Deep-learning improves 6x6-mm OCTA angiograms by reducing noise and artifacts.
problem Reduced scan quality in 6x6-mm OCTA angiograms due to undersampling.
method Deep-learning-based high-resolution angiogram reconstruction network (HARNet) trained on 3x3-mm and 6x6-mm angiogram data.
result Reconstructed 6x6-mm angiograms have lower noise and better vascular connectivity.
MM algorithms simplify solving machine learning and statistical problems.
problem Optimization problems in machine learning and statistics.
method Majorization-minimization framework applied to specific examples.
result Derivation and demonstration of MM algorithms for Gaussian mixtures, multinomial logistic, and SVM.
Non-convex optimization is ubiquitous in machine learning. Majorization-Minimization (MM) is a powerful iterative procedure for optimizing non-convex functions that works by optimizing a sequence of bounds on the function. In MM, the bound at each iteration is required to \emph{touch} the objective function at the opti…
Paper accelerates MM algorithm for faster inference of ranking scores from comparison data.
problem Inference of Bradley-Terry model parameters from comparison data.
method Developed and analyzed MM algorithm for maximum likelihood and Bayesian estimation, proposed an accelerated version.
result Accelerated MM algorithm achieves faster convergence rates compared to classical MM algorithm.
A novel MM algorithm optimizes DCOV for SDR and SVS.
problem Dimension reduction and variable selection in nonparametric settings.
method Formulated as a DC program, MM algorithm solves quadratic subproblems on the Stiefel manifold.
result Improves computation efficiency and robustness across various settings.
The MM algorithm improves robust penalized estimation for outlier-contaminated data.
problem Outliers in data affect the reliability of penalized estimation.
method Innovative MM algorithm for both convex and nonconvex loss functions.
result Established convergence theory for MM algorithm with various loss functions.
A new method accelerates deep neural network training using minimal margin score.
problem Training deep neural networks is computationally expensive.
method Introduces minimal margin score (MMS) for selecting samples.
result Significant acceleration in training deep neural networks.
Paper explores MM strategies that can refuse to quote or provide single-sided quotes.
problem Overcoming risks in market making due to changing market conditions.
method Adversarial reinforcement learning with new MM agent designs.
result Refusal to quote or providing single-sided quotes can improve MM performance.
Unified approach for federated learning using MM optimization.
problem Scaling stochastic optimization to federated learning.
method Unified Majorize-Minimize (MM) framework for stochastic optimization, extended to federated learning.
result Unified algorithm \QSMM\ for federated learning that aggregates surrogate majorizing functions.
NS-GAN mode collapse due to sample weighting inversion, solved with MM-nsat.
problem Mode collapse in GANs due to sample weighting inversion.
method Preserves MM-GAN sample weighting while avoiding saturation by rescaling gradients.
result MM-nsat improves mode coverage, stability, and FID on MNIST and CIFAR-10.
Proposes MM-KTD for efficient RL learning with reduced sample size.
problem High sensitivity to parameter selection and overfitting in DNN-based RL methods.
method Adapts Kalman filter parameters using observed states and rewards, enhances sampling efficiency through active learning.
result Significantly reduced number of samples needed to learn optimal policy.
DGMM improves Gaussian mixture modeling efficiency and stability.
problem Efficiently estimating Gaussian mixtures in high dimensions.
method Diagonally-weighted generalized method of moments (DGMM).
result DGMM achieves smaller estimation errors with shorter runtime.
The purpose of this paper is the study of the roots in the mapping class groups. Let Σ be a compact oriented surface, possibly with boundary, let $\PP$ be a finite set of punctures in the interior of Σ, and let $\MM (Σ, \PP)$ denote the mapping class group of $(Σ, \PP)$. We prove that, if Σ is of genus 0, then ea…
Let $(\MM ,{\tilde g})$ be an N-dimensional smooth compact Riemannian manifold. We consider the singularly perturbed Allen-Cahn equation $$ ε^2Δ_{ {\tilde g}} {u}\,+\, (1 - {u}^2)u \,=\,0\quad \mbox{in } \MM, $$ where ε is a small parameter. Let $\KK\subset \MM$ be an (N−1)-dimensional smooth minimal submanifold …
Unified algorithm for tensor decomposition supports multiple loss functions and models.
problem Efficient tensor decomposition for various models and loss functions.
method Hierarchical combination of ADMM and MM for optimization.
result Wide-range applications can be solved by the proposed algorithm.
The paper classifies Poincaré complexes as topological manifolds.
problem Classifying Poincaré complexes as topological manifolds.
method Using spherical fibrations and CW-complexes, the paper proves stability and homotopy equivalence.
result A sufficient condition for Poincaré complexes to be homotopy types of topological manifolds.
