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ORCIDVolodymyr Kuleshov
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2020 – today
- 2023
[i22]Volodymyr Kuleshov, Shachi Deshpande:
Online Calibrated Regression for Adversarially Robust Forecasting. CoRR abs/2302.12196 (2023)- 2022
- [c18]Yuntian Deng, Volodymyr Kuleshov, Alexander M. Rush:
Model Criticism for Long-Form Text Generation. EMNLP 2022: 11887-11912 - [c17]Phillip Si, Allan Bishop, Volodymyr Kuleshov:
Autoregressive Quantile Flows for Predictive Uncertainty Estimation. ICLR 2022 - [c16]Volodymyr Kuleshov, Shachi Deshpande:
Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation. ICML 2022: 11683-11693 - [i21]Shachi Deshpande, Zheng Li, Volodymyr Kuleshov:
Multi-Modal Causal Inference with Deep Structural Equation Models. CoRR abs/2203.09672 (2022) - [i20]Richa Rastogi, Yuntian Deng, Ian Lee, Mert R. Sabuncu, Volodymyr Kuleshov:
Semi-Parametric Deep Neural Networks in Linear Time and Memory. CoRR abs/2205.11718 (2022) - [i19]Subham Sekhar Sahoo, Marin Vlastelica, Anselm Paulus, Vít Musil, Volodymyr Kuleshov, Georg Martius:
Gradient Backpropagation Through Combinatorial Algorithms: Identity with Projection Works. CoRR abs/2205.15213 (2022) - [i18]Phillip Si, Volodymyr Kuleshov:
Energy Flows: Towards Determinant-Free Training of Normalizing Flows. CoRR abs/2206.06672 (2022) - [i17]Yuntian Deng, Volodymyr Kuleshov, Alexander M. Rush:
Model Criticism for Long-Form Text Generation. CoRR abs/2210.08444 (2022) - [i16]Jacqueline R. M. A. Maasch, Hao Zhang, Qian Yang, Fei Wang, Volodymyr Kuleshov:
Regularized Data Programming with Bayesian Priors. CoRR abs/2210.08677 (2022) - 2021
- [i15]Bojian Hou, Hao Zhang, Gur Ladizhinsky, Stephen Yang, Volodymyr Kuleshov, Fei Wang, Qian Yang:
Clinical Evidence Engine: Proof-of-Concept For A Clinical-Domain-Agnostic Decision Support Infrastructure. CoRR abs/2111.00621 (2021) - [i14]Shachi Deshpande, Volodymyr Kuleshov:
Calibration Improves Bayesian Optimization. CoRR abs/2112.04620 (2021) - [i13]Phillip Si, Allan Bishop, Volodymyr Kuleshov:
Autoregressive Quantile Flows for Predictive Uncertainty Estimation. CoRR abs/2112.04643 (2021) - [i12]Volodymyr Kuleshov, Evgenii Nikishin, Shantanu Thakoor, Tingfung Lau, Stefano Ermon:
Quantifying and Understanding Adversarial Examples in Discrete Input Spaces. CoRR abs/2112.06276 (2021) - [i11]Volodymyr Kuleshov, Shachi Deshpande:
Calibrated and Sharp Uncertainties in Deep Learning via Simple Density Estimation. CoRR abs/2112.07184 (2021)
2010 – 2019
- 2019
- [c15]Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon:
Calibrated Model-Based Deep Reinforcement Learning. ICML 2019: 4314-4323 - [c14]Sawyer Birnbaum, Volodymyr Kuleshov, S. Zayd Enam, Pang Wei Koh, Stefano Ermon:
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations. NeurIPS 2019: 10287-10298 - [i10]Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon:
Calibrated Model-Based Deep Reinforcement Learning. CoRR abs/1906.08312 (2019) - [i9]Sawyer Birnbaum, Volodymyr Kuleshov, S. Zayd Enam, Pang Wei Koh, Stefano Ermon:
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations. CoRR abs/1909.06628 (2019) - 2018
- [j4]Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon:
Learning with Weak Supervision from Physics and Data-Driven Constraints. AI Mag. 39(1): 27-38 (2018) - [j3]Victoria Popic, Volodymyr Kuleshov, Michael P. Snyder, Serafim Batzoglou:
