Search Results for author: Thomas Lavastida

Found 4 papers, 1 papers with code

From Stream to Pool: Dynamic Pricing Beyond i.i.d. Arrivals

no code implementations30 Oct 2023 Titing Cui, Su Jia, Thomas Lavastida

The dynamic pricing problem has been extensively studied under the \textbf{stream} model: A stream of customers arrives sequentially, each with an independently and identically distributed valuation.

Algorithms with Prediction Portfolios

1 code implementation22 Oct 2022 Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii

For each of these problems we introduce new algorithms that take advantage of multiple predictors, and prove bounds on the resulting performance.

Scheduling

Faster Matchings via Learned Duals

no code implementations NeurIPS 2021 Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii

Second, once the duals are feasible, they may not be optimal, so we show that they can be used to quickly find an optimal solution.

Combinatorial Optimization

Learnable and Instance-Robust Predictions for Online Matching, Flows and Load Balancing

no code implementations23 Nov 2020 Thomas Lavastida, Benjamin Moseley, R. Ravi, Chenyang Xu

Instance robustness ensures that the prediction is robust to modest changes in the problem input, where the measure of the change may be problem specific.

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