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Thomas Lavastida
Thomas Lavastida
Assistant Professor, Jindal School of Management, University of Texas at Dallas
Verified email at utdallas.edu - Homepage
Title
Cited by
Cited by
Year
Online Scheduling via Learned Weights
S Lattanzi, T Lavastida, B Moseley, S Vassilvitskii
Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete …, 2020
1882020
Learnable and Instance-Robust Predictions for Online Matching, Flows and Load Balancing
T Lavastida, B Moseley, R Ravi, C Xu
arXiv preprint arXiv:2011.11743, 2020
542020
A Framework for Parallelizing Hierarchical Clustering Methods
S Lattanzi, T Lavastida, K Lu, B Moseley
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2019
23*2019
Faster matchings via learned duals
M Dinitz, S Im, T Lavastida, B Moseley, S Vassilvitskii
Advances in Neural Information Processing Systems 34, 10393-10406, 2021
222021
Using Predicted Weights for Ad Delivery
T Lavastida, B Moseley, R Ravi, C Xu
SIAM Conference on Applied and Computational Discrete Algorithms (ACDA21), 21-31, 2021
132021
Algorithms with prediction portfolios
M Dinitz, S Im, T Lavastida, B Moseley, S Vassilvitskii
Advances in neural information processing systems 35, 20273-20286, 2022
72022
Scaling Average-Linkage via Sparse Cluster Embeddings
T Lavastida, K Lu, B Moseley, Y Wang
Asian Conference on Machine Learning, 1429-1444, 2021
22021
A scalable approximation algorithm for weighted longest common subsequence
J Buhler, T Lavastida, K Lu, B Moseley
European Conference on Parallel Processing, 368-384, 2021
12021
Controlling Tail Risk in Online Ski-Rental
M Dinitz, S Im, T Lavastida, B Moseley, S Vassilvitskii
Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms …, 2024
2024
On Scalable Algorithms and Algorithms with Predictions
T Lavastida
Carnegie Mellon University, 2022
2022
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