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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,932 papers · 148 categories

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3671107142 · Jun 202019922001200920172026
48 results for Interacting criteria

New method for sorting with interacting criteria using value functions and convex programming.

problem Learning models for sorting with interacting criteria.
method Additive piecewise-linear value function, convex quadratic programming, regularization, classification methods.
result The proposed method outperforms classical methods in sorting tasks.

New method ranks European countries' innovation performance considering criterion interactions.

problem Lack of consensus on weighting composite innovation indicators.
method Hierarchical-SMAA-Choquet integral approach to rank and benchmark innovation performance.
result Robust measurement of innovation performances in Europe with a hierarchy of interacting composite indicators.

Fairness in machine learning has predominantly been studied in static classification settings without concern for how decisions change the underlying population over time. Conventional wisdom suggests that fairness criteria promote the long-term well-being of those groups they aim to protect. We study how static fairne…

2018-03-12abs ↗pdf ↗

Feature selection is a technique to screen out less important features. Many existing supervised feature selection algorithms use redundancy and relevancy as the main criteria to select features. However, feature interaction, potentially a key characteristic in real-world problems, has not received much attention. As a…

2012-10-06abs ↗pdf ↗

Causal Interaction Trees identify treatment subgroup effects in observational data.

problem Identifying subgroups with enhanced treatment effects in observational studies.
method Extending Classification and Regression Trees with subgroup-specific treatment effect estimators.
result The proposed algorithms enhance treatment effect heterogeneity in subgroups.

Economies and societal structures in general are complex stochastic systems which may not lend themselves well to algebraic analysis. An addition of subjective value criteria to the mechanics of interacting agents will further complicate analysis. The purpose of this short study is to demonstrate capabilities of agent-…

2018-11-07abs ↗pdf ↗

New architecture improves decision-making in dense traffic.

problem Designing accurate and compact learning architectures for autonomous vehicles in crowded conditions.
method Attention-based architecture that accounts for interactions between vehicles.
result Significant performance gains and interpretable interaction patterns.

Unified framework for risk-aware policy learning in contextual bandits.

problem Optimizing decision rules in high-stakes domains with adverse outcomes.
method Distributional framework for Lipschitz-continuous risk functionals, with novel empirical concentration inequalities.
result Data-dependent suboptimality bounds with an ildeO(1/n) ilde{\mathcal{O}}(1/\sqrt{n}) rate, matching risk-neutral offline policy optimization.

MIDAS learns to adaptively control other cars in urban driving scenarios.

problem Autonomous vehicles need to interact with other agents on the road.
method Reinforcement learning with attention mechanism to handle multiple agents.
result MIDAS policies are adaptive and robust to external changes.

Variable selection for optimal treatment regime in a clinical trial or an observational study is getting more attention. Most existing variable selection techniques focused on selecting variables that are important for prediction, therefore some variables that are poor in prediction but are critical for decision-making…

2014-05-20abs ↗pdf ↗

A natural approach to analyze interaction data of form "what-connects-to-what-when" is to create a time-series (or rather a sequence) of graphs through temporal discretization (bandwidth selection) and spatial discretization (vertex contraction). Such discretization together with non-negative factorization techniques c…

2014-06-24abs ↗pdf ↗

New models improve machine learning accuracy and transparency in finance.

problem Black-box machine learning models lack interpretability in regulated industries.
method Introducing generalized groves of neural additive models with clear feature categories and interactions.
result Generalized groves of neural additive models achieve high accuracy with predominantly linear and sparse nonlinear components.

Considering that a trader or a trading algorithm interacting with markets during continuous auctions can be modeled by an iterating procedure adjusting the price at which he posts orders at a given rhythm, this paper proposes a procedure minimizing his costs. We prove the a.s. convergence of the algorithm under assumpt…

2011-12-11abs ↗pdf ↗

We propose a novel kinetic exchange model differing from previous ones in two main aspects. First, the basic dynamics is modified in order to represent economies where immediate wealth exchanges are carried out, instead of reshufflings or uni-directional movements of wealth. Such dynamics produces wealth distributions …

2015-05-06abs ↗pdf ↗

The paper proposes an algorithm to enumerate K best models with distinct support vectors for SVM.

problem Finding multiple models with distinct support vectors for non-standard machine learning applications.
method A K-best model enumeration algorithm for SVM that efficiently finds models with distinct support vectors in the dual SVM problem.
result The algorithm efficiently finds the next best model with small latency, useful for interactive examination of requirements.

A hybrid algorithm fuses significance-based splitting with honest sample-splitting for estimating heterogeneous treatment effects.

problem Estimating heterogeneous treatment effects while maintaining valid inference.
method Significance-first splitting using a squared tt-statistic for treatment imes imes side interaction.
result Achieves approximately 90% CI coverage at the 90% nominal level across various synthetic designs and datasets.

New model handles complex non-linear relationships with hidden graph structures.

problem Modeling non-linear relationships with hidden graph-structured interactions.
method Block-diagonal localized mixture of polynomial experts (BLoMPE) regression model with penalized maximum likelihood selection criterion.
result Strong theoretical guarantee for finite-sample oracle inequality.

