The Matrix Factorization models, sometimes called the latent factor models, are a family of methods in the recommender system research area to (1) generate the latent factors for the users and the items and (2) predict users' ratings on items based on their latent factors. However, current Matrix Factorization models p…
On-device research index
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.
168,657 papers · 148 categories
Trend · papers per month
48 results for “weight factorization”
Deep weight factorization improves neural network training through smooth optimization of sparse penalties.
problem Challenges in applying sparse regularization in neural networks due to non-differentiability of penalties.
method Introduces deep weight factorization, decomposing weights into multiple factors for smooth optimization of -penalized networks.
result Deep weight factorization outperforms shallow factorization and pruning methods consistently across various architectures and datasets.
Unweighted matrix factorization can match or outperform weighted methods in recommender systems.
problem Improving recommendation performance with matrix factorization on implicit feedback data.
method Systematic study of various weighting schemes and matrix factorization algorithms.
result Training with unweighted data can perform comparably to, and sometimes outperform, training with weighted data.
The paper introduces a portfolio construction method using Black-Litterman model and factors.
problem Developing an efficient portfolio construction method using Black-Litterman model and factors.
method The method involves selecting 20 factors based on global market, asset class, and stock characteristics, applying various weight allocation methods including Black-Litterman model, and incorporating deep learning for dynamic weight updates.
result The model using Black-Litterman and deep learning outperforms other weight allocation schemes.
A Bayesian nonparametric approach for continual learning using neural networks.
problem Catastrophic forgetting in neural networks during sequential task settings.
method Indian Buffet Process (IBP) prior for dynamic model expansion and factorization of weight matrices.
result The method promotes positive knowledge transfer between tasks and allows for dynamic model complexity.
Test-asset construction affects factor model performance.
problem How test assets are constructed impacts factor model performance.
method Forming characteristic-unsorted random portfolios and varying stock selection, initial weighting, holding, and rebalancing.
result Test-asset construction shifts factor model rankings materially.
The study explores continuous noncrossing partitions and their relation to weighted circular factorizations.
problem Understanding the structure of continuous noncrossing partitions on the unit circle.
method Analyzes degree-d continuous noncrossing partitions and their equivalence classes of weighted linear factorizations.
result Maximal elements in the poset of continuous noncrossing partitions form a subspace homeomorphic to the dual Garside classifying space for the d-strand braid group.
New portfolios outperform traditional methods by using factor weights.
problem Improving portfolio allocation in markets driven by factors.
method Factor-weighted Dirichlet portfolios outperform uniform Dirichlet portfolios.
result Factor-weighted portfolios outperform uniformly sampled portfolios in market returns.
Study introduces a new investment strategy model using lazy factor and probability weights.
problem Optimizing investment strategies in volatile markets with transaction costs.
method Combines Price Portfolio Forecasting and Mean-Variance Models with Transaction Costs, using probability weights as laziness factor coefficients.
result Model demonstrates adaptability and generalizability in transforming investment strategies.
DMFAW improves multi-view clustering with adaptive weights and feature selection.
problem Lack of effective feature selection and empirical hyperparameter selection in existing deep matrix factorization methods.
method Introduces Deep Matrix Factorization with Adaptive Weights (DMFAW) for multi-view clustering, incorporating feature selection and dynamically updating weights using Control Theory.
result DMFAW outperforms state-of-the-art methods in clustering performance.
With the widespread engineering applications ranging from artificial intelligence and big data decision-making, originally a lot of tedious financial data processing, processing and analysis have become more and more convenient and effective. This paper aims to improve the accuracy of stock price forecasting. It improv…
AlphaForge mines and dynamically combines alpha factors for better investment performance.
problem Inconsistency and inflexibility of fixed factor weights in alpha factor mining.
method Generative-predictive neural network for factor generation and dynamic weight adjustment.
result Demonstrated superior performance in formulaic alpha factor mining and portfolio returns.
Principal component analysis (PCA) is a useful tool when trying to construct factor models from historical asset returns. For the implied volatilities of U.S. equities there is a PCA-based model with a principal eigenportfolio whose return time series lies close to that of an overarching market factor. The authors show…
Same, Same But Different - Recovering Neural Network Quantization Error Through Weight Factorizationcs.LG
Quantization of neural networks has become common practice, driven by the need for efficient implementations of deep neural networks on embedded devices. In this paper, we exploit an oft-overlooked degree of freedom in most networks - for a given layer, individual output channels can be scaled by any factor provided th…
SPIDER uses deep neural networks for streaming tensor factorization.
problem Lack of effective approach for deep tensor factorization of streaming data.
method Bayesian neural networks with spike-and-slab prior, Taylor expansions, moment matching, and EPI framework.
result Effective incremental updates for latent factors and NN weights.
