Dylan Sam
Dylan Sam
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Adversarial Multiclass Learning under Weak Supervision with Performance Guarantees
A Mazzetto, C Cousins, D Sam, SH Bach, E Upfal
International Conference on Machine Learning, 2021
Semi-supervised aggregation of dependent weak supervision sources with performance guarantees
A Mazzetto, D Sam, A Park, E Upfal, S Bach
International Conference on Artificial Intelligence and Statistics, 3196-3204, 2021
Label Propagation with Weak Supervision
R Pukdee, D Sam, MF Balcan, P Ravikumar
International Conference on Learning Representations, 2023
Automated data accountability for missions in Mars rover data
R Alimo, D Sam, D Lakhmiri, B Kahovec, D Divsalar
2021 IEEE Aerospace Conference (50100), 1-8, 2021
Losses over labels: Weakly supervised learning via direct loss construction
D Sam, JZ Kolter
Proceedings of the AAAI Conference on Artificial Intelligence 37 (8), 9695-9703, 2023
Learning with explanation constraints
R Pukdee, D Sam, JZ Kolter, MFF Balcan, P Ravikumar
Advances in Neural Information Processing Systems 36, 2024
Bayesian neural networks with domain knowledge priors
D Sam, R Pukdee, DP Jeong, Y Byun, JZ Kolter
arXiv preprint arXiv:2402.13410, 2024
Understanding prompt engineering may not require rethinking generalization
V Akinwande, Y Jiang, D Sam, JZ Kolter
arXiv preprint arXiv:2310.03957, 2023
Computing Low-Entropy Couplings for Large-Support Distributions
S Sokota, D Sam, CS de Witt, S Compton, J Foerster, JZ Kolter
arXiv preprint arXiv:2405.19540, 2024
Auditing Fairness under Unobserved Confounding
Y Byun, D Sam, M Oberst, Z Lipton, B Wilder
International Conference on Artificial Intelligence and Statistics, 4339-4347, 2024
Learning from Dependent Weak Supervision Sources
D Sam
Brown University, 2021
Improving self-supervised representation learning via sequential adversarial masking
D Sam, M Bai, T McKinney, LE Li
Self-Supervised Learning: Theory and Practice @ NeurIPS, 2022, 2022
Hierarchical Clustering Analysis of Spectral Fingerprints for Cheminformatics
D Sam, BM Rubenstein
NeurIPS Machine Learning for Molecules Workshop, 2020, 2020
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