Fast Metagenomic Binning via Hashing and Bayesian Clustering. J. Comput. Biol. 25(7): 677-688 (2018) - [c13]Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon:
Accurate Uncertainties for Deep Learning Using Calibrated Regression. ICML 2018: 2801-2809 - [c12]Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon:
Adversarial Constraint Learning for Structured Prediction. IJCAI 2018: 2637-2643 - [i8]Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon:
Adversarial Constraint Learning for Structured Prediction. CoRR abs/1805.10561 (2018) - [i7]Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon:
Accurate Uncertainties for Deep Learning Using Calibrated Regression. CoRR abs/1807.00263 (2018) - 2017
- [b1]Volodymyr Kuleshov:
Intelligent systems for personalized genomic medicine. Stanford University, USA, 2017 - [c11]Volodymyr Kuleshov, Stefano Ermon:
Estimating Uncertainty Online Against an Adversary. AAAI 2017: 2110-2116 - [c10]Volodymyr Kuleshov, S. Zayd Enam, Stefano Ermon:
Audio Super-Resolution using Neural Networks. ICLR (Workshop) 2017 - [c9]Volodymyr Kuleshov, Stefano Ermon:
Neural Variational Inference and Learning in Undirected Graphical Models. NIPS 2017: 6734-6743 - [c8]Victoria Popic, Volodymyr Kuleshov, Michael P. Snyder, Serafim Batzoglou:
GATTACA: Lightweight Metagenomic Binning Using Kmer Counting. RECOMB 2017: 391-392 - [c7]Volodymyr Kuleshov, Stefano Ermon:
Hybrid Deep Discriminative/Generative Models for Semi-Supervised Learning. UAI 2017 - [i6]Volodymyr Kuleshov, S. Zayd Enam, Stefano Ermon:
Audio Super Resolution using Neural Networks. CoRR abs/1708.00853 (2017) - [i5]Volodymyr Kuleshov, Stefano Ermon:
Neural Variational Inference and Learning in Undirected Graphical Models. CoRR abs/1711.02679 (2017) - 2016
- [j2]Volodymyr Kuleshov, Michael P. Snyder
, Serafim Batzoglou:
Genome assembly from synthetic long read clouds. Bioinform. 32(12): 216-224 (2016) - [i4]Volodymyr Kuleshov, Stefano Ermon:
Reliable Confidence Estimation via Online Learning. CoRR abs/1607.03594 (2016) - 2015
- [c6]Volodymyr Kuleshov, Arun Tejasvi Chaganty, Percy Liang:
Tensor Factorization via Matrix Factorization. AISTATS 2015 - [c5]Volodymyr Kuleshov, Percy Liang:
Calibrated Structured Prediction. NIPS 2015: 3474-3482 - [c4]Volodymyr Kuleshov, Okke Schrijvers:
Inverse Game Theory: Learning Utilities in Succinct Games. WINE 2015: 413-427 - [i3]Volodymyr Kuleshov, Arun Tejasvi Chaganty, Percy Liang:
Simultaneous diagonalization: the asymmetric, low-rank, and noisy settings. CoRR abs/1501.06318 (2015) - [i2]Volodymyr Kuleshov, Arun Tejasvi Chaganty, Percy Liang:
Tensor Factorization via Matrix Factorization. CoRR abs/1501.07320 (2015) - 2014
- [j1]Volodymyr Kuleshov:
Probabilistic single-individual haplotyping. Bioinform. 30(17): 379-385 (2014) - [i1]Volodymyr Kuleshov, Doina Precup:
Algorithms for multi-armed bandit problems. CoRR abs/1402.6028 (2014) - 2013
- [c3]Volodymyr Kuleshov:
Fast algorithms for sparse principal component analysis based on Rayleigh quotient iteration. ICML (3) 2013: 1418-1425 - 2012
- [c2]Volodymyr Kuleshov, Gordon T. Wilfong:
On the Efficiency of the Simplest Pricing Mechanisms in Two-Sided Markets. WINE 2012: 284-297 - 2010
- [c1]Volodymyr Kuleshov, Adrian Vetta:
On the Efficiency of Markets with Two-Sided Proportional Allocation Mechanisms. SAGT 2010: 246-261
Coauthor Index
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last updated on 2023-03-01 22:57 CET by the dblp team
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