RNNs guide clause selection in proof trees, improving inference accuracy.
problem Improving inference accuracy in proof trees.
method Recurrent Neural Networks (RNNs) encode literals to select clauses.
result RNNs outperform gradient boosted trees in clause selection.
CTM uses conjunctive clauses for image recognition, achieving high accuracy.
problem High computational complexity and lack of interpretability in CNNs.
method Introduces Convolutional Tsetlin Machine (CTM) using conjunctive clauses in propositional logic.
result CTM achieves competitive accuracy on various benchmarks, including MNIST and Fashion-MNIST.
WTM reduces clause usage and computation time for pattern recognition.
problem High computation time and memory usage in Tsetlin Machine.
method Weighting clauses and using binomial sampling to reduce complexity.
result WTM achieves similar accuracy with fewer clauses and faster training.
Paper presents LLM-enhanced contract metadata extraction.
problem Automatic detection and annotation of legal clauses in contracts.
method Integration of publicly available and proprietary datasets with advanced LLM methodologies.
result Substantial improvements in clause identification accuracy and efficiency.
Faster Tsetlin Machines use clause indexing to speed inference and learning.
problem Overfitting and slow inference in Tsetlin Machines.
method Introduced a look-up table that indexes clauses based on feature falsification, enabling faster evaluation of clauses.
result Up to 15 times faster classification and three times faster learning on MNIST and Fashion-MNIST.
Statistical relational frameworks such as Markov logic networks and probabilistic soft logic (PSL) encode model structure with weighted first-order logical clauses. Learning these clauses from data is referred to as structure learning. Structure learning alleviates the manual cost of specifying models. However, this be…
New study shows low-degree polynomial algorithms struggle at clause densities close to Fix's.
problem Finding satisfying assignments in random k-SAT formulas at high clause densities.
method Analysis of low-degree polynomial algorithms and a new many-way overlap gap property.
result No efficient algorithms can find satisfying assignments at clause densities close to Fix's.
Improved RTM uses integer weights to reduce computation and increase interpretability.
problem Lack of interpretability in nonlinear regression models.
method Integer weighted RTM clauses, combined with a novel learning scheme.
result Significantly reduced computation cost with improved accuracy.
ClauseLens uses reinforcement learning to price reinsurance treaties transparently and auditably.
problem Opaque and difficult-to-audit reinsurance treaty pricing practices.
method ClauseLens models treaty pricing as a Risk-Aware Constrained Markov Decision Process (RA-CMDP), incorporating legal clauses and generating interpretable explanations.
result ClauseLens reduces solvency violations and improves tail-risk performance, achieving 88.2% accuracy in clause-grounded explanations.
Improved Tsetlin Machine reduces hyperparameter complexity.
problem Complex hyperparameter search in Tsetlin Machines.
method Introduces Multigranular Tsetlin Machine (MTM) with varying specificity clauses.
result MTM achieves similar performance with reduced hyperparameter tuning.
RTM extends TM for continuous output problems using conjunctive clauses.
problem Continuous output problems in machine learning.
method Modified inner inference mechanism to produce a single continuous output.
result RTM achieves better regression accuracy with fewer clauses.
This paper works out fair values of stock loan model with automatic termination clause, cap and margin. This stock loan is treated as a generalized perpetual American option with possibly negative interest rate and some constraints. Since it helps a bank to control the risk, the banks charge less service fees compared …
Insurance contracts for autonomous AI agents must be actuarially sound and resistant to gaming.
problem Designing insurance contracts for autonomous AI agents that are actuarially sound and resistant to gaming.
method Characterizing a five-attack space and proving the actuarial runtime is gaming-resistant.
result An incentive-compatible layer for actuarial control of autonomous-agent side effects.
The paper introduces closed-form expressions for interpreting Tsetlin Machines.
problem Interpreting complex Tsetlin Machines with a large number of clauses.
method Developed closed-form expressions for local and global interpretability of Tsetlin Machines.
result The expressions enable real-time feature importance assessment and data clustering.
Pricing Chinese convertible bonds using Monte Carlo simulation and dynamic programming.
problem Pricing Chinese convertible bonds accurately.
method Monte Carlo simulation and dynamic programming with regression and backward induction.
result An underpriced strategy significantly outperforms benchmarks.
Python package for functional data analysis.
problem Handling and analysis of functional data.
method Comprehensive tools for representation, preprocessing, and exploratory analysis of functional data.
result Scikit-fda package provides a comprehensive set of tools for functional data analysis.
