Unified framework guarantees exactness of asymmetric low-rank SDP learning.
problem Exactness of asymmetric low-rank SDP learning with a quadratic penalty.
method Unified regularized asymmetric Burer-Monteiro factorization framework.
result Explicit lower bound on penalty parameter γ for exactness. We quantitatively relate the Patterson-Sullivant currents and generic stretching factors for free group automorphisms to the asymmetric Lipschitz metric on Outer space and to Guirardel's intersection number.
Gradient descent solves asymmetric low-rank matrix factorization efficiently.
problem Optimizing asymmetric low-rank matrix factorization with non-convex and non-smoothness issues.
method Randomly initialized gradient descent with new symmetrization and perturbation techniques.
result Gradient descent converges to a global minimum of the asymmetric low-rank factorization problem.
Gradient descent recovers principal components of overparametrized asymmetric matrices without explicit regularization.
problem Asymmetric matrix factorization under overparametrization with minimal rank assumptions.
method Vanilla gradient descent with small random initialization and proper early stopping.
result Gradient descent produces the best low-rank approximation without explicit regularization.
Random walks on free groups reveal asymmetric expansion factors.
problem Understanding expansion factors in free groups.
method Random walks and BGIP on metric spaces.
result Generic outer automorphisms have different forward and backward expansion factors.
Bayesian VI copula models capture asymmetric intraday equity dependence.
problem Modeling asymmetric and extreme tail dependence in financial data.
method Bayesian variational inference for skew-t copula models in high dimensions.
result The copula captures substantial heterogeneity in asymmetric dependence over equity pairs and time.
Gradient descent solves asymmetric low-rank matrix sensing without balancing.
problem Recovering asymmetric low-rank matrices from linear measurements.
method Gradient descent with spectral initialization, avoiding balancing term.
result Gradient descent converges linearly without balancing, factors stay balanced.
Paper analyzes asymmetry in LoRA initialization for foundation models.
problem Asymmetry in LoRA initialization affects generalization of foundation models.
method Theoretical analysis of asymmetric LoRA with frozen random factors.
result Upper bound on sample complexity of $ ilde{\mathcal{O}}\left(\frac{\sqrt{r}}{\sqrt{N}}
ight)$ with high probability.
In this paper, we test a partially segmented ICAPM for two developed markets, two emerging markets and World market, using an asymmetric extension of the multivariate GARCH process of De Santis and Gerard (1997,1998). We find that this asymmetric process provides a significantly better fit of the data than a standard s…
A family of parsimonious shifted asymmetric Laplace mixture models is introduced. We extend the mixture of factor analyzers model to the shifted asymmetric Laplace distribution. Imposing constraints on the constitute parts of the resulting decomposed component scale matrices leads to a family of parsimonious models. An…
Paper studies asymmetric matrix sensing, proving gradient descent converges to low-rank solutions.
problem Reconstructing asymmetric low-rank matrices from linear measurements.
method Factorized gradient descent with coupling and regularization properties.
result Gradient descent from small random initialization converges to globally optimal and generalizing solutions.
We explore transformation groups of manifolds of the form M×Sn, where M is an asymmetric manifold, i.e. a manifold which does not admit any non-trivial action of a finite group. In particular, we prove that for n=2 there exists an infinite family of distinct non-diagonal effective circle actions on such pr…
Robust kNN classifier achieves optimal rates in noisy classification.
problem Classification with unknown asymmetric label noise.
method Robust kNN classifier with additional assumptions.
result Achieves minimax optimal rates in noisy classification.
Recent studies show that a negative shock in stock prices will generate more volatility than a positive shock of similar magnitude. The aim of this paper is to appraise the hypothesis under which the conditional mean and the conditional variance of stock returns are asymmetric functions of past information. We compare …
DeepLR constructs confidence intervals for neural networks with asymmetric expansions.
problem Uncertainty estimation for neural network predictions.
method Likelihood-ratio-based approach for constructing asymmetric confidence intervals.
result DeepLR offers asymmetric intervals expanding in regions with limited data.
A new method for CT-DCEGs simplifies inference for asymmetric processes.
problem Inference in asymmetric state space problems with continuous time evolution.
method An extension of CEG propagation for CT-DCEGs, employing junction tree inference.
result CT-DCEGs are preferred over DBNs and continuous time BNs for asymmetric processes.