This paper proposes MM-DAGs for analyzing traffic congestion, learning multiple DAGs jointly.
problem Analyzing multi-modal traffic data with overlapping and distinct variables.
method Developed MM-DAGs for multi-task, multi-modal DAG learning, using multi-modal regression and CD measure.
result Proved the effectiveness of MM-DAGs in traffic congestion analysis.
MM-DREX adapts LLM experts for financial trading via dynamic routing.
problem Challenges of non-stationary financial markets and static expert designs.
method MM-DREX uses a VLM-powered dynamic router to allocate expert weights and designs heterogeneous trading experts.
result Significantly outperforms 15 baselines across key metrics.
New IRLS algorithms for SVM fitting via MM approach.
problem Fitting support vector machines (SVMs) via quadratic programming.
method Majorization--Minimization (MM) paradigm for iteratively-reweighted least-squares (IRLS) algorithms.
result IRLS algorithms for SVM risk minimization problems with various losses and penalties.
A new method trains physics-constrained neural networks more efficiently.
problem Training machine learning tools with limited data and physical constraints.
method Dual-Dimer method for searching saddle points in nonconvex-nonconcave functions.
result The Dual-Dimer method improves training efficiency and convergence speed.
New methods for parameter estimation in mechanistic models using data-consistent inversion.
problem Parameter estimation bias in Bayesian analysis for mechanistic models.
method Data-consistent inversion methods based on rejection sampling, MCMC, GANs, and constrained optimization.
result Improved parameter estimation without bias from uninformative priors.
Paper proposes an efficient MM method for optimizing mean-reverting portfolios in finance.
problem Optimizing mean-reverting portfolios in financial markets considering mean-reversion strength, variance, and investment constraints.
method Majorization-Minimization (MM) method.
result The proposed method significantly outperforms other methods in financial market simulations.
Proposes a new method for estimating sparse precision matrices in GMRF-MM models.
problem Difficulty in learning GMMs with large parameters and limited data.
method Restricts GMM to GMRF-MM, proposes efficient optimization for sparse precision matrices, and debiases the estimates.
result Debiasing approach outperforms GLASSO in single-GMRF and GMRF-MM cases.
The article explains how to use Mixture-of-Experts models for complex data.
problem Modeling complex data generating processes (DGPs).
method Constructing Mixture-of-Experts (MoE) models using maximum quasi-likelihood (MQL) estimators and blockwise-MM algorithms.
result MQL estimators are consistent and asymptotically normal under certain conditions.
Introduces screening rules for non-convex Lasso problems.
problem Efficiently solving non-convex Lasso problems with theoretical guarantees.
method Iterative majorization-minimization strategy with screening rule.
result Significant computational gain compared to classical methods.
The measure concentration property of an mm-space X is roughly described as that any 1-Lipschitz map on X to a metric space Y is almost close to a constant map. The target space Y is called the screen. The case of Y=R is widely studied in many literature (see \cite{gromov}, \cite{ledoux}, \cite{mil2}…
Proposes a robust method for estimating sparse VECM models.
problem Outliers and heavy-tailed data in traditional VECM models.
method Robust estimation using Cauchy distribution and sparse cointegration.
result Efficient algorithm for nonconvex problem solving.
Study uses logistic regression and association rules to identify early symptoms of malignant mesothelioma.
problem Difficult diagnosis of malignant mesothelioma leading to late-stage detection and poor patient survival.
method Implemented logistic regression and developed association rules to identify early symptoms.
result Categorical logistic regression improved training accuracy from 72.30% to 81.40%.
Let Ef be the energy of some knot τ for any f from certain class of functions. The problem is to find knots with extremal values of energy. We discuss the notion of the locally perturbed knot. The knot circle minimizes some energies Ef and maximizes some others. So, is there any energy such that the circle ne…
IMM uses imitation learning and predictive representation learning to improve market making strategies.
problem Challenges in training RL agents for multi-price level market making strategies.
method IMM combines RL and imitation learning, introducing effective state and action representations and a representation learning unit.
result IMM outperforms existing RL-based market making strategies in financial criteria.
Bayesian optimization for function-valued responses, addressing worst case deviations.
problem Optimizing expensive functions with functional responses, focusing on worst case performance.
method Min-Max Functional Bayesian Optimization (MM-FBO) using Gaussian process surrogates and functional principal component analysis.
result MM-FBO consistently outperforms existing methods in synthetic and real-world applications.
