Alessandro De Palma
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Benchmarking and optimization of gradient boosting decision tree algorithms
A Anghel, N Papandreou, T Parnell, A De Palma, H Pozidis
Workshop on Systems for ML at NeurIPS 2018, 2018
Lagrangian decomposition for neural network verification
R Bunel, A De Palma, A Desmaison, K Dvijotham, P Kohli, P Torr, ...
Conference on Uncertainty in Artificial Intelligence, 370-379, 2020
Scaling the Convex Barrier with Active Sets
A De Palma, HS Behl, R Bunel, PHS Torr, MP Kumar
International Conference on Learning Representations, 2021
Communication-avoiding parallel minimum cuts and connected components
L Gianinazzi, P Kalvoda, A De Palma, M Besta, T Hoefler
ACM SIGPLAN Notices 53 (1), 219-232, 2018
In defense of the unitary scalarization for deep multi-task learning
V Kurin, A De Palma, I Kostrikov, S Whiteson, PK Mudigonda
Advances in Neural Information Processing Systems 35, 12169-12183, 2022
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition
A De Palma, R Bunel, A Desmaison, K Dvijotham, P Kohli, PHS Torr, ...
arXiv preprint arXiv:2104.06718, 2021
Sampling acquisition functions for batch Bayesian optimization
A De Palma, C Mendler-DŁnner, T Parnell, A Anghel, H Pozidis
arXiv preprint arXiv:1903.09434, 2019
IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound
A De Palma, R Bunel, K Dvijotham, MP Kumar, R Stanforth
Workshop on Formal Verification of Machine Learning at ICML 2022, 2022
Scaling the Convex Barrier with Sparse Dual Algorithms
A De Palma, HS Behl, R Bunel, PHS Torr, MP Kumar
arXiv preprint arXiv:2101.05844, 2021
Distributed stratified locality sensitive hashing for critical event prediction in the cloud
A De Palma, E Hemberg, UM O'Reilly
Workshop on Machine Learning for Health at NeurIPS 2017, 2017
Expressive Losses for Verified Robustness via Convex Combinations
A De Palma, R Bunel, K Dvijotham, MP Kumar, R Stanforth, A Lomuscio
arXiv preprint arXiv:2305.13991, 2023
Efficient neural network verification and training
A De Palma
University of Oxford, 2023
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