Research

2022.
In Defense of the Unitary Scalarization for Deep Multi-Task Learning.
V. Kurin*, A. De Palma*, I. Kostrikov, S. Whiteson,  M. P. Kumar.
Neural Information Processing Systems (NeurIPS) 2022. [arXiv]

IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound.
A. De Palma, R. Bunel, K. Dvijotham, M. P. Kumar, R. Stanforth.
ICML 2022 Workshop on Formal Verification of Machine Learning, best paper award. [arXiv]

2021.
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition.
A. De Palma, R. Bunel, A. Desmaison, K. Dvijotham, P. Kohli, P. H.S. Torr, M. P. Kumar.
arXiv preprint arXiv:2104.06718. [arXiv]

Scaling the Convex Barrier with Sparse Dual Algorithms.
A. De Palma, H.S. Behl, R. Bunel, P. H.S. Torr, M. P. Kumar.
arXiv preprint arXiv:2101.058448. [arXiv]

Scaling the Convex Barrier with Active Sets.
A. De Palma*, H.S. Behl*, R. Bunel, P. H.S. Torr, M. P. Kumar.
International Conference on Learning Representations (ICLR) 2021. [OpenReview]

2020.
Lagrangian Decomposition for Neural Network Verification.
R. Bunel*, A. De Palma*, A. Desmaison, K. Dvijotham, P. Kohli, P. H.S. Torr, M. P. Kumar.
Conference on Uncertainty in Artificial Intelligence (UAI) 2020. [arXiv]

2018.
Sampling Acquisition Functions for Batch Bayesian Optimization.
A. De Palma, C. Dünner, T. Parnell, A. Anghel, H. Pozidis.
NeurIPS BNP 2018 workshop. [arXiv]

Benchmarking and Optimization of Gradient Boosting Decision Tree Algorithms.
A. Anghel, N. Papandreou, T. Parnell, A. De Palma, H. Pozidis.
NeurIPS SysML 2018 workshop. [arXiv]

Communication-avoiding minimum cuts and connected components.
L. Gianinazzi, P. Kalvoda, A. De Palma, M. Besta, T. Hoefler.
ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming 2018.

2017.
Distributed Stratified Locality Sensitive Hashing for critical event prediction in the cloud.
A. De Palma, E. Hemberg, U. O’Reilly.
NeurIPS ML4H 2017 workshop, travel award winner. [arXiv]

(* indicates joint first authorship)

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