Models interactions among market participants in a financial asset's order book.
problem Understanding dynamic interactions among market participants in a financial asset's order book.
method Derives variational partial differential equations for MM and HFT strategies, and explains almost optimal control.
result Illustrates interactions between market participants through simulations of an order book.
Proposes MM-DUST for efficient generalized lasso solution paths.
problem Efficiently solve generalized lasso problems in large-scale and non-linear models.
method Majorization-minimization dual stagewise algorithm incorporating quadratic majorizers and stagewise learning.
result Established the uniform convergence of approximated solution paths.
Develops RL for optimal market-making in non-Markov processes.
problem Optimal market-making in non-Markov price processes.
method Deep reinforcement learning with Soft Actor-Critic (SAC) algorithm.
result Optimal strategy for market-making in semi-Markov and Hawkes Jump-Diffusion dynamics.
New algorithm improves on EM for streaming data, outperforming existing methods.
problem Processing high-volume, streaming data efficiently.
method Incremental stochastic Majorization-Minimization (MM) algorithm.
result The algorithm converges to a stationary point with vanishing gradient.
This paper uses deep RL to optimize market quotes from LOB data.
problem Optimizing quotes for market making from complex LOB data.
method Attn-LOB neural network with convolutional filters and attention mechanism for feature extraction; hybrid reward function for continuous action space.
result The RL agent outperforms traditional methods in market making tasks.
Study confirms the Epps effect using different volume time averaging methods for JSE stocks.
problem Demonstrating the Epps effect in stock market data using various aggregation methods.
method Used two non-parametric covariance estimators (Malliavin and Mancino, Hayashi and Yoshida) and two volume time averaging methods (asset intrinsic and synchronised volume time).
result MM estimator more representative of trade time reality, confirming market phenomenology.
mm-Pose detects human skeletons in real-time using mmWave radar and CNNs.
problem Real-time human skeletal posture estimation in various scenarios.
method mmWave radar, radar-to-image representation, forked CNN architecture.
result Accurate predictions for human skeletal joints in 3D space.
New algorithm speeds up NMF with β-divergence.
problem Efficiently factorize nonnegative matrices with β-divergence. method Joint majorization-minimization with multiplicative updates.
result Significant reduction in computation time for NMF.
A new method estimates expectations from subtractive mixture models without sampling.
problem Estimating expectations from multimodal distributions using SMMs.
method Difference representation of SMMs to create unbiased IS estimator (ΔextEx). result Demonstrates that ΔextEx can achieve comparable estimation quality to auto-regressive sampling but is faster. RobustTrend filters time series trends robustly against outliers and abrupt changes.
problem Extracting accurate trend signals from noisy, potentially abrupt-changing time series.
method Uses Huber loss for outlier suppression and a combination of first and second order differences for regularization.
result Our algorithm outperforms existing methods in synthetic and real-world datasets.
New method uses subtractive mixture models for approximate inference.
problem How to effectively use subtractive mixture models for approximate inference.
method Design expectation estimators for IS and learning schemes for VI with SMMs.
result Empirical evaluation shows SMMs can approximate distributions effectively.
New approach for distributed learning of Gaussian mixtures.
problem Large datasets distributed across different centers.
method Split-and-conquer approach with MM algorithm.
result New estimator is consistent and retains root-n consistency.
Paper introduces new copulas from shock models, improving on maxmin copulas.
problem Improving on maxmin copulas for better characteristics.
method Developed RMM copulas with dependent endogenous shocks and proved convergence of iteration procedures.
result RMM copulas exhibit better characteristics than maxmin copulas, including convergence properties.
Kernel k-Means algorithm improves clustering of non-linear data.
problem Non-convexity of kernel k-Means objective function leads to local minima.
method Generalizes MM approach to solve non-convex problem in kernel and multi-kernel settings.
result Establishes strong consistency guarantees for Kernel Power k-Means.
Estimates deformation of elastic objects using neural networks from few observations.
problem Estimating deformation of elastic objects using limited data.
method Learning approach with a neural network to estimate entire deformation from few observations.
result Average estimation error of 0.041 mm for human liver model under significant deformation.
This paper rates robustness of multi-modal time-series forecasting models.
problem Robustness of AI systems in time-series forecasting is crucial for stakeholders.
method Causal analysis to assess robustness of MM-TSFM models.
result Multi-modal forecasting models are more robust than numeric models.
New method clusters time-evolving networks using exponential-family models.
problem Detecting community structure in time-evolving networks.
method Model-based clustering using temporal Exponential-Family Random Graph Models (ERGMs).
result Efficient variational EM algorithm for large-scale networks.