DSARF models complex spatio-temporal data with deep switching auto-regressive factors.
problem Forecasting complex spatio-temporal data with recurring patterns.
method Deep switching auto-regressive factorization (DSARF) with stochastic variational inference.
result DSARF outperforms state-of-the-art methods in long- and short-term prediction accuracy.
DMSTF models spatio-temporal data with deep Markov priors.
problem Analyzing nonlinear multimodal spatio-temporal dynamics.
method Deep Markov spatio-temporal factorization with stochastic variational inference.
result DMSTF outperforms other methods in predictive performance and clustering.
PS^2 selects assets then weights for high-dimensional investing.
problem High-dimensional mean--variance investing challenges.
method Two-step framework: Lasso screening followed by standard portfolio estimation.
result FPS^2 with defactored returns improves performance.
A new weighted MCC measure improves classifier performance evaluation.
problem Lack of measures sensitive to observation weights in multiclass classification.
method Proposes weighted versions of Pearson-Matthews Correlation Coefficient (MCC) for binary and multiclass classification.
result Weighted MCC values are higher for classifiers that perform better on highly weighted observations.
RL learns to ignore factors in factor investing portfolios.
problem Combining factor investing and reinforcement learning for optimal portfolio allocation.
method RL agent learns through sequential allocations based on firms' characteristics using Dirichlet distributions.
result RL-based portfolios are very close to equally-weighted allocations, indicating agnostic factor learning.
Adaptive framework improves NB accuracy by fusing two index categories.
problem Challenges in attribute weighted NB, especially fusion of two indexes.
method Proposes ATFNB framework using switching factor to fuse two index categories.
result ATFNB outperforms basic NB and state-of-the-art models.
The study compares different models for predicting factor premiums and finds neural networks perform better but have unstable weights.
problem Predicting and timing the CMA factor premium using machine learning models.
method Compared regression models (OLS, Ridge, Random Forest, Neural Network) and tested factor timing strategies.
result Neural networks outperform linear models in explaining factor premium variance, but weights are unstable.
Proposes WM-NMF for better multi-view clustering.
problem Learning multi-view data with unequal view information content.
method Introduces a weighted multi-view NMF algorithm to learn view-specific and observation-specific weights.
result Achieves better clustering performance and handles noisy data.
Develops FGL for better portfolio allocation under common factor influence.
problem Sparsity assumption fails for stock returns driven by common factors.
method Integrates graphical models with factor structure to estimate portfolio weights and risk exposure robust to heavy-tailed distributions.
result FGL-based portfolios outperform equal-weighted and Index portfolios in empirical applications.
This paper studies optimal approximation factors in misspecified off-policy RL, identifying key factors under various settings.
problem Understanding optimal approximation factors in misspecified off-policy value function estimation.
method Examined various settings including weighted -norm, norm, state aliasing, and state coverage.
result Established optimal asymptotic approximation factors for different norms and identified two instance-dependent factors for norm.
New Ricci flow method for directed graphs with balancing factor.
problem Analyzing asymmetry in directed networks.
method Rigorous formulation of Ricci flow on directed weighted graphs with balancing factor.
result Existence and uniqueness of discrete Ricci flow solutions.
WAFFLe anonymizes federated learning weights to protect data privacy and fairness.
problem Federated learning exposes local models to attacks and underfits heterogeneous clients.
method Combines Indian Buffet Process with shared weight factors.
result Significant improvement in local test performance and fairness.
In this paper we propose and discuss different 0-1 linear models in order to solve the cardinality constrained portfolio problem by using factor models. Factor models are used to build portfolios to track indexes, together with other objectives, also need a smaller number of parameters to estimate than the classical Ma…
In this paper we show that both of the Green-Schwarz anomaly factorization formula for the gauge group and the Hořava-Witten anomaly factorization formula for the gauge group can be derived through modular forms of weight 14. This answers a question of J. H. Schwarz. We also establish generalizati…
This work shows how penalising bias terms in norm regularisation leads to sparse solutions.
problem Understanding the relation between parameter norm regularization and the sparsity of neural network solutions.
method Analyzes one hidden ReLU layer networks with unidimensional data, showing the norm required for function representation and the importance of the bias term's norm.
result Penalising the bias terms in regularisation leads to sparse solutions, enforcing the uniqueness and sparsity of the minimal norm interpolator.
Researchers develop weighted GJMS operators for smooth metric measure spaces.
problem Developing mathematical tools for smooth metric measure spaces.
method Constructing and proving formal self-adjointness of weighted GJMS operators.
result Formal self-adjointness of weighted GJMS operators proved.