We give some general criteria of being a homeomorphism for continuous mappings of topological manifolds, as well as criteria of being a diffeomorphism for smooth mappings of smooth manifolds. As an illustration, we apply these criteria to the problems arising in two- and three-dimensional grid generation.

2015-04-05abs ↗pdf ↗

The study reveals flaws in pruning criteria and proposes a new assumption for better filter selection.

problem Flaws in existing pruning criteria for CNNs.
method Empirical experiments and Convolutional Weight Distribution Assumption.
result The Convolutional Weight Distribution Assumption improves filter selection in pruning.

Improved recommendations using latent embeddings from user reviews.

problem Lack of consideration for latent embeddings in multi-criteria recommender systems.
method Utilized variational autoencoders to map user reviews into latent embeddings, which are then compressed into discrete vectors for multi-criteria recommendation.
result The proposed method significantly outperforms baselines across various datasets and evaluation measures.

New criteria for Heegaard splittings ensure strong irreducibility and finite Goeritz groups.

problem Determining strong irreducibility and finite Goeritz groups of Heegaard splittings.
method Two diagrammatic criteria for Heegaard splittings, accepting arbitrary disk systems.
result Criteria ensure strong irreducibility and finite Goeritz groups for Heegaard splittings.

Develops scenario theory for multi-criteria decision making.

problem Need for robustness assessment with multiple criteria and datasets.
method Collectively treats risks associated with individual criteria for multi-criteria decision problems.
result More accurate robustness certificates and sharper quantification of simultaneous criterion satisfaction.

We consider the problem of identifying patterns in a data set that exhibit anomalous behavior, often referred to as anomaly detection. In most anomaly detection algorithms, the dissimilarity between data samples is calculated by a single criterion, such as Euclidean distance. However, in many cases there may not exist …

2011-10-17abs ↗pdf ↗

The paper evaluates criteria for selecting cryptocurrencies based on historical data.

problem High risk of cryptocurrencies due to volatility.
method Characterized returns and risks using historical data in short time windows (7 and 15 days). Analyzed the importance of criteria using various methods.
result Importance of criteria for selecting cryptocurrencies is analyzed and evaluated.

Hybrid framework improves machine learning interpretability for decision making.

problem Trade-off between model performance and interpretability in machine learning.
method Neural Network-based Multiple Criteria Decision Aiding (NN-MCDA) combining additive value model and MLP.
result Enhanced interpretability of machine learning models with good performance.

Systems rank PubMed abstracts and sentences for RDoC criteria, achieving high mAP and MAA.

problem Lack of RDoC labeled datasets and complex labelling process hinder full use of RDoC framework.
method Attention-based neural topic models, supervised and unsupervised sentence ranking models, BM25, BoW, TF-IDF.
result Best systems achieved 1st rank with 0.86 mAP and 0.58 MAA.

Unified framework for analyzing model stealing attacks and defenses.

problem Vulnerability of ML applications to model stealing attacks.
method Developed a rigorous threat model and evaluation criteria, proposed methods to quantify attack and defense strategies.
result Demonstrated the importance of attack-specific perturbations for effective defenses.

We present criteria for establishing a triangulation of a manifold. Given a manifold M, a simplicial complex A, and a map H from the underlying space of A to M, our criteria are presented in local coordinate charts for M, and ensure that H is a homeomorphism. These criteria do not require a differentiable structure, or…

2018-03-20abs ↗pdf ↗

The paper analyzes performance criteria for competing fund managers in Ito-diffusion markets.

problem Analyzing performance of competing fund managers in Ito-diffusion markets.
method Developed forward relative performance criteria and forward Nash equilibrium for passive and competitive cases.
result Extended performance criteria for investment problems in Ito-diffusion markets.

A Human-in-the-Loop Bayesian Optimization framework for constraint-aware bioprocess development.

problem Bioprocess development
method Pareto Front Guided Sampling (PFGS) with Bayesian Optimization (BO)
result Systematic identification of high-performing, feasibility-compliant, and perturbation-resilient operating conditions.

Bayesian optimization with preference learning identifies preferred solutions in multi-objective problems.

problem Optimizing multiple criteria with decision maker preferences in expensive functions.
method Bayesian optimization with interactive preference learning and active acquisition function.
result Identifies the most preferred solution with reduced interaction cost.

A new method for multi-criteria recommender systems using graph attention networks.

problem Lack of nuanced relationships between users and items based on specific criteria.
method MDGAT, a multi-edge bipartite graph with dual attention networks and contrastive learning.
result MDGAT achieves higher accuracy in predicting item ratings compared to baseline methods.

New framework for resilient bi-criteria optimization under noisy feedback.

problem Bi-criteria combinatorial optimization with noisy function evaluations.
method Introducing (α,β,δ,extttN)(α,β,δ, exttt{N})-resilience and developing a black-box framework.
result Achieves sublinear regret and constraint violation for bi-criteria bandit problems.