Cover Image

Learning and Decision-Making with Data: Optimal Formulations and Phase Transitions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bennouna, Amine, Van Parys, Bart P. G.
Format:	Preprint
Published:	2021
Subjects:	Machine Learning Optimization and Control Statistics Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

From Distributional Robustness to Robust Statistics: A Confidence Sets Perspective
by: Chan, Gabriel, et al.
Published: (2024)

Stochastic Optimization with Optimal Importance Sampling
by: Aolaritei, Liviu, et al.
Published: (2025)

Efficient Data-Driven Optimization with Noisy Data
by: Van Parys, Bart P. G.
Published: (2021)

What Data Enables Optimal Decisions? An Exact Characterization for Linear Optimization
by: Bennouna, Omar, et al.
Published: (2025)

Holistic Robust Data-Driven Decisions
by: Bennouna, Amine, et al.
Published: (2022)

Learning an Optimal Assortment Policy under Observational Data
by: Han, Yuxuan, et al.
Published: (2025)

4+3 Phases of Compute-Optimal Neural Scaling Laws
by: Paquette, Elliot, et al.
Published: (2024)

Robustly Learning Monotone Generalized Linear Models via Data Augmentation
by: Zarifis, Nikos, et al.
Published: (2025)

Error Analysis of Triangular Optimal Transport Maps for Filtering
by: Al-Jarrah, Mohammad, et al.
Published: (2025)

A New Perspective On Denoising Based On Optimal Transport
by: Trillos, Nicolas Garcia, et al.
Published: (2023)

An Elementary Proof of the Near Optimality of LogSumExp Smoothing
by: Samakhoana, Thabo, et al.
Published: (2025)

Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis
by: Li, Gen, et al.
Published: (2021)

Denoising Diffusions with Optimal Transport: Localization, Curvature, and Multi-Scale Complexity
by: Liang, Tengyuan, et al.
Published: (2024)

Optimal Regularization for a Data Source
by: Leong, Oscar, et al.
Published: (2022)

Online Inference of Constrained Optimization: Primal-Dual Optimality and Sequential Quadratic Programming
by: Gao, Yihang, et al.
Published: (2025)

Robust Assortment Optimization from Observational Data
by: Lu, Miao, et al.
Published: (2026)

Efficient Group Lasso Regularized Rank Regression with Data-Driven Parameter Determination
by: Lin, Meixia, et al.
Published: (2025)

The High Line: Exact Risk and Learning Rate Curves of Stochastic Adaptive Learning Rate Algorithms
by: Collins-Woodfin, Elizabeth, et al.
Published: (2024)

Data-Efficient Non-Gaussian Semi-Nonparametric Density Estimation for Nonlinear Dynamical Systems
by: Liao, Aaron R., et al.
Published: (2026)

Optimal and instance-dependent guarantees for Markovian linear stochastic approximation
by: Mou, Wenlong, et al.
Published: (2021)

Optimal transport natural gradient for statistical manifolds with continuous sample space
by: Chen, Yifan, et al.
Published: (2018)

A Theory of Feature Learning in Kernel Models
by: Chen, Yunlu, et al.
Published: (2023)

On the Uniform Convergence of Subdifferentials in Stochastic Optimization and Learning
by: Ruan, Feng
Published: (2024)

Gradient Equilibrium in Online Learning: Theory and Applications
by: Angelopoulos, Anastasios N., et al.
Published: (2025)

Bayesian Design Principles for Frequentist Sequential Learning
by: Xu, Yunbei, et al.
Published: (2023)

Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning
by: Zhang, Dake, et al.
Published: (2024)

Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria
by: Liang, Tengyuan
Published: (2022)

Risk-Aware Financial Forecasting Enhanced by Machine Learning and Intuitionistic Fuzzy Multi-Criteria Decision-Making
by: Turgay, Safiye, et al.
Published: (2025)

Globalized Adversarial Regret Optimization: Robust Decisions with Uncalibrated Predictions
by: Kurtz, Jannis, et al.
Published: (2026)

Learning the Uncertainty Sets for Control Dynamics via Set Membership: A Non-Asymptotic Analysis
by: Li, Yingying, et al.
Published: (2023)

Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality
by: Chen, Siyu, et al.
Published: (2024)

Mixing Times and Privacy Analysis for the Projected Langevin Algorithm under a Modulus of Continuity
by: Bravo, Mario, et al.
Published: (2025)

A review of NMF, PLSA, LBA, EMA, and LCA with a focus on the identifiability issue
by: Qi, Qianqian, et al.
Published: (2025)

Learning linear dynamical systems under convex constraints
by: Tyagi, Hemant, et al.
Published: (2023)

Joint Learning of Linear Dynamical Systems under Smoothness Constraints
by: Tyagi, Hemant
Published: (2024)

Markov Kernels, Distances and Optimal Control: A Parable of Linear Quadratic Non-Gaussian Distribution Steering
by: Teter, Alexis M. H., et al.
Published: (2025)

Kernel Mean Embedding Topology: Weak and Strong Forms for Stochastic Kernels and Implications for Model Learning
by: Saldi, Naci, et al.
Published: (2025)

Mixing Time of the Proximal Sampler in Relative Fisher Information via Strong Data Processing Inequality
by: Wibisono, Andre
Published: (2025)

Optimal Regularization Under Uncertainty: Distributional Robustness and Convexity Constraints
by: Leong, Oscar, et al.
Published: (2025)

Causal Invariance Learning via Efficient Nonconvex Optimization
by: Wang, Zhenyu, et al.
Published: (2024)