Volodymyr Kuleshov
Volodymyr Kuleshov
Cornell Tech
Verified email at - Homepage
Cited by
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A guide to deep learning in healthcare
A Esteva, A Robicquet, B Ramsundar, V Kuleshov, M DePristo, K Chou, ...
Nature medicine 25 (1), 24-29, 2019
Accurate uncertainties for deep learning using calibrated regression
V Kuleshov, N Fenner, S Ermon
International conference on machine learning, 2796-2804, 2018
Algorithms for multi-armed bandit problems
V Kuleshov, D Precup
arXiv preprint arXiv:1402.6028, 2014
Whole-genome haplotyping using long reads and statistical methods
V Kuleshov, D Xie, R Chen, D Pushkarev, Z Ma, T Blauwkamp, M Kertesz, ...
Nature biotechnology 32 (3), 261-266, 2014
Audio super resolution using neural networks
V Kuleshov, SZ Enam, S Ermon
arXiv preprint arXiv:1708.00853, 2017
Calibrated structured prediction
V Kuleshov, PS Liang
Advances in Neural Information Processing Systems 28, 2015
Synthetic long-read sequencing reveals intraspecies diversity in the human microbiome
V Kuleshov, C Jiang, W Zhou, F Jahanbani, S Batzoglou, M Snyder
Nature biotechnology 34 (1), 64-69, 2016
Tensor factorization via matrix factorization
V Kuleshov, A Chaganty, P Liang
Artificial Intelligence and Statistics, 507-516, 2015
Adversarial examples for natural language classification problems
V Kuleshov, S Thakoor, T Lau, S Ermon
Probabilistic single-individual haplotyping
V Kuleshov
Bioinformatics 30 (17), i379-i385, 2014
Quip: 2-bit quantization of large language models with guarantees
J Chee, Y Cai, V Kuleshov, CM De Sa
Advances in Neural Information Processing Systems 36, 2024
Calibrated model-based deep reinforcement learning
A Malik, V Kuleshov, J Song, D Nemer, H Seymour, S Ermon
International Conference on Machine Learning, 4314-4323, 2019
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations.
S Birnbaum, V Kuleshov, Z Enam, PW Koh, S Ermon
Advances in Neural Information Processing Systems 32, 2019
Inverse game theory: Learning utilities in succinct games
V Kuleshov, O Schrijvers
Web and Internet Economics: 11th International Conference, WINE 2015 …, 2015
Estimating uncertainty online against an adversary
V Kuleshov, S Ermon
Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017
Neural variational inference and learning in undirected graphical models
V Kuleshov, S Ermon
Advances in Neural Information Processing Systems 30, 2017
Genome assembly from synthetic long read clouds
V Kuleshov, MP Snyder, S Batzoglou
Bioinformatics 32 (12), i216-i224, 2016
Harnessing biomedical literature to calibrate clinicians’ trust in AI decision support systems
Q Yang, Y Hao, K Quan, S Yang, Y Zhao, V Kuleshov, F Wang
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems …, 2023
A machine-compiled database of genome-wide association studies
V Kuleshov, J Ding, C Vo, B Hancock, A Ratner, Y Li, C Ré, S Batzoglou, ...
Nature communications 10 (1), 3341, 2019
Learning with weak supervision from physics and data-driven constraints
H Ren, R Stewart, J Song, V Kuleshov, S Ermon
AI Magazine 39 (1), 27-38, 2018
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