Cover Image

It's All in the Mix: Wasserstein Classification and Regression with Mixed Features

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Belbasi, Reza, Selvi, Aras, Wiesemann, Wolfram
Format:	Preprint
Published:	2023
Subjects:	Optimization and Control Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Differential Privacy via Distributionally Robust Optimization
by: Selvi, Aras, et al.
Published: (2023)

Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls
by: Selvi, Aras, et al.
Published: (2024)

Distributionally Robust Optimization
by: Kuhn, Daniel, et al.
Published: (2024)

Efficient Algorithms for Robust Markov Decision Processes with $s$-Rectangular Ambiguity Sets
by: Ho, Chin Pang, et al.
Published: (2026)

Mixed-feature Logistic Regression Robust to Distribution Shifts
by: Sun, Qingshi, et al.
Published: (2025)

Mind the Gap: Mixtures of Gaussians in Approximate Differential Privacy
by: Liu, Huikang, et al.
Published: (2026)

Fréchet Regression on the Bures-Wasserstein Manifold
by: Nguyen, Duc Toan, et al.
Published: (2026)

Fair Generalized Linear Mixed Models
by: Burgard, Jan Pablo, et al.
Published: (2024)

Generalized Wasserstein Flow Matching: Transport Plans, Everywhere, All at Once
by: Piening, Moritz, et al.
Published: (2026)

Mixed-Integer Programming for Change-point Detection
by: Narula, Apoorva, et al.
Published: (2026)

Provable Mixed-Noise Learning with Flow-Matching
by: Hagemann, Paul, et al.
Published: (2025)

Flowing Datasets with Wasserstein over Wasserstein Gradient Flows
by: Bonet, Clément, et al.
Published: (2025)

Tight Mixed-Integer Optimization Formulations for Prescriptive Trees
by: Biggs, Max, et al.
Published: (2023)

Hybrid Reinforcement Learning Framework for Mixed-Variable Problems
by: Zhai, Haoyan, et al.
Published: (2024)

Conformal Mixed-Integer Constraint Learning with Feasibility Guarantees
by: Ovalle, Daniel, et al.
Published: (2025)

Responsible Machine Learning via Mixed-Integer Optimization
by: Justin, Nathan, et al.
Published: (2025)

Improving DAPO from a Mixed-Policy Perspective
by: Tan, Hongze, et al.
Published: (2025)

A Distance Metric for Mixed Integer Programming Instances
by: Maudet, Gwen, et al.
Published: (2025)

Predicting Accurate Lagrangian Multipliers for Mixed Integer Linear Programs
by: Demelas, Francesco, et al.
Published: (2023)

Learning to Price Bundles: A GCN Approach for Mixed Bundling
by: Ding, Liangyu, et al.
Published: (2025)

ML-Guided Primal Heuristics for Mixed Binary Quadratic Programs
by: Huang, Weimin, et al.
Published: (2026)

Generating Poisoning Attacks against Ridge Regression Models with Categorical Features
by: Guedes-Ayala, Monse, et al.
Published: (2025)

Learning to Optimize for Mixed-Integer Non-linear Programming with Feasibility Guarantees
by: Tang, Bo, et al.
Published: (2024)

Speeding Up Mixed-Integer Programming Solvers with Sparse Learning for Branching
by: Bayramoğlu, Selin, et al.
Published: (2026)

Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs
by: Chen, Ziang, et al.
Published: (2024)

Scalable Mixed-Integer Optimization with Neural Constraints via Dual Decomposition
by: Zeng, Shuli, et al.
Published: (2025)

Machine Learning Augmented Branch and Bound for Mixed Integer Linear Programming
by: Scavuzzo, Lara, et al.
Published: (2024)

Global and Preference-based Optimization with Mixed Variables using Piecewise Affine Surrogates
by: Zhu, Mengjia, et al.
Published: (2023)

Data-driven Mixed Integer Optimization through Probabilistic Multi-variable Branching
by: Chen, Yanguang, et al.
Published: (2023)

The Mixing method: low-rank coordinate descent for semidefinite programming with diagonal constraints
by: Wang, Po-Wei, et al.
Published: (2017)

Towards An Unsupervised Learning Scheme for Efficiently Solving Parameterized Mixed-Integer Programs
by: Qu, Shiyuan, et al.
Published: (2024)

Semidefinite Relaxations of the Gromov-Wasserstein Distance
by: Chen, Junyu, et al.
Published: (2023)

An MILP-Based Solution Scheme for Factored and Robust Factored Markov Decision Processes
by: Liu, Huikang, et al.
Published: (2024)

Wasserstein Distributionally Robust Regret Optimization
by: Fiechtner, Lukas-Benedikt, et al.
Published: (2025)

Wasserstein Distributionally Robust Online Learning
by: Chen, Guixian, et al.
Published: (2026)

Linear Partial Gromov-Wasserstein Embedding
by: Bai, Yikun, et al.
Published: (2024)

A Double Inertial Forward-Backward Splitting Algorithm With Applications to Regression and Classification Problems
by: Işik, İrfan, et al.
Published: (2025)

Machine Learning Algorithms for Improving Exact Classical Solvers in Mixed Integer Continuous Optimization
by: Kimiaei, Morteza, et al.
Published: (2025)

A note on convergence of Wasserstein policy optimization
by: Šiška, David, et al.
Published: (2026)

Sliced Inner Product Gromov-Wasserstein Distances
by: Gong, Xiaoyun, et al.
Published: (2026)