Bayesian method learns causal orderings from heterogeneous data.
problem Learning causal structure from heterogeneous data.
method Order-based Bayesian framework for Gaussian DAG models.
result Causal ordering is identifiable up to two permutations.
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New pruning method for sparse additive models speeds up causal structure learning.
problem Efficiently prune spurious edges from fully-connected DAG induced by estimated topological order.
method Sparse additive models combined with randomized tree embedding and group-wise sparse regression.
result Significantly faster than existing pruning methods while maintaining comparable accuracy.
A new algorithm for generalized multivariate regression with monotonic responses.
problem Generalized multivariate regression with monotonic responses.
method Semi-parametric order-based algorithm maximizing rank correlation.
result The algorithm is a consistent estimator with a convergence rate of .
New criterion for bi-ordering free groups under automorphisms.
problem Bi-ordering free groups under automorphisms.
method New criterion for bi-ordering free groups.
result Fundamental group of the magic manifold is bi-orderable.
One of the basic tasks for Bayesian networks (BNs) is that of learning a network structure from data. The BN-learning problem is NP-hard, so the standard solution is heuristic search. Many approaches have been proposed for this task, but only a very small number outperform the baseline of greedy hill-climbing with tabu…
A prognosis oriented microscopic stock market modelcond-mat.stat-mech
We present a new microscopic stochastic model for an ensemble of interacting investors that buy and sell stocks in discrete time steps via limit orders based on individual forecasts about the price of the stock. These orders determine the supply and demand fixing after each round (time step) the new price of the stock …
Paper proposes a second-order method for faster SVI convergence.
problem Poor convergence rate of first-order SVI algorithms.
method Derives Hessian matrix and implements two numerical schemes for efficient second-order SVI.
result Proposed approach achieves faster convergence compared to first-order SVI.
We develop a new statistical test for comparing variables with varying scales.
problem Comparing variables with different scales in multidimensional spaces.
method Order based on expectations of random variables, generalized stochastic dominance (GSD) order, regularized statistical test, linear optimization, imprecise probability models.
result Validated through multidimensional data from various fields.
Structural equation models and Bayesian networks have been widely used to analyze causal relations between continuous variables. In such frameworks, linear acyclic models are typically used to model the datagenerating process of variables. Recently, it was shown that use of non-Gaussianity identifies a causal ordering …
A new method for selecting model order in statistical shape models.
problem Choosing the right model order in PDMs for optimal performance.
method Information-theoretic criteria for selecting model order.
result The new method provides a better trade-off between overfitting and underfitting.
Proposes SGCN for spatially structured data.
problem Lack of node neighbor ordering in GCNs.
method Uses spatial features to learn from graphs with spatial positions.
result Empirically outperforms state-of-the-art methods.
A new family of stochastic dominance orders based on distortion functions.
problem Determining a continuum of dominance relations for risk assessment.
method Introducing H-distorted stochastic dominance, a generalized family of stochastic orders.
result Power-distorted stochastic dominance is particularly appealing due to its simplicity and statistical interpretations.
New depth function for partial orders helps compare machine learning algorithms.
problem Comparing machine learning algorithms using non-standard data types.
method Adapted simplicial depth to partial orders, using ufg depth for comparison.
result Demonstrates promising variety of analysis approaches based on ufg methods.
Generative model improves intraday electricity price forecasting.
problem Intraday electricity price forecasting for improved trading strategies.
method Generative neural network model for probabilistic path forecasts.
result Generative model leads to higher profit gains than benchmark methods.
A new selective ensemble learning strategy reduces false discovery rate and improves selection accuracy.
problem Improving variable selection accuracy and reducing false discovery rate in ensemble learning.
method A novel ordering-based selective ensemble learning strategy with a greedy sorting strategy to rearrange ensemble members.
result Pruned StabSel generally achieves higher selection accuracy and lower false discovery rates than StabSel and other methods.
Proposes a score-based approach to learn causal graphs in the presence of latent variables.
problem Learning causal graphs in the presence of latent confounders.
method Greedy algorithm over the space of posets for causal structure discovery.
result Sparsest Poset formulation motivates a new algorithm for causal structure discovery.
The standard deviation and Gini mean difference order based on tail behavior.
problem Ordering between standard deviation and Gini mean difference for real-valued risks.
method Analysis of the mean excess function of the pairwise difference .
result Dominance regimes of SD and GMD are determined by tail behavior of the distribution.
Performance Optimization of a Fuzzy Entropy based Feature Selection and Classification Frameworkcs.LG
Optimized fuzzy entropy framework improves feature selection and classification performance.
problem Improving feature selection and classification in fuzzy entropy frameworks.
method Implemented and compared combinations of ideal vectors, maximal similarity classifiers, and fuzzy entropy functions.
result Optimized combination of ideal vector, similarity classifier, and fuzzy entropy function achieved the most stable performance for all three datasets.
Interpretable framework evaluates structure learning methods for causal discovery from observational data.
problem Evaluation of structure learning methods under assumption violations in causal discovery.
method Six-dimensional evaluation metric (DOS) tailored for causal discovery.
result Amortized causal discovery delivers results with high proximity to the optimal solution.
