The paper optimizes exceptions in a statistical production system using machine learning.
problem Lack of curated and labeled training data for machine learning in data quality assurance.
method Explainable supervised machine learning to identify and prioritize exceptions.
result Improvement in the quality and efficiency of exceptions generated and authenticated by users.
State-of-the-art password guessing tools, such as HashCat and John the Ripper, enable users to check billions of passwords per second against password hashes. In addition to performing straightforward dictionary attacks, these tools can expand password dictionaries using password generation rules, such as concatenation…
Time-aware fact-checking improves veracity predictions for time-sensitive claims.
problem Fact-checking decisions should consider temporal information of claims and evidence.
method Investigated four temporal ranking methods to optimize evidence ranking for fact-checking models.
result Time-aware evidence ranking surpasses relevance assumptions and improves veracity predictions for time-sensitive claims.
DC-Check helps guide ML development by considering data-centric aspects.
problem Lack of standardized framework for data-centric considerations in ML.
method DC-Check is a checklist-style framework for data-centric AI at ML pipeline stages.
result Promotes thoughtfulness and transparency in ML development.
Invariant Causal Set Covering Machines avoid spurious associations.
problem Learning algorithms for rule-based models are vulnerable to spurious associations.
method Building on invariant causal prediction, propose Invariant Causal Set Covering Machines for conjunctions/disjunctions of binary-valued rules.
result The method can identify causal parents of a variable of interest in polynomial time.
The Yarowsky algorithm is a rule-based semi-supervised learning algorithm that has been successfully applied to some problems in computational linguistics. The algorithm was not mathematically well understood until (Abney 2004) which analyzed some specific variants of the algorithm, and also proposed some new algorithm…
Proves SYZ mirror symmetry for del Pezzo and rational elliptic surfaces.
problem Proving mirror symmetry for specific Calabi-Yau surfaces.
method Adapting Hein's work, constructing asymptotically semi-flat Calabi-Yau metrics, and defining a mirror map.
result Existence and uniqueness of Calabi-Yau metrics on Y∖D. Social networks are getting closer to our real physical world. People share the exact location and time of their check-ins and are influenced by their friends. Modeling the spatio-temporal behavior of users in social networks is of great importance for predicting the future behavior of users, controlling the users' mov…
We describe the infinitesimal moduli space of pairs (Y,V) where Y is a manifold with G2 holonomy, and V is a vector bundle on Y with an instanton connection. These structures arise in connection to the moduli space of heterotic string compactifications on compact and non-compact seven dimensional spaces, e.…
By the SYZ construction, a mirror pair (X,Xˇ) of a complex torus X and a mirror partner Xˇ of the complex torus X is described as the special Lagrangian torus fibrations X→B and Xˇ→B on the same base space B. Then, by the SYZ transform, we can construct a simpl…
We prove the following result announced in Todorov and Valov: Any homogeneous, metric ANR-continuum is a VGn-continuum provided dimGX=n≥1 and Hˇn(X;G)=0, where G is a principal ideal domain. This implies that any homogeneous n-dimensional metric ANR-continuum with $\check{H}^n(X;G)\neq…
We specify a result of Yokoi \cite{yo} by proving that if G is an abelian group and X is a homogeneous metric ANR compactum with dimGX=n and Hˇn(X;G)=0, then X is an (n,G)-bubble. This implies that any such space X has the following properties: Hˇn−1(A;G)=0 for every closed…
Paper checks SSC for matrix factorizations using Gurobi.
problem Checking the SSC for various matrix factorizations.
method Formulated as a non-convex quadratic optimization problem over a bounded set, solved with Gurobi.
result SSC can be checked in reasonable time for realistic scenarios.
New method evaluates language model forecasters by checking consistency of predictions.
problem Evaluating the performance of language model forecasters is difficult due to lack of ground truth.
method Developed a consistency check framework based on arbitrage to evaluate forecasters.
result Consistency metrics correlate with ground truth performance of LLM forecasters.
Study how past radiation determines present matter in Penrose's cyclic cosmology.
problem Determining matter content in the present eon from past radiation in Penrose's cyclic cosmology.
method Solve Einstein's equations for a spherical wave in the past eon, then apply reciprocity to find the present eon's matter content.
result The present eon is filled with three types of radiation: a damped wave, an in-going wave, and randomly scattered waves.
Recent exploration of optimal individualized decision rules (IDRs) for patients in precision medicine has attracted a lot of attention due to the heterogeneous responses of patients to different treatments. In the existing literature of precision medicine, an optimal IDR is defined as a decision function mapping from t…
Time series forecasting models fail to consistently select the best model across different datasets.
problem Inconsistency in model selection for time series forecasting across varying data regimes.
method Characterized time series using descriptors like trend strength, seasonality, noise level, and temporal dependence. Developed a rule-based selection mechanism to map data regimes to candidate models.
result Rule-based model selection achieves low accuracy, with correct model identification occurring in only a small fraction of cases.
FinRL-X unifies trading components for AI and rule-based strategies.
problem Inconsistent between research and live deployment in trading platforms.
method Modular architecture integrating data processing, strategy construction, backtesting, and execution.
result Unified protocol supports AI and rule-based trading components without altering execution.