Optimization

Homogenized SGD reveals heavy-tailed neural network parameters, offering quantifiable bounds on tail-index and showcasing the interplay between optimization hyperparameters and model generalization.

STREAM-UGS: a novel semi-streaming algorithm for efficient graphlet sampling, enabling fast analysis of massive graphs with limited memory.

Sign-based optimization gets a speed boost! This paper introduces new algorithms that significantly accelerate convergence in distributed optimization by cleverly using variance reduction and enhanced…

Efficient algorithm achieves near-perfect graph matching in correlated stochastic block models, resolving a key open problem and enabling improved community detection.

Heat Diffusion Optimization (HeO) framework efficiently solves combinatorial optimization problems by enabling information propagation through heat diffusion, outperforming existing methods.

New efficient algorithms minimize regret in extensive-form games by cleverly using low-degree swap deviations and a relaxed fixed-point concept, improving correlated equilibrium computation.

First-price auction efficiency in autobidding plummets to 45.7% with mixed bidders, but machine-learned advice restores optimality.

This research introduces novel algorithms to dynamically price third-party platform service fees under strategic buyer behavior, achieving optimal revenue with a theoretically proven regret bound.

Dual Lagrangian Learning (DLL) revolutionizes conic optimization by leveraging machine learning to efficiently learn high-quality dual-feasible solutions, achieving 1000x speedups over traditional sol…

DRAGO: A novel primal-dual algorithm delivers faster, state-of-the-art convergence for distributionally robust optimization.

DISTRICTNET: A novel decision-aware learning approach drastically cuts geographical districting costs by integrating combinatorial optimization and graph neural networks.

This research introduces distributionally robust performative prediction, offering a new solution concept (DRPO) that minimizes performative risk even with misspecified distribution maps, ensuring rob…

This paper provides the first statistical learning guarantees for distributional regression using CRPS, offering concentration bounds for model fitting, selection, and convex aggregation, applicable t…

Generative models often produce invalid outputs; this work shows that ensuring validity is easier than expected when using log-loss and carefully selecting model classes and data distributions.

Researchers developed a novel sparse sketching method for distributed least squares regression, achieving near-unbiased estimates with optimal space and time complexity.

Researchers surpass the 1/e barrier in submodular maximization with novel combinatorial algorithms!

New sub-optimality bounds for gradient descent leverage directional smoothness, a localized gradient variation measure, achieving tighter convergence guarantees and adapting to optimization paths.

D-PBEMO: A novel framework for preference-based multi-objective optimization using clustering-based stochastic dueling bandits to directly leverage human feedback, improving efficiency and managing co…

Diffeomorphic interpolation boosts topological optimization by transforming sparse gradients into smooth vector fields, enabling efficient large-scale point cloud optimization and black-box autoencode…

Stochastic gradient descent’s derivatives, crucial for hyperparameter optimization, converge to the solution mapping derivative; rates depend on step size, exhibiting O(log(k)²/k) convergence with van…