Inspired by recent works of Zang Liu, Alan Weinstein and Ping Xu, we introduce the notions of CC algebroids and non asymmetric Courant algebroids and study these structures. It is shown that CC algebroids of rank greater than 3 are the same as Courant algebroids up to a constant factor, though the definition of CC alge…
Tensor factorization arises in many machine learning applications, such knowledge base modeling and parameter estimation in latent variable models. However, numerical methods for tensor factorization have not reached the level of maturity of matrix factorization methods. In this paper, we propose a new method for CP te…
Corporate bond factor research is flawed due to measurement errors and ex-post filtering.
problem Replication crisis in corporate bond factor research.
method Analysis of 108 signals across nine thematic clusters, correction of transaction prices and return filtering.
result Majority of previously documented factors do not produce statistically significant alphas after correction.
New asymmetric metric on Teichmüller space for surfaces.
problem Defining a new asymmetric weak metric on Teichmüller space.
method Introducing Teichmüller-Randers metric as an asymmetric deformation of the Teichmüller metric.
result Teichmüller geodesics become unique Teichmüller-Randers geodesics under certain conditions.
Study finds significant premium for low-beta stocks in firm-level idiosyncratic return distributions.
problem Understanding the role of common idiosyncratic quantile factors in asset pricing.
method Quantile factor analysis to extract common idiosyncratic quantile factors with asymmetric pricing effects.
result Significant premium for innovations to the lower-tail factor: high-beta stocks outperform low-beta stocks by around 7-8% per year.
Eigenvalue and eigenvector estimation improves with asymmetric data.
problem Estimating eigenvalues and eigenvectors from asymmetrically perturbed symmetric matrices.
method Eigenvalue and eigenvector analyses of asymmetrically perturbed low-rank matrices.
result The leading eigenvalue of the data matrix can be significantly more accurate than its singular value.
A new activation function improves credit scoring accuracy for imbalanced datasets.
problem Imbalanced datasets in credit scoring lead to underestimation of misclassification costs.
method Introduces ASIG, an asymmetric adjusted Sigmoid function.
result ASIG-embedded classifier outperforms traditional classifiers across various imbalance ratios.
Paper introduces AQ-LRMF for better noise modeling in low-rank matrix factorization.
problem Real data often have skew noise, which LRMF models assume symmetric.
method Develops AQ-LRMF using a mixture of asymmetric Laplace distributions and EM algorithm.
result AQ-LRMF outperforms state-of-the-art techniques in noise approximation and image analysis.
We show how bad and good volatility propagate through forex markets, i.e., we provide evidence for asymmetric volatility connectedness on forex markets. Using high-frequency, intra-day data of the most actively traded currencies over 2007 - 2015 we document the dominating asymmetries in spillovers that are due to bad r…
We study the implicit regularization imposed by gradient descent for learning multi-layer homogeneous functions including feed-forward fully connected and convolutional deep neural networks with linear, ReLU or Leaky ReLU activation. We rigorously prove that gradient flow (i.e. gradient descent with infinitesimal step …
We propose a new volatility model based on two stylized facts of the volatility in the stock market: clustering and leverage effect. We calibrate our model parameters, in the leading order, with 77 years Dow Jones Industrial Average data. We find in the short time scale (10 to 50 days) the future volatility is sensitiv…
Model disentangles font content and style.
problem Analyzing and reconstructing fonts.
method Variational inference and asymmetric transpose convolutional process.
result Model outperforms state-of-the-art models in font reconstruction.
Study of geometric analysis on asymmetric metric spaces, including heat flow and Sobolev spaces.
problem Analysis of geometric properties on asymmetric metric measure spaces.
method Introduction of upper gradients, q-Laplacian, and q-heat flow in asymmetric settings. result Extension of concepts from symmetric to asymmetric metric measure spaces.
A new asymmetric correntropy method improves robust adaptive filtering for asymmetric error distributions.
problem Inadequate handling of asymmetric error distributions in adaptive filtering.
method Proposes asymmetric correntropy using an asymmetric Gaussian kernel and develops a robust adaptive filtering algorithm.
result The proposed algorithm shows better steady-state convergence performance for asymmetric error distributions.
Matrix factorization is a popular approach to solving matrix estimation problems based on partial observations. Existing matrix factorization is based on least squares and aims to yield a low-rank matrix to interpret the conditional sample means given the observations. However, in many real applications with skewed and…
New metrics for Anosov representations defined from Thurston's asymmetric metrics.
problem Defining metrics for Anosov representations.
method Generalizing Thurston's asymmetric metric to Anosov representations.
result Provides a (possibly asymmetric) Finsler distance in some cases.