We extend the recently introduced theory of Lovasz-Bregman (LB) divergences (Iyer & Bilmes 2012) in several ways. We show that they represent a distortion between a "score" and an "ordering", thus providing a new view of rank aggregation and order based clustering with interesting connections to web ranking. We show ho…
We extend the recently introduced theory of Lovasz-Bregman (LB) divergences (Iyer & Bilmes, 2012) in several ways. We show that they represent a distortion between a 'score' and an 'ordering', thus providing a new view of rank aggregation and order based clustering with interesting connections to web ranking. We show h…
Vector autoregression (VAR) is a fundamental tool for modeling multivariate time series. However, as the number of component series is increased, the VAR model becomes overparameterized. Several authors have addressed this issue by incorporating regularized approaches, such as the lasso in VAR estimation. Traditional a…
New algorithm speeds up learning of graphical models.
problem Learning graphical models with sparse structure efficiently.
method Vertex-greedy score-based algorithm for learning DAGs.
result Polynomial runtime for learning DAG models.
BWS selects best window subsets for efficient data pruning.
problem Challenges in selecting subsets of large datasets for neural network training.
method Best Window Selection (BWS) by choosing optimal window intervals from ordered sample scores.
result BWS outperforms other methods across various selection ratios and datasets.
The paper finds formulas for word lengths and conjugacy classes in surface groups.
problem Finding formulas for word lengths and conjugacy classes in surface groups.
method Investigating symmetric presentations and normal forms of conjugacy classes.
result Derives three formulae for word lengths and provides efficient algorithms for conjugacy problems.
Deep learning compresses L-values for QAM symbols, reducing memory and improving performance.
problem Efficiently storing log-likelihood ratios (L-values) for QAM modulation.
method A deep autoencoder that jointly compresses and reconstructs L-values with a weighted loss function.
result Reduces memory footprint by up to two times with less than 0.1 dB performance loss.
RL agent learns to place limit orders for trading signals in financial markets.
problem Training an RL agent to execute trading signals in limit order book markets.
method Deep Duelling Double Q-learning with APEX architecture, using synthetic alpha signals.
result RL agent outperforms heuristic trading strategies in inventory management and order placing.
We consider concepts and models for measuring inequality in the distribution of resources with a focus on how inequality varies as a function of covariates. Lorenz introduced a device for measuring inequality in the distribution of income that indicates how much the incomes below the u quantile fall short of the…
Estimation, Prediction, and Assortment Optimization for Markov Chain Choice Models with Panel Datacs.LG
Proposes new methods for Markov chain choice models with panel data.
problem Dependence among transactions for the same customer in historical data.
method Expectation-maximization (EM) algorithms incorporating partial-ordering preference information.
result EM algorithms outperform traditional methods on synthetic and real datasets.
Scalable method learns context-specific models for hundreds of variables.
problem Learning context-specific models for large numbers of variables.
method Order-based Markov chain Monte-Carlo search with context-specific sparsity assumption.
result Method scales to hundreds of variables and learns accurate models.
Proposes an efficient method for ordered counterfactual explanations.
problem Insufficient explanation of perturbation vectors for executing actions.
method Mixed-Integer Linear Optimization (MILP) approach for evaluating and extracting optimal pairs of actions and orders.
result Demonstrated effectiveness of the proposed method on real datasets.
Study finds internal view preferred over external view in defining startups.
problem Insufficient understanding of how to start a new business.
method Empirical study with 701 entrepreneurs reflecting on nine resource categories and three business mission elements.
result Tendency to favor internal view over external view, stronger in stable economies.
Paper analyzes Winograd convolution errors and proposes methods to reduce them.
problem Reduction of floating point error in Winograd convolution for deep neural networks.
method Analysis of worst case FP error, estimation of norm and conditioning, proposed evaluation orderings, sampling points selection, mixed-precision convolution, pairwise summation.
result Proposed methods significantly reduce FP error for a given block size, allowing larger block sizes and reduced computation.
Optimizes pension mix of PAYGO, EET, and individual savings.
problem Balancing PAYGO, EET, and individual savings in funded pension schemes.
method Solves a Nash equilibrium between pension participants and government, considering age-dependent preferences and optimal asset allocation.
result Identifies critical ages and optimal contribution rates for maximizing overall utility.
xDeepInt learns both vector-wise and bit-wise feature interactions.
problem Learning feature interactions for CTR prediction and recommendation.
method Polynomial Interaction Network (PIN) architecture with subspace-crossing mechanism.
result xDeepInt outperforms state-of-the-art models in CTR prediction and recommendation.
In this paper we introduce a completely continuous and time-variate model of the evolution of market limit orders based on the existence, uniqueness, and regularity of the solutions to a type of stochastic partial differential equations obtained in Zheng and Sowers (2012). In contrary to several models proposed and res…
ARCS learns Bayesian networks by optimizing a regularized Cholesky score over permutations.
problem Learning Bayesian networks from data.
method Annealing on regularized Cholesky score (ARCS) for topological sorting.
result ARCS outperforms existing methods in learning Bayesian networks.
Enhances clustering performance with a novel high-order Laplacian matrix.
problem Limited representation capability and insufficient information exploitation in multi-view spectral clustering.
method Proposes a multi-view spectral clustering algorithm that learns a high-order optimal neighborhood Laplacian matrix.
result Improves clustering performance through enhanced representation capacity of the learned optimal Laplacian matrix.
VI struggles to fully quantify uncertainty when distributions don't factorize.
problem Uncertainty quantification in non-factorizable distributions.
method Analysis of variational inference trade-offs and divergence choices.
result Different divergences yield different measures of uncertainty in VI.
RGRR allocates between QQQ and DIA based on relative states, improving Sharpe and CAGR.
problem Optimizing ETF allocation between QQQ and DIA for better risk-adjusted returns.
method Screened relative and macro states, globally screened interactions, fixed position mapping, walk-forward validation.
result RGRR improves Sharpe and CAGR compared to 100% QQQ and 50/50 QQQ-DIA allocations.