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Grigoris Velegkas
Grigoris Velegkas
Yale University
Zweryfikowany adres z yale.edu - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
An Efficient ∊-BIC to BIC Transformation and Its Application to Black-Box Reduction in Revenue Maximization
Y Cai, A Oikonomou, G Velegkas, M Zhao
Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms (SODA …, 2021
28*2021
Statistical indistinguishability of learning algorithms
A Kalavasis, A Karbasi, S Moran, G Velegkas
International Conference on Machine Learning, 15586-15622, 2023
242023
Replicable bandits
H Esfandiari, A Kalavasis, A Karbasi, A Krause, V Mirrokni, G Velegkas
arXiv preprint arXiv:2210.01898, 2022
222022
Optimal learners for realizable regression: Pac learning and online learning
I Attias, S Hanneke, A Kalavasis, A Karbasi, G Velegkas
Advances in Neural Information Processing Systems 36, 2023
172023
Replicable clustering
H Esfandiari, A Karbasi, V Mirrokni, G Velegkas, F Zhou
Advances in Neural Information Processing Systems 36, 2024
162024
Multiclass learnability beyond the pac framework: Universal rates and partial concept classes
A Kalavasis, G Velegkas, A Karbasi
Advances in Neural Information Processing Systems 35, 20809-20822, 2022
122022
How to sell information optimally: An algorithmic study
Y Cai, G Velegkas
arXiv preprint arXiv:2011.14570, 2020
122020
Is Selling Complete Information (Approximately) Optimal?
D Bergemann, Y Cai, G Velegkas, M Zhao
Proceedings of the 23rd ACM Conference on Economics and Computation, 608-663, 2022
112022
Replicability in reinforcement learning
A Karbasi, G Velegkas, L Yang, F Zhou
Advances in Neural Information Processing Systems 36, 74702-74735, 2023
92023
Replicable learning of large-margin halfspaces
A Kalavasis, A Karbasi, KG Larsen, G Velegkas, F Zhou
arXiv preprint arXiv:2402.13857, 2024
62024
Universal rates for interactive learning
S Hanneke, A Karbasi, S Moran, G Velegkas
Advances in Neural Information Processing Systems 35, 28657-28669, 2022
62022
Reinforcement learning with logarithmic regret and policy switches
G Velegkas, Z Yang, A Karbasi
Advances in Neural Information Processing Systems 35, 36040-36053, 2022
4*2022
Universal Rates for Regression: Separations between Cut-Off and Absolute Loss
I Attias, S Hanneke, A Kalavasis, A Karbasi, G Velegkas
The Thirty Seventh Annual Conference on Learning Theory, 359-405, 2024
22024
On the Limits of Language Generation: Trade-Offs Between Hallucination and Mode Collapse
A Kalavasis, A Mehrotra, G Velegkas
arXiv preprint arXiv:2411.09642, 2024
12024
On the Computational Landscape of Replicable Learning
A Kalavasis, A Karbasi, G Velegkas, F Zhou
arXiv preprint arXiv:2405.15599, 2024
12024
Injecting Undetectable Backdoors in Obfuscated Neural Networks and Language Models
A Kalavasis, A Karbasi, A Oikonomou, K Sotiraki, G Velegkas, ...
The Thirty-eighth Annual Conference on Neural Information Processing Systems, 0
1*
Procurement Auctions via Approximately Optimal Submodular Optimization
Y Deng, A Karbasi, V Mirrokni, RP Leme, G Velegkas, S Zuo
arXiv preprint arXiv:2411.13513, 2024
2024
Randomized Truthful Auctions with Learning Agents
G Aggarwal, A Gupta, A Perlroth, G Velegkas
arXiv preprint arXiv:2411.09517, 2024
2024
Understanding Aggregations of Proper Learners in Multiclass Classification
J Asilis, MM Høgsgaard, G Velegkas
arXiv preprint arXiv:2410.22749, 2024
2024
Pointwise Lipschitz Continuous Graph Algorithms via Proximal Gradient Analysis
QC Liu, G Velegkas, Y Yoshida, F Zhou
arXiv preprint arXiv:2405.08938, 2024
2024
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