Publications by Henrik Hult
Peer reviewed
Articles
[1]
H. Hultin et al., "A deterministic policy gradient method for order execution and option hedging in the presence of market impact," Journal of Financial Data Science, vol. 6, no. 3, pp. 81-114, 2024.
[2]
M. Favero and H. Hult, "Weak convergence of the scaled jump chain and number of mutations of the Kingman coalescent," Electronic Journal of Probability, vol. 29, 2024.
[3]
H. Hultin et al., "A generative model of a limit order book using recurrent neural networks," Quantitative finance (Print), vol. 23, no. 6, pp. 931-958, 2023.
[4]
L. Tarnawski et al., "Cholinergic regulation of vascular endothelial function by human ChAT + T cells," Proceedings of the National Academy of Sciences of the United States of America, vol. 120, no. 14, 2023.
[5]
M. Favero and H. Hult, "Asymptotic behaviour of sampling and transition probabilities in coalescent models under selection and parent dependent mutations," Electronic Communications in Probability, vol. 27, no. none, 2022.
[6]
B. Djehiche, H. Hult and P. Nyquist, "Importance Sampling for a Simple Markovian Intensity Model Using Subsolutions," ACM Transactions on Modeling and Computer Simulation, vol. 32, no. 2, pp. 1-25, 2022.
[7]
A. S. Caravaca et al., "Vagus nerve stimulation promotes resolution of inflammation by a mechanism that involves Alox15 and requires the α7nAChR subunit," Proceedings of the National Academy of Sciences of the United States of America, vol. 119, no. 22, 2022.
[8]
M. J. Donahue et al., "Wireless optoelectronic devices for vagus nerve stimulation in mice," Journal of Neural Engineering, vol. 19, no. 6, pp. 066031, 2022.
[9]
M. Favero, H. Hult and T. Koski, "A dual process for the coupled Wright-Fisher diffusion," Journal of Mathematical Biology, vol. 82, no. 1-2, 2021.
[10]
C. Garcia-Pareja, H. Hult and T. Koski, "EXACT SIMULATION OF COUPLED WRIGHT-FISHER DIFFUSIONS," Advances in Applied Probability, vol. 53, no. 4, pp. 923-950, 2021.
[11]
H. Hult and P. Nyquist, "Large deviