The study connects minimal and maximal surfaces in 3D and 3-L space.
problem Describing correspondences between minimal and maximal surfaces in different spaces.
method Weierstrass representation and asymptotic analysis.
result Established criteria for singularity types and moduli spaces.
Maximizes probability of completing investment schedules with optimal portfolio weights.
problem Optimizing probability of completing investment schedules with optimal portfolio weights.
method Computing maximum probability and optimal portfolio weight functions for various rebalancing schedules.
result Noticeable improvements in probability to complete schedules with optimal portfolio weights.
The study examines the independence of GKM manifolds and symmetric spaces.
problem Understanding the independence of isotropy weights in GKM manifolds.
method Using weighted graphs and properties of symmetric spaces, the study analyzes the independence of isotropy weights.
result The maximal independence of G/H is 2, 3, or n=dimT, corresponding to symmetric spaces of rank >2. Investment strategy optimizes risk using a specific risk measure.
problem Optimizing investment with risk controlled by a weighted entropic risk measure.
method Investigation of expected utility maximization and risk minimization problems with solutions provided iteratively.
result Explicit characterization of solutions to optimization problems.
Estimates Gaussian mixtures from weighted samples efficiently.
problem Estimating Gaussian mixtures from weighted samples with correct weight treatment.
method Density interpretation and expectation-maximization method considering weights.
result Correctly estimates Gaussian mixtures with weighted samples.
We tackle robust influence maximization in social networks with hyperparametric edge probabilities.
problem Maximizing worst-case influence in social networks with hyperparametric edge probabilities.
method Proposed a model with NP-hard proper robust optimization, using sampling and multiplicative weight updates.
result Empirically validated method outperforms state-of-the-art robust influence maximization techniques.
Over-parameterization helps EM avoid local optima in Gaussian mixture models.
problem Finding global optima in non-convex optimization problems with EM.
method Using an over-parameterized model with unknown mixing weights.
result Over-parameterization improves EM's ability to find the global maximizer of the log-likelihood.
Identifies filtration in Lagrangian fibrations to monodromy weight filtration in degenerations.
problem Understanding the relationship between Lagrangian fibrations and degenerations of hyper-Kähler manifolds.
method Identifies and compares perverse filtration with monodromy weight filtration.
result Identifies the perverse filtration of a Lagrangian fibration with the monodromy weight filtration of a degeneration.
The paper proves Calabi-Bernstein type results for minimal and maximal surfaces in 3D and 3D-L spacetime.
problem Characterizing minimal and maximal surfaces in 3D and 3D-L spacetime.
method Analyzing surfaces with specific properties and using geometric and functional methods.
result Calabi-Bernstein type results for critical points of a weighted area functional in R3 and L3. The paper predicts survival functions using random survival trees and concordance maximization.
problem Predicting conditional survival functions in right-censored data.
method The approach combines regression strategies with random survival trees and maximizes concordance.
result The proposed weighted predictor outperforms the usual survival cobra in terms of concordance.
RWR converges to global optimum in certain settings.
problem Proving convergence of RWR to optimal policy.
method Iterative learning with return-weighted log-likelihood.
result RWR converges to global optimum under certain conditions.
A new HRL method learns hierarchical policies using mutual information maximization.
problem Learning hierarchical policies in reinforcement learning for structured tasks.
method Mutual information maximization for latent variable learning, advantage-weighted importance sampling for option policies, deterministic policy gradient for optimization.
result Enhanced performance in continuous control tasks through learned hierarchical policies.
The densest k-clique problem is solved via semidefinite programming for weighted graphs.
problem Clustering dense weighted graphs into disjoint subgraphs maximizing density.
method Solving a semidefinite relaxation to recover clusters with high probability.
result Clusters can be recovered from the solution of a semidefinite relaxation with high probability.
We introduce a minorization-maximization approach to optimizing common measures of discovery significance in high energy physics. The approach alternates between solving a weighted binary classification problem and updating class weights in a simple, closed-form manner. Moreover, an argument based on convex duality sho…
Optimizes treatment duration to maximize quality-adjusted lifetime.
problem Balancing risks and benefits in clinical decision making.
method Proposes a weighted estimating equation to adjust for confounding and informative censoring, and a nonparametric estimator for mean counterfactual quality-adjusted lifetime.
result Shows the optimal time for percutaneous endoscopic gastrostomy insertion in ALS patients.
Study on learning rates in neural networks of varying depth.
problem Dependence of maximal update learning rate on network depth.
method Analysis of random fully connected ReLU networks with mean-field weight initialization.
result Maximal update learning rate scales like L−3/2 with network depth. A new weighted FDA method improves face recognition accuracy.
problem Equal treatment of all class pairs in FDA leads to suboptimal performance.
method Cosine-weighted and automatically weighted FDA methods are proposed.
result Improved face recognition accuracy through weighted FDA.
We consider a geometrically finite discrete group of conformal transformations of the sphere. Further we consider distributions which are supported on the limit set and are invariant with conformal weight. We estimate their regularity in terms of the conformal weight, the Hausdorff dimension of the limit set, and the m…
Algorithm uses RBM to solve matching problems on weighted graphs.
problem Perfect matching problem on bipartite weighted graphs.
method Iterative RBM algorithm to maximize energy function and assignment.
result Algorithm successfully solves real-world matching problems.
Reinterprets IWAE as optimizing a tighter variational bound.
problem Improving variational lower bounds in autoencoders.
method Derives and visualizes a tighter lower bound using a more complex distribution.
result IWAE optimizes the standard variational lower bound with a complex distribution.
The paper computes characteristic classes for Lie group representations.
problem Computing characteristic classes for Lie group representations.
method The paper outlines a procedure to compute characteristic classes of irreducible representations of Lie groups, expressing them as polynomial functions in the highest weight.
result The paper expresses characteristic classes of Lie group representations as polynomial functions in the highest weight.
Improves random survival forest model by weighted averaging.
problem Improving the performance of random survival forest.
method Modifies random forest by weighted averaging of trees, optimizing weights via quadratic optimization to maximize Harrell's C-index.
result The weighted random survival forest outperforms the original model in numerical examples.
A determinantal point process (DPP) is a probabilistic model of set diversity compactly parameterized by a positive semi-definite kernel matrix. To fit a DPP to a given task, we would like to learn the entries of its kernel matrix by maximizing the log-likelihood of the available data. However, log-likelihood is non-co…
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.
Optimal option portfolios under Sharpe Ratio maximization with skew-elliptical t-distributed returns
problem Optimal option portfolios under Sharpe Ratio maximization
method Formulation for explicit portfolio weights
result Different optimal portfolios for Sharpe Ratio and return-to-Value-at-Risk (VaR) ratio
Paper uses SC to estimate hidden interference for WSRM.
problem Maximizing sum-rate with hidden interfering sources.
method Synthetic control (SC) for estimating counterfactual interference in WMMSE.
result SC-WMMSE outperforms original WMMSE in convergence and objective.
Paper develops a framework to optimize neural networks using weighted metrics.
problem Discrepancy between maximizing weighted classification scores and minimizing loss function.
method Formalizes weighted classification metrics and constructs corresponding losses.
result Framework includes well-established approaches like cost-sensitive learning and weighted cross entropy.
QEM uses parallel importance weighting for fast approximate Bayesian inference.
problem Bayesian inference challenges in large models with many observations and latent variables.
method Expectation Maximization (EM) with massively parallel importance weighting.
result QEM is faster and more scalable than RWS and VI.
This work improves policy optimization by maximizing entropy of state distribution, leading to better exploration.
problem Lack of exploration in state space when maximizing policy entropy.
method Proposes maximizing the entropy of a lower bound approximation to the state weighting distribution, based on latent space representation.
result Entropy regularization based on marginal state distribution achieves superior state space coverage and better performance in various domains.
New theorem splits spaces with maximal variance of 1-Lipschitz functions.
problem Understanding the structure of metric measure spaces.
method Analyzing isoperimetric profiles and variance of 1-Lipschitz functions.
result Spaces with maximal variance are foliated by minimal geodesics.
In this paper we study the convergence behavior of grafting rays to the Thurston boundary of Teichmuller space. When the grafting is done along a weighted system of simple closed curves or along a maximal uniquely ergodic lamination this behavior is the same as for Teichmuller geodesics and lines of minima. We also sho…
Crowdsourcing with prospect theory handles spammers in binary classification.
problem Binary classification with spammers and reject option.
method Prospect theory for worker behavior, weighted majority voting for decision fusion.
result Asymptotic system performance and correct classification probability derived.
A new approach optimizes weights in DLP for better risk-adjusted performance.
problem Optimizing time-varying weights in Double Linear Policy (DLP) for better risk-adjusted performance.
method Stochastic Model Predictive Control (SMPC) framework to maximize risk-adjusted returns while enforcing constraints.
result Empirical results show improved risk-adjusted performance and drawdown control.
The paper studies highest weight representations of Lie superalgebras and their geometric realizations.
problem Understanding highest weight representations of Lie superalgebras and their geometric realizations.
method Analyzes representations of Lie superalgebras and their geometric realizations on Hermitian superspaces.
result Discovers geometric realizations of highest weight representations of Lie superalgebras.
New algorithm improves causal effect estimation for continuous treatments.
problem Observational causal inference with continuous treatments.
method End-to-end entropy balancing for maximizing causal inference accuracy.
result Our algorithm estimates causal effect more accurately than baseline.
Stability theory for Lie group actions on spaces.
problem Characterizing stability of points in topological spaces under Lie group actions.
method Abstract setting for actions of non-compact real reductive Lie groups, introducing maximal weight function.
result Characterization of stability, semi-stability, and polystability using numerical criteria.
Networks are a convenient way to represent complex systems of interacting entities. Many networks contain "communities" of nodes that are more densely connected to each other than to nodes in the rest of the network. In this paper, we investigate the detection of communities in temporal networks represented as multilay…
Extends elliptic operator regularity to maximally hypoelliptic operators.
problem Maximally hypoelliptic differential operators and their regularity.
method Define a principal symbol for arbitrary differential operators involving vector fields and their commutators.
result Proves the invertibility of the principal symbol is equivalent to maximally hypoellipticity, answering a conjecture.
A family of parsimonious Gaussian cluster-weighted models is presented. This family concerns a multivariate extension to cluster-weighted modelling that can account for correlations between multivariate responses. Parsimony is attained by constraining parts of an eigen-decomposition imposed on the component covariance …
We study geometry of complete Riemannian manifolds endowed with a weighted measure, where the weight function is of quadratic growth. Assuming the associated Bakry-Emery curvature is bounded from below, we derive a new Laplacian comparison theorem and establish various sharp volume upper and lower bounds. We also obtai…
New method for optimizing neural networks with quantized weights and activations.
problem Improving resource efficiency of deep neural networks.
method Mean-field theory applied to quantized activation networks.
result Closed-form equation for maximal trainable depth, showing Lmax∝N1.82. A new modularity density measure improves community detection in heterogeneous networks.
problem Detecting meaningful communities in heterogeneous networks.
method Formulated a novel metric, modularity density, for undirected, weighted networks.
result Maximization of modularity density is free from bias and better at detecting weakly-separated communities.
New collapsing mechanism for G2-manifolds discovered.
problem Understanding collapsing behavior of G2-manifolds.
method Adiabatic description involving weighted maximal submanifold equation; formal power series solutions.
result Existence of formal power series solutions and heuristic discussion of compactification.
Discrete maximal surfaces identified from s-embeddings.
problem Understanding the conformal invariance of the Ising model.
method Introduced a special class of isothermic s-embeddings that correspond to discrete S-maximal surfaces.
result Each S-maximal surface comes with a 1-parameter family of associated surfaces that are isometric.
The paper defines function spaces on manifolds with bounded or singular geometries.
problem Defining function spaces on manifolds with various geometries.
method Introduces and analyzes Sobolev, Besov, and Bessel potential spaces on uniformly regular and singular Riemannian manifolds.
result Demonstrates maximal regularity for a linear parabolic problem on singular manifolds.
The paper classifies Fano distributions on specific Fano manifolds.
problem Investigating Fano distributions on Fano manifolds.
method Classification of Fano distributions on various Fano manifolds.
result Classification of codimension one del Pezzo distributions on Fano manifolds with Picard number one.
The paper integrates behavioral distortions into portfolio optimization using implied probability weighting functions.
problem Behavioral distortions in probability weighting affect portfolio optimization under different return distributions.
method Developed a unified framework to extract probability weighting functions from optimal portfolios modeled under Gaussian and NIG distributions.
result Increasing tail fatness amplifies behavioral distortions, and shifts in risk-free rates alter the curvature of these distortions.
Data clustering has received a lot of attention and numerous methods, algorithms and software packages are available. Among these techniques, parametric finite-mixture models play a central role due to their interesting mathematical properties and to the existence of maximum-likelihood estimators based on expectation-m…