Enhances neural networks with logical knowledge for better performance.
problem Improving neural network performance with logical knowledge.
method Integrating logical knowledge into neural networks through a new final layer with learnable clause weights.
result KENN outperforms other methods in collective classification tasks with relational data.
We present a case-study demonstrating the usefulness of Bayesian hierarchical mixture modelling for investigating cognitive processes. In sentence comprehension, it is widely assumed that the distance between linguistic co-dependents affects the latency of dependency resolution: the longer the distance, the longer the …
A Python tool assesses fairness, accountability, and transparency in AI decisions.
problem Lack of regulation and certification for AI-driven decisions.
method Developed an open-source Python toolbox to analyze fairness, accountability, and transparency aspects of machine learning.
result Automatically reports fairness, accountability, and transparency aspects of AI decisions to stakeholders.
In this note we show how to replicate a stylized CDS with a repurchase agreement and an asset swap. The latter must be designed in such a way that, on default of the issuer, it is terminated with a zero close-out amount. This break clause can be priced using the well known unilateral credit/debit valuation adjustment f…
We present a stochastic-local volatility model for derivative contracts on commodity futures able to describe forward-curve and smile dynamics with a fast calibration to liquid market quotes. A parsimonious parametrization is introduced to deal with the limited number of options quoted in the market. Cleared commodity …
NUBO simplifies Bayesian optimization for researchers.
problem Optimizing expensive functions like experiments and simulations.
method Bayesian optimization using Gaussian processes and acquisition functions.
result Transparency and user-friendly design for easy access.
Seglearn is an open-source python package for machine learning time series or sequences using a sliding window segmentation approach. The implementation provides a flexible pipeline for tackling classification, regression, and forecasting problems with multivariate sequence and contextual data. This package is compatib…
Paper characterizes and represents pairwise causal background knowledge for improved causal inference.
problem Improving causal inference by handling pairwise causal constraints.
method Graphical characterization, direct causal clause (DCC), unified representation, MPDAG, polynomial-time algorithms.
result Pairwise causal background knowledge uniquely decomposes into MPDAG and DCCs, improving causal effect identification.
Counterparty Risk FAQ: Credit VaR, PFE, CVA, DVA, Closeout, Netting, Collateral, Re-hypothecation, WWR, Basel, Funding, CCDS and Margin Lendingq-fin.PR We present a dialogue on Counterparty Credit Risk touching on Credit Value at Risk (Credit VaR), Potential Future Exposure (PFE), Expected Exposure (EE), Expected Positive Exposure (EPE), Credit Valuation Adjustment (CVA), Debit Valuation Adjustment (DVA), DVA Hedging, Closeout conventions, Netting clauses, Collateral …
Normal surface theory, a tool to represent surfaces in a triangulated 3-manifold combinatorially, is ubiquitous in computational 3-manifold theory. In this paper, we investigate a relaxed notion of normal surfaces where we remove the quadrilateral conditions. This yields normal surfaces that are no longer embedded. We …
The authors of (Cho et al., 2014a) have shown that the recently introduced neural network translation systems suffer from a significant drop in translation quality when translating long sentences, unlike existing phrase-based translation systems. In this paper, we propose a way to address this issue by automatically se…
Is an option to early terminate a swap at its market value worth zero? At first sight it is, but in presence of counterparty risk it depends on the criteria used to determine such market value. In case of a single uncollateralised swap transaction under ISDA between two defaultable counterparties, the additional unilat…
The paper analyzes how CNNs interpret NLP tasks and identify linguistic features.
problem Understanding how CNNs capture linguistic features in NLP tasks.
method Visualization techniques and error analysis to interpret CNNs.
result Identified how CNNs capture different linguistic features and their impact on model performance.
System detects financial forecasts in tweets, achieving high precision.
problem Detecting financial forecasts in social media messages.
method Natural Language Processing and Machine Learning techniques for real-time analysis.
result Achieves over 90% precision for financial forecasts.
Lifted Relational Neural Networks (LRNNs) describe relational domains using weighted first-order rules which act as templates for constructing feed-forward neural networks. While previous work has shown that using LRNNs can lead to state-of-the-art results in various ILP tasks, these results depended on hand-crafted ru…
Algorithm extracts non-monotonic rules from statistical models using HUIM.
problem Extracting non-monotonic rules from statistical learning models.
method Reduces problem to HUIM, uses TreeExplainer for feature importance.
result Significant improvement in classification metrics and training time.
