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1

Contextual Bandit Applications in a Customer Support Bot:

, In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining,

Sajeev, Sandra ; Huang, Jade ; Karampatziakis, Nikos... - p. 3522-3530 , 2021

Link: https://dl.acm.org/doi/1..

2

Active Entity Recognition in Low Resource Settings:

, In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management,

Gao, Ning ; Karampatziakis, Nikos ; Potharaju, Rahul. - p. 2261-2264 , 2019

Link: https://dl.acm.org/doi/1..

3

Parallel Online Learning:

, In: Scaling up Machine Learning,

Hsu, Daniel ; Karampatziakis, Nikos ; Langford, John. - p. 283-306 , 2011

Link: https://doi.org/10.1017/..

4

Learning prediction suffix trees with Winnow:

, In: Proceedings of the 26th Annual International Conference on Machine Learning,

Karampatziakis, Nikos ; Kozen, Dexter - p. 489-496 , 2009

Link: https://dl.acm.org/doi/1..

5

An empirical evaluation of supervised learning in high dime..:

, In: Proceedings of the 25th international conference on Machine learning,

Caruana, Rich ; Karampatziakis, Nikos ; Yessenalina, Ainur - p. 96-103 , 2008

Link: https://dl.acm.org/doi/1..

6

Three-Dimensional Face Recognition in the Presence of Facia..:

Kakadiaris, Ioannis A. ; Passalis, Georgios ; Toderici, George...
IEEE Transactions on Pattern Analysis and Machine Intelligence. 29 (2007) 4 - p. 640-649 , 2007

Link: https://doi.org/10.1109/..

7

AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient..:

Zhang, Qingru ; Chen, Minshuo ; Bukharin, Alexander...
http://arxiv.org/abs/2303.10512. , 2023

Link: http://arxiv.org/abs/230..

8

LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Langua..:

Li, Yixiao ; Yu, Yifan ; Liang, Chen...
http://arxiv.org/abs/2310.08659. , 2023

Link: http://arxiv.org/abs/231..

9

Meet in the Middle: A New Pre-training Paradigm:

Nguyen, Anh ; Karampatziakis, Nikos ; Chen, Weizhu
http://arxiv.org/abs/2303.07295. , 2023

Link: http://arxiv.org/abs/230..

10

Anytime-valid off-policy inference for contextual bandits:

Waudby-Smith, Ian ; Wu, Lili ; Ramdas, Aaditya..
http://arxiv.org/abs/2210.10768. , 2022

Link: http://arxiv.org/abs/221..

11

Contextual Bandit Applications in Customer Support Bot:

Sajeev, Sandra ; Huang, Jade ; Karampatziakis, Nikos...
http://arxiv.org/abs/2112.03210. , 2021

Link: http://arxiv.org/abs/211..

12

Off-policy Confidence Sequences:

Karampatziakis, Nikos ; Mineiro, Paul ; Ramdas, Aaditya
http://arxiv.org/abs/2102.09540. , 2021

Link: http://arxiv.org/abs/210..

13

Lessons from Contextual Bandit Learning in a Customer Suppo..:

Karampatziakis, Nikos ; Kochman, Sebastian ; Huang, Jade...
http://arxiv.org/abs/1905.02219. , 2019

Link: http://arxiv.org/abs/190..

14

Empirical Likelihood for Contextual Bandits:

Karampatziakis, Nikos ; Langford, John ; Mineiro, Paul
http://arxiv.org/abs/1906.03323. , 2019

Link: http://arxiv.org/abs/190..

15

Logarithmic Time One-Against-Some:

Daume III, Hal ; Karampatziakis, Nikos ; Langford, John.
http://arxiv.org/abs/1606.04988. , 2016

Link: http://arxiv.org/abs/160..

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