Spremljaj
Nika Haghtalab
Naslov
Navedeno
Navedeno
Leto
Jailbroken: How does llm safety training fail?
A Wei, N Haghtalab, J Steinhardt
Advances in Neural Information Processing Systems 36, 2024
3092024
Commitment Without Regrets: Online Learning in Stackelberg Security Games
MF Balcan, A Blum, N Haghtalab, AD Procaccia
1372015
Learning optimal commitment to overcome insecurity
A Blum, N Haghtalab, AD Procaccia
Advances in Neural Information Processing Systems 27, 2014
1112014
Efficient learning of linear separators under bounded noise
P Awasthi, MF Balcan, N Haghtalab, R Urner
Conference on Learning Theory, 167-190, 2015
1012015
Learning and 1-bit compressed sensing under asymmetric noise
P Awasthi, MF Balcan, N Haghtalab, H Zhang
Conference on Learning Theory, 152-192, 2016
982016
The disparate equilibria of algorithmic decision making when individuals invest rationally
LT Liu, A Wilson, N Haghtalab, AT Kalai, C Borgs, J Chayes
Proceedings of the 2020 Conference on Fairness, Accountability, and …, 2020
862020
Ignorance is almost bliss: Near-optimal stochastic matching with few queries
A Blum, JP Dickerson, N Haghtalab, AD Procaccia, T Sandholm, ...
Proceedings of the Sixteenth ACM Conference on Economics and Computation …, 2015
822015
Oracle-efficient online learning and auction design
M Dudík, N Haghtalab, H Luo, RE Schapire, V Syrgkanis, JW Vaughan
Journal of the ACM (JACM) 67 (5), 1-57, 2020
752020
Maximizing welfare with incentive-aware evaluation mechanisms
N Haghtalab, N Immorlica, B Lucier, JZ Wang
arXiv preprint arXiv:2011.01956, 2020
692020
Collaborative PAC learning
A Blum, N Haghtalab, AD Procaccia, M Qiao
Advances in Neural Information Processing Systems 30, 2017
582017
The provable virtue of laziness in motion planning
N Haghtalab, S Mackenzie, A Procaccia, O Salzman, S Srinivasa
Proceedings of the International Conference on Automated Planning and …, 2018
552018
Smoothed analysis of online and differentially private learning
N Haghtalab, T Roughgarden, A Shetty
Advances in Neural Information Processing Systems 33, 9203-9215, 2020
502020
Three strategies to success: Learning adversary models in security games
N Haghtalab, F Fang, TH Nguyen, A Sinha, AD Procaccia, M Tambe
452016
Online learning with a hint
O Dekel, N Haghtalab, P Jaillet
Advances in Neural Information Processing Systems 30, 2017
442017
Smoothed analysis with adaptive adversaries
N Haghtalab, T Roughgarden, A Shetty
2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS …, 2022
422022
One for one, or all for all: Equilibria and optimality of collaboration in federated learning
A Blum, N Haghtalab, RL Phillips, H Shao
International Conference on Machine Learning, 1005-1014, 2021
412021
Structured robust submodular maximization: Offline and online algorithms
N Anari, N Haghtalab, S Naor, S Pokutta, M Singh, A Torrico
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
392019
Clustering in the Presence of Background Noise
S Ben-David, N Haghtalab
International Conference in Machine Learning (ICML 2014), 2014
352014
On-demand sampling: Learning optimally from multiple distributions
N Haghtalab, M Jordan, E Zhao
Advances in Neural Information Processing Systems 35, 406-419, 2022
312022
Lazy Defenders Are Almost Optimal Against Diligent Attackers
A Blum, N Haghtalab, AD Procaccia
28th AAAI Conference on Artificial Intelligence, 2014
302014
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