Hristo Paskov
Hristo Paskov
Unknown affiliation
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Cited by
Cited by
On the feasibility of internet-scale author identification
A Narayanan, H Paskov, NZ Gong, J Bethencourt, E Stefanov, ECR Shin, ...
2012 IEEE Symposium on Security and Privacy, 300-314, 2012
Exploiting social network structure for person-to-person sentiment analysis
R West, HS Paskov, J Leskovec, C Potts
Transactions of the Association for Computational Linguistics 2, 297-310, 2014
The failure of noise-based non-continuous audio captchas
E Bursztein, R Beauxis, H Paskov, D Perito, C Fabry, J Mitchell
2011 IEEE symposium on security and privacy, 19-31, 2011
Multitask learning improves prediction of cancer drug sensitivity
H Yuan, I Paskov, H Paskov, AJ González, CS Leslie
Scientific reports 6 (1), 31619, 2016
A promising direction for web tracking countermeasures
J Bau, J Mayer, H Paskov, JC Mitchell
Proceedings of W2SP, 2013
Compressive feature learning
HS Paskov, R West, JC Mitchell, T Hastie
Advances in Neural Information Processing Systems 26, 2013
Crosslingual document embedding as reduced-rank ridge regression
M Josifoski, IS Paskov, HS Paskov, M Jaggi, R West
Proceedings of the Twelfth ACM International Conference on Web Search and …, 2019
Learning high order feature interactions with fine control kernels
H Paskov, A Paskov, R West
arXiv preprint arXiv:2002.03298, 2020
A regularization framework for active learning from imbalanced data
HS Paskov
Massachusetts Institute of Technology, 2010
Data representation and compression using linear-programming approximations
HS Paskov, JC Mitchell, TJ Hastie
arXiv preprint arXiv:1511.06606, 2015
Fast Algorithms for Learning with Long N-grams via Suffix Tree Based Matrix Multiplication.
HS Paskov, JC Mitchell, TJ Hastie
UAI, 672-681, 2015
An efficient algorithm for large scale compressive feature learning
H Paskov, J Mitchell, T Hastie
Artificial Intelligence and Statistics, 760-768, 2014
A Practitioners Guide to Differentially Private Convex Optimization
R McKenna, H Paskov, K Talwar
Learning with N-Grams: From Massive Scales to Compressed Representations
HS Paskov
Stanford University, 2017
Exploiting Social Network Structure for Person-to-Person Sentiment Analysis (Supplementary Material to [WPLP14])
R West, HS Paskov, J Leskovec, C Potts
Supplementary Material for Compressive Feature Learning
HS Paskov, R West, JC Mitchell, TJ Hastie
Supplementary Material for An Efficient Algorithm for Large Scale Compressive Feature Learning
H Paskov
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