A Framework for Parallelizing Hierarchical Clustering Methods. - Silvio Lattanzi; Thomas Lavastida; Kefu Lu; Benjamin Moseley (ECML/PKDD (1), 2019)

Online Scheduling via Learned Weights. - Silvio Lattanzi; Thomas Lavastida; Benjamin Moseley; Sergei Vassilvitskii (SODA, 2020)

Using Predicted Weights for Ad Delivery. - Thomas Lavastida; Benjamin Moseley; R. Ravi 001; Chenyang Xu 002 (ACDA, 2021)

Scaling Average-Linkage via Sparse Cluster Embeddings. - Thomas Lavastida; Kefu Lu; Benjamin Moseley; Yuyan Wang (ACML, 2021)

Learnable and Instance-Robust Predictions for Online Matching, Flows and Load Balancing. - Thomas Lavastida; Benjamin Moseley; R. Ravi 001; Chenyang Xu 002 (ESA, 2021)

A Scalable Approximation Algorithm for Weighted Longest Common Subsequence. - Jeremy Buhler; Thomas Lavastida; Kefu Lu; Benjamin Moseley (Euro-Par, 2021)

Faster Matchings via Learned Duals. - Michael Dinitz; Sungjin Im; Thomas Lavastida; Benjamin Moseley; Sergei Vassilvitskii (NeurIPS, 2021)

Algorithms with Prediction Portfolios. - Michael Dinitz; Sungjin Im; Thomas Lavastida; Benjamin Moseley; Sergei Vassilvitskii (NeurIPS, 2022)

From Stream to Pool: Dynamic Pricing Beyond i.i.d. Arrivals. - Titing Cui; Su Jia; Thomas Lavastida (CoRR, 2023)

Binary Search with Distributional Predictions. - Michael Dinitz; Sungjin Im; Thomas Lavastida; Benjamin Moseley; Aidin Niaparast; Sergei Vassilvitskii (NeurIPS, 2024)

Controlling Tail Risk in Online Ski-Rental. - Michael Dinitz; Sungjin Im; Thomas Lavastida; Benjamin Moseley; Sergei Vassilvitskii (SODA, 2024)

Brief Announcement: Stochastic Parallel Scheduling with Bandit Feedback. - Gerdus Benadè; Rathish Das; Thomas Lavastida (SPAA, 2025)