We study an asset allocation stochastic problem with restriction for a defined-contribution pension plan during the accumulation phase. We consider a financial market with stochastic interest rate, composed of a risk-free asset, a real zero coupon bond price, the inflation-linked bond and the risky asset. A plan member…
Algorithm learns CNF formulas from random solutions under specific conditions.
problem Learning a CNF formula from uniform random solutions.
method Revisits Valiant's algorithm and applies Lovász local lemma conditions.
result Significantly reduces sample complexity for learning CNFs.
We present an effective technique for training deep learning agents capable of negotiating on a set of clauses in a contract agreement using a simple communication protocol. We use Multi Agent Reinforcement Learning to train both agents simultaneously as they negotiate with each other in the training environment. We al…
The paper compares machine learning methods with traditional techniques for pricing and sensitivities of financial products with path-dependent structures.
problem Evaluating financial products with early-termination clauses, especially those with path-dependent structures.
method The paper compares regression methods including randomized recurrent and feed-forward neural networks, and a novel approach using signatures of the underlying price process, with traditional polynomial basis functions for pricing and sensitivities.
result Machine learning algorithms often match the accuracy and efficiency of traditional methods for Asian and look-back options, while randomized neural networks are best for callable certificates.
We depart from the usual methods for pricing contracts with the counterparty credit risk found in most of the existing literature. In effect, typically, these models do not account for either systemic effects or at-first-default contagion and postulate that the contract value at default equals either the risk-free valu…
Python package reduces hubness in high-dimensional data.
problem Hubness in high-dimensional data affects machine learning tasks.
method Hubness analysis, reduction, and approximate neighbor search.
result Improves neighbor retrieval and learning tasks in high dimensions.
Develops a machine-learning framework for optimal share repurchase hedging.
problem Challenges in hedging share repurchase programs due to market regulations and trading activity.
method Machine-learning framework that optimizes execution and hedging of share repurchase programs.
result Substantial performance improvements and an optimized hedging approach.
Paper improves text-to-SQL models with schema-aware denoising.
problem Text-to-SQL models struggle with schema linking and grammar correctness.
method Adapts transformer-based seq-to-seq model with SeaD denoising objectives and clause-sensitive decoding.
result Improves seq-to-seq model performance on WikiSQL benchmark.
Study on rank-one ECS manifolds, focusing on dilational type.
problem Characterizing ECS manifolds with specific properties.
method Analyzing properties of pseudo-Riemannian manifolds with parallel Weyl tensor.
result Generic compact rank-one ECS manifolds are either translational or locally homogeneous.
The paper proposes modern computational methods for optimizing reinsurance contracts.
problem Optimizing catastrophe excess-of-loss reinsurance contracts with realistic constraints and risk measures.
method Two approaches: simulated annealing for local search and quantum branch & bound for future potential.
result Quantum branch & bound approach shows potential for future optimization with quantum computers.
Paper proposes machine unlearning method to forget user data from neural networks.
problem Memorization of user data in neural networks violates GDPR's right to be forgotten.
method Proposes Forsaken method to measure and achieve high forgetting rates without significant accuracy loss.
result Forsaken method achieves over 90% forgetting rate with less than 5% accuracy loss.
Solves a 60-year-old compatibility problem on manifolds with boundary.
problem Finding a compatibility operator for Lie derivatives of the metric tensor on compact Riemannian manifolds.
method Develops a framework for elliptic pre-complexes and pseudodifferential operators to correct and yield Hodge-like decompositions.
result Explicit integrability conditions for overdetermined boundary-value problems are derived, resolving the Saint-Venant problem.
Deep learning improves legal document translation, summarization, and classification.
problem Data scarcity in legal document processing.
method Multi-task deep learning to leverage transfer learning.
result Multi-task DL outperformed state-of-the-art results in all tasks.
Model forecasts natural gas consumption with Fourier series and feedback.
problem Forecast natural gas consumption for risk minimization.
method Modulated Fourier series with temperature deviations, day-ahead feedback.
result Model outperforms time series methods for long-term projections.
Improved aspect detection from few seed keywords.
problem Fine-grained aspect detection from user reviews is labor-intensive.
method Weakly supervised co-training with student-teacher approach.
result Significant improvement in F1 scores over previous methods.
Deep learning models price convertible bonds with complex reset and call features.
problem Pricing convertible bonds with path-dependent reset and call provisions.
method Formulated as a PPDE, deep learning approximates conditional expectations.
result Deep learning produces stable and accurate prices across various model specifications.