K-FAC speeds up training of modern neural networks with linear weight-sharing.
problem Efficiently training modern neural networks with linear weight-sharing layers.
method Kronecker-Factored Approximate Curvature (K-FAC) applied to linear weight-sharing layers.
result K-FAC-reduce is generally faster than K-FAC-expand for deep linear networks.
We speed up marginal inference by ignoring factors that do not significantly contribute to overall accuracy. In order to pick a suitable subset of factors to ignore, we propose three schemes: minimizing the number of model factors under a bound on the KL divergence between pruned and full models; minimizing the KL dive…
Matrix factorization is one of the best approaches for collaborative filtering, because of its high accuracy in presenting users and items latent factors. The main disadvantages of matrix factorization are its complexity, and being very hard to be parallelized, specially with very large matrices. In this paper, we intr…
Recommendation from implicit feedback is a highly challenging task due to the lack of the reliable observed negative data. A popular and effective approach for implicit recommendation is to treat unobserved data as negative but downweight their confidence. Naturally, how to assign confidence weights and how to handle t…
An innovative extension of Geometric Brownian Motion model is developed by incorporating a weighting factor and a stochastic function modelled as a mixture of power and trigonometric functions. Simulations based on this Modified Brownian Motion Model with optimal weighting factors selected by goodness of fit tests, sub…
New method detects intrinsic cross-correlations in non-stationary time series affected by common factors.
problem Bias in cross-correlation analysis due to common external factors.
method Multifractal temporally weighted detrended partial cross-correlation analysis (MF-TWDPCCA).
result MF-TWDPCCA accurately detects intrinsic cross-correlations between non-stationary time series.
FAMDAD detects anomalies in mixed data using kurtosis-weighted Factor Analysis.
problem Detecting anomalies in high-dimensional mixed data.
method kurtosis-weighted Factor Analysis of Mixed Data (FAMDAD).
result Anomalies are highly separable in the first and last few dimensions of the FAMDAD embedding.
We empirically show the superiority of the equally weighted S\&P 500 portfolio over Sharpe's market capitalization weighted S\&P 500 portfolio. We proceed to consider the MaxMedian rule, a non-proprietary rule designed for the investor who wishes to do his/her own investing on a laptop with the purchase of only 20 stoc…
Regularization is essential when training large neural networks. As deep neural networks can be mathematically interpreted as universal function approximators, they are effective at memorizing sampling noise in the training data. This results in poor generalization to unseen data. Therefore, it is no surprise that a ne…
Recently, Factorization Machines (FM) has become more and more popular for recommendation systems, due to its effectiveness in finding informative interactions between features. Usually, the weights for the interactions is learnt as a low rank weight matrix, which is formulated as an inner product of two low rank matri…
Alternating optimization method based on nonnegative matrix factorizations for deep neural networkscs.LG
The backpropagation algorithm for calculating gradients has been widely used in computation of weights for deep neural networks (DNNs). This method requires derivatives of objective functions and has some difficulties finding appropriate parameters such as learning rate. In this paper, we propose a novel approach for c…
New NMF algorithm uses Toeplitz matrix for facial recognition.
problem Facial recognition performance improvement.
method Proposes TNMF algorithm with Toeplitz penalty for NMF.
result TNMF outperforms ZNMF and other constrained NMF algorithms.
Non-negative matrix factorization is a popular tool for decomposing data into feature and weight matrices under non-negativity constraints. It enjoys practical success but is poorly understood theoretically. This paper proposes an algorithm that alternates between decoding the weights and updating the features, and sho…
Machine learning factors outperform traditional portfolio optimization methods.
problem Comparing machine learning and traditional portfolio optimization methods.
method Examined machine learning and factor-based portfolio optimization using autoencoder neural networks and dimensionality reduction techniques.
result Minimum-variance portfolios using latent factors derived from autoencoders and sparse methods outperform simpler benchmarks in risk minimization.
An algorithm was recently introduced by INTECH for the purposes of estimating the trading-profit contribution of systematic rebalancing to the relative return of rules-based investment strategies. We apply this methodology to analyze the size factor through the use of equal-weighted portfolios. These strategies combine…
Deep fundamental factor models are developed to automatically capture non-linearity and interaction effects in factor modeling. Uncertainty quantification provides interpretability with interval estimation, ranking of factor importances and estimation of interaction effects. With no hidden layers we recover a linear fa…
We propose a new nonlinear factorization model for graphs that are with topological structures, and optionally, node attributes. This model is based on a pseudometric called Gromov-Wasserstein (GW) discrepancy, which compares graphs in a relational way. It estimates observed graphs as GW barycenters constructed by a se…