Generalizes Thurston's asymmetric metric to flat metrics.
problem Defining an asymmetric metric on flat metrics.
method Defined an asymmetric metric on the space of unit-area flat metrics.
result Discussed two different topologies from the asymmetry.
New method models asymmetric data with improved tail dependence.
problem Asymmetric data and tail dependence modeling.
method Generalized Skew-t Probabilistic Principal Component Analysis.
result Improved modeling of asymmetric data with tail effects.
This study examines asymmetric cross-correlations in cryptocurrency markets using fractal analysis.
problem Exploring asymmetric multifractal cross-correlations in cryptocurrency markets.
method Fractal analysis and MF-ADCCA method to investigate asymmetric volatility dynamics.
result Cross-correlations are stronger in downtrend markets than in uptrend markets for maturing BTC and ETH.
Theoretical justification for asymmetric actor-critic algorithms in reinforcement learning.
problem Lack of precise theoretical justification for asymmetric actor-critic algorithms in reinforcement learning.
method Adapting a finite-time convergence analysis to the asymmetric actor-critic setting with linear function approximators.
result A finite-time bound reveals that the asymmetric critic eliminates aliasing errors in the agent state.
This work presents deep asymmetric networks with a set of node-wise variant activation functions. The nodes' sensitivities are affected by activation function selections such that the nodes with smaller indices become increasingly more sensitive. As a result, features learned by the nodes are sorted by the node indices…
We consider the problem of designing locality sensitive hashes (LSH) for inner product similarity, and of the power of asymmetric hashes in this context. Shrivastava and Li argue that there is no symmetric LSH for the problem and propose an asymmetric LSH based on different mappings for query and database points. Howev…
New asymmetric kernel methods improve feature learning.
problem Improving feature learning with asymmetric kernels.
method Coupled covariance eigenproblem and Nyström method.
result Empirical evaluations show benefits of KSVD.
A new algorithm improves model-based reinforcement learning by guiding latent representations.
problem Improving model-based reinforcement learning through additional supervision.
method Proposed a novel asymmetric representation learning objective using latent guidance.
result Significantly improved performance over previous asymmetric approaches.
The article confirms two quasi-alternating surgeries for 9 asymmetric L-space knots.
problem Understanding quasi-alternating surgeries on asymmetric L-space knots.
method Using the Montesinos trick to confirm known surgeries.
result Confirmation of two quasi-alternating surgeries for each of 9 asymmetric L-space knots.
The paper improves asymmetric causality tests by addressing inefficiencies and statistical significance issues.
problem Inefficiencies and statistical significance issues in asymmetric causality tests.
method Improved asymmetric causality tests via partial cumulative sums for positive and negative components, explicitly testing differences between causal parameters.
result Efficiently tested hypotheses on asymmetric causal interaction between financial markets.
Asymmetric expansion preserves convexity in hyperbolic geometry.
problem Maintaining convexity in hyperbolic geometry under asymmetric expansions.
method Generalizing earlier results on radial expansion to asymmetric expansion.
result Asymmetric expansion of hyperbolic convex sets remains convex.
Loss-calibrated EP improves Bayesian decision-making by focusing on utility-sensitive posterior approximations.
problem Bayesian decision-making under asymmetric utility functions.
method Loss-calibrated expectation propagation (Loss-EP) that tilts the posterior towards higher utility decisions.
result Loss-EP can capture useful information for decision-making under asymmetric penalties.
We propose Deep Asymmetric Multitask Feature Learning (Deep-AMTFL) which can learn deep representations shared across multiple tasks while effectively preventing negative transfer that may happen in the feature sharing process. Specifically, we introduce an asymmetric autoencoder term that allows reliable predictors fo…
Study of classification in asymmetric quasi-metric spaces.
problem Classification in asymmetric quasi-metric spaces.
method Sample compression and nearest neighbor algorithm.
result Algorithm has favorable statistical properties.
In this paper we show how the study of asymmetric R&D alliances, that are those between young and small firms and large and MNEs firms for knowledge exploration and/or exploitation, requires the adoption of a coopetitive framework which consider both collaboration and competition. We draw upon the literature on asymmet…
AACC improves RL performance in changing environments.
problem Deterioration of RL performance in real-world tasks.
method Formalizes CMDPs, proposes AACC for contextual RL.
result AACC outperforms baselines in various simulated environments.