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Nikos Karampatziakis
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2020 – today
- 2024
- [c18]Yixiao Li, Yifan Yu, Chen Liang, Nikos Karampatziakis, Pengcheng He, Weizhu Chen, Tuo Zhao:
LoftQ: LoRA-Fine-Tuning-aware Quantization for Large Language Models. ICLR 2024 - [i26]Marah I Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Harkirat S. Behl, Alon Benhaim, Misha Bilenko, Johan Bjorck, Sébastien Bubeck, Martin Cai, Caio César Teodoro Mendes, Weizhu Chen, Vishrav Chaudhary, Parul Chopra, Allie Del Giorno, Gustavo de Rosa, Matthew Dixon, Ronen Eldan, Dan Iter, Amit Garg, Abhishek Goswami, Suriya Gunasekar, Emman Haider, Junheng Hao, Russell J. Hewett, Jamie Huynh, Mojan Javaheripi, Xin Jin, Piero Kauffmann, Nikos Karampatziakis, Dongwoo Kim, Mahoud Khademi, Lev Kurilenko, James R. Lee, Yin Tat Lee, Yuanzhi Li, Chen Liang, Weishung Liu, Eric Lin, Zeqi Lin, Piyush Madan, Arindam Mitra, Hardik Modi, Anh Nguyen, Brandon Norick, Barun Patra, Daniel Perez-Becker, Thomas Portet, Reid Pryzant, Heyang Qin, Marko Radmilac, Corby Rosset, Sambudha Roy, Olatunji Ruwase, Olli Saarikivi, Amin Saied, Adil Salim, Michael Santacroce, Shital Shah, Ning Shang, Hiteshi Sharma, Xia Song, Masahiro Tanaka, Xin Wang, Rachel Ward, Guanhua Wang, Philipp Witte, Michael Wyatt, Can Xu, Jiahang Xu, Sonali Yadav, Fan Yang, Ziyi Yang, Donghan Yu, Chengruidong Zhang, Cyril Zhang, Jianwen Zhang, Li Lyna Zhang, Yi Zhang, Yue Zhang, Yunan Zhang, Xiren Zhou:
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone. CoRR abs/2404.14219 (2024) - [i25]Ziyu Xu, Nikos Karampatziakis, Paul Mineiro:
Active, anytime-valid risk controlling prediction sets. CoRR abs/2406.10490 (2024) - [i24]Yifang Chen, Shuohang Wang, Ziyi Yang, Hiteshi Sharma, Nikos Karampatziakis, Donghan Yu, Kevin G. Jamieson, Simon Shaolei Du, Yelong Shen:
Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning. CoRR abs/2407.02119 (2024) - 2023
- [c17]Anh Nguyen, Nikos Karampatziakis, Weizhu Chen:
Meet in the Middle: A New Pre-training Paradigm. NeurIPS 2023 - [i23]Anh Nguyen, Nikos Karampatziakis, Weizhu Chen:
Meet in the Middle: A New Pre-training Paradigm. CoRR abs/2303.07295 (2023) - [i22]Yixiao Li, Yifan Yu, Chen Liang, Pengcheng He, Nikos Karampatziakis, Weizhu Chen, Tuo Zhao:
LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models. CoRR abs/2310.08659 (2023) - 2022
- [i21]Ian Waudby-Smith, Lili Wu, Aaditya Ramdas, Nikos Karampatziakis, Paul Mineiro:
Anytime-valid off-policy inference for contextual bandits. CoRR abs/2210.10768 (2022) - 2021
- [c16]Nikos Karampatziakis, Paul Mineiro, Aaditya Ramdas:
Off-Policy Confidence Sequences. ICML 2021: 5301-5310 - [c15]Sandra Sajeev, Jade Huang, Nikos Karampatziakis, Matthew Hall, Sebastian Kochman, Weizhu Chen:
Contextual Bandit Applications in a Customer Support Bot. KDD 2021: 3522-3530 - [i20]Nikos Karampatziakis, Paul Mineiro, Aaditya Ramdas:
Off-policy Confidence Sequences. CoRR abs/2102.09540 (2021) - [i19]Sandra Sajeev, Jade Huang, Nikos Karampatziakis, Matthew Hall, Sebastian Kochman, Weizhu Chen:
Contextual Bandit Applications in Customer Support Bot. CoRR abs/2112.03210 (2021) - 2020
- [c14]Nikos Karampatziakis, John Langford, Paul Mineiro:
Empirical Likelihood for Contextual Bandits. NeurIPS 2020
2010 – 2019
- 2019
- [c13]Ning Gao, Nikos Karampatziakis, Rahul Potharaju, Silviu Cucerzan:
Active Entity Recognition in Low Resource Settings. CIKM 2019: 2261-2264 - [i18]Nikos Karampatziakis, Sebastian Kochman, Jade Huang, Paul Mineiro, Kathy Osborne, Weizhu Chen:
Lessons from Real-World Reinforcement Learning in a Customer Support Bot. CoRR abs/1905.02219 (2019) - [i17]Nikos Karampatziakis, John Langford, Paul Mineiro:
Empirical Likelihood for Contextual Bandits. CoRR abs/1906.03323 (2019) - 2017
- [c12]Hal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro:
Logarithmic Time One-Against-Some. ICML 2017: 923-932 - [c11]Rashish Tandon, Qi Lei, Alexandros G. Dimakis, Nikos Karampatziakis:
Gradient Coding: Avoiding Stragglers in Distributed Learning. ICML 2017: 3368-3376 - 2016
- [i16]He He, Paul Mineiro, Nikos Karampatziakis:
Active Information Acquisition. CoRR abs/1602.02181 (2016) - [i15]Hal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro:
Logarithmic Time One-Against-Some. CoRR abs/1606.04988 (2016) - [i14]Kalina Jasinska, Nikos Karampatziakis:
Log-time and Log-space Extreme Classification. CoRR abs/1611.01964 (2016) - [i13]Rashish Tandon, Qi Lei, Alexandros G. Dimakis, Nikos Karampatziakis:
Gradient Coding. CoRR abs/1612.03301 (2016) - 2015
- [c10]Paul Mineiro, Nikos Karampatziakis:
Fast Label Embeddings via Randomized Linear Algebra. ECML/PKDD (1) 2015: 37-51 - [c9]Paul Mineiro, Nikos Karampatziakis:
Fast Label Embeddings for Extremely Large Output Spaces. ICLR (Workshop) 2015 - [i12]Nikos Karampatziakis, Paul Mineiro:
Multilabel Prediction via Calibration. CoRR abs/1502.02710 (2015) - [i11]Paul Mineiro, Nikos Karampatziakis:
A Hierarchical Spectral Method for Extreme Classification. CoRR abs/1511.03260 (2015) - 2014
- [c8]Nikos Karampatziakis, Paul Mineiro:
Discriminative Features via Generalized Eigenvectors. ICML 2014: 494-502 - [c7]Alekh Agarwal, Sham M. Kakade, Nikos Karampatziakis, Le Song, Gregory Valiant:
Least Squares Revisited: Scalable Approaches for Multi-class Prediction. ICML 2014: 541-549 - [c6]Xi C. Chen, Abdullah Mueen, Vijay K. Narayanan, Nikos Karampatziakis, Gagan Bansal, Vipin Kumar:
Online Discovery of Group Level Events in Time Series. SDM 2014: 632-640 - [i10]Paul Mineiro, Nikos Karampatziakis:
A Randomized Algorithm for CCA. CoRR abs/1411.3409 (2014) - [i9]Paul Mineiro, Nikos Karampatziakis:
Fast Label Embeddings for Extremely Large Output Spaces. CoRR abs/1412.6547 (2014) - 2013
- [c5]Paul Mineiro, Nikos Karampatziakis:
Loss-Proportional Subsampling for Subsequent ERM. ICML (3) 2013: 522-530 - [i8]Paul Mineiro, Nikos Karampatziakis:
Loss-Proportional Subsampling for Subsequent ERM. CoRR abs/1306.1840 (2013) - [i7]Nikos Karampatziakis, Paul Mineiro:
Discriminative Features via Generalized Eigenvectors. CoRR abs/1310.1934 (2013) - [i6]Alekh Agarwal, Sham M. Kakade, Nikos Karampatziakis, Le Song, Gregory Valiant:
Least Squares Revisited: Scalable Approaches for Multi-class Prediction. CoRR abs/1310.1949 (2013) - [i5]Nikos Karampatziakis, Paul Mineiro:
Combining Structured and Unstructured Randomness in Large Scale PCA. CoRR abs/1310.6304 (2013) - [i4]Zhen Qin, Vaclav Petricek, Nikos Karampatziakis, Lihong Li, John Langford:
Efficient Online Bootstrapping for Large Scale Learning. CoRR abs/1312.5021 (2013) - 2012
- [c4]Nikos Karampatziakis, Jack W. Stokes, Anil Thomas, Mady Marinescu:
Using File Relationships in Malware Classification. DIMVA 2012: 1-20 - 2011
- [c3]Miroslav Dudík, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang:
Efficient Optimal Learning for Contextual Bandits. UAI 2011: 169-178 - [c2]Nikos Karampatziakis, John Langford:
Online Importance Weight Aware Updates. UAI 2011: 392-399 - [i3]Daniel J. Hsu, Nikos Karampatziakis, John Langford, Alexander J. Smola:
Parallel Online Learning. CoRR abs/1103.4204 (2011) - [i2]Miroslav Dudík, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang:
Efficient Optimal Learning for Contextual Bandits. CoRR abs/1106.2369 (2011) - 2010
- [c1]Nikos Karampatziakis:
Static Analysis of Binary Executables Using Structural SVMs. NIPS 2010: 1063-1071 - [i1]Nikos Karampatziakis, John Langford:
Importance Weight Aware Gradient Updates. CoRR abs/1011.1576 (2010)
Coauthor Index
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