29 citations · 47 across the 15 of their papers we have counts for
19 papers
Beyond Optimal Rates in Stochastic Optimization: Trajectory-Adaptive Stopping Rules
Liviu Aolaritei, Lucas Lévy, Francis Bach +1
Stochastic gradient descent (SGD) is typically analyzed at a deterministic horizon chosen before the algorithm is run, even though practical stopping decisions are made adaptively…
Diffusion-Robust Optimization over Graphs
Liviu Aolaritei, Ricky Huang, Michael I. Jordan +1
We introduce a diffusion-based uncertainty model for robust optimization on directed graphs, in which perturbations of edge weights propagate along adjacent edges and satisfy conse…
Stopping Rules for Stochastic Gradient Descent via Anytime-Valid Confidence Sequences
Liviu Aolaritei, Michael I. Jordan
The problem of stopping stochastic gradient descent (SGD) in an online manner, based solely on the observed trajectory, is a challenging theoretical problem with significant conseq…
Hedging against Black Swans in Day-Ahead Energy Markets
Liviu Aolaritei, Boubacar Bangoura, Saverio Bolognani +2
Renewable generators must commit to day-ahead market bids despite uncertainty in both production and real-time prices. While forecasts provide valuable guidance, rare and unpredict…
Revisiting mean estimation over balls: Is the MLE optimal?
Liviu Aolaritei, Michael I. Jordan, Reese Pathak +1
We revisit the problem of mean estimation in the Gaussian sequence model with constraints for . We demonstrate two phenomena for the behavior of the max…
Valid Selection among Conformal Sets
Mahmoud Hegazy, Liviu Aolaritei, Michael I. Jordan +1
Conformal prediction offers a distribution-free framework for constructing prediction sets with coverage guarantees. In practice, multiple valid conformal prediction sets may be av…
Stochastic Optimization with Optimal Importance Sampling
Liviu Aolaritei, Bart P. G. Van Parys, Henry Lam +1
Importance Sampling (IS) is a widely used variance reduction technique for enhancing the efficiency of Monte Carlo methods, particularly in rare-event simulation and related applic…
Minimum Volume Conformal Sets for Multivariate Regression
Sacha Braun, Liviu Aolaritei, Michael I. Jordan +1
Conformal prediction provides a principled framework for constructing predictive sets with finite-sample validity. While much of the focus has been on univariate response variables…
Learn to Bid as a Price-Maker Wind Power Producer
Shobhit Singhal, Marta Fochesato, Liviu Aolaritei +1
Wind power producers (WPPs) participating in short-term power markets face significant imbalance costs due to their non-dispatchable and variable production. While some WPPs have a…
Conformal Prediction under Levy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations
Liviu Aolaritei, Zheyu Oliver Wang, Julie Zhu +2
Conformal prediction provides a powerful framework for constructing prediction intervals with finite-sample guarantees, yet its robustness under distribution shifts remains a signi…
Wasserstein Tube MPC with Exact Uncertainty Propagation
Liviu Aolaritei, Marta Fochesato, John Lygeros +1
We study model predictive control (MPC) problems for stochastic LTI systems, where the noise distribution is unknown, compactly supported, and only observable through a limited num…
Capture, Propagate, and Control Distributional Uncertainty
Liviu Aolaritei, Nicolas Lanzetti, Florian Dörfler
We study stochastic dynamical systems in settings where only partial statistical information about the noise is available, e.g., in the form of a limited number of noise realizatio…
Nash Equilibria, Regularization and Computation in Optimal Transport-Based Distributionally Robust Optimization
Soroosh Shafiee, Liviu Aolaritei, Florian Dörfler +1
We study optimal transport-based distributionally robust optimization problems where a fictitious adversary, often envisioned as nature, can choose the distribution of the uncertai…
Wasserstein Distributionally Robust Estimation in High Dimensions: Performance Analysis and Optimal Hyperparameter Tuning
Liviu Aolaritei, Soroosh Shafiee, Florian Dörfler
Distributionally robust optimization (DRO) has become a powerful framework for estimation under uncertainty, offering strong out-of-sample performance and principled regularization…
Distributional Uncertainty Propagation via Optimal Transport
Liviu Aolaritei, Nicolas Lanzetti, Hongruyu Chen +1
This paper addresses the limitations of standard uncertainty models, e.g., robust (norm-bounded) and stochastic (one fixed distribution, e.g., Gaussian), and proposes to model unce…
A Robustness Measure of Transient Stability under Operational Constraints in Power Systems
Liviu Aolaritei, Dongchan Lee, Thanh Long Vu +1
The aggressive integration of distributed renewable sources is changing the dynamics of the electric power grid in an unexpected manner. As a result, maintaining conventional perfo…
Robustness against Disturbances in Power Systems under Frequency Constraints
Dongchan Lee, Liviu Aolaritei, Thanh Long Vu +1
The wide deployment of renewable generation and the gradual decrease in the overall system inertia make modern power grids more vulnerable to transient instabilities and unacceptab…
Hierarchical and Distributed Monitoring of Voltage Stability in Distribution Networks
Liviu Aolaritei, Saverio Bolognani, Florian Dörfler
We consider the problem of quantifying and assessing the steady-state voltage stability in radial distribution networks. Our approach to the voltage stability problem is based on a…
A distributed voltage stability margin for power distribution networks
Liviu Aolaritei, Saverio Bolognani, Florian Dörfler
We consider the problem of characterizing and assessing the voltage stability in power distribution networks. Different from previous formulations, we consider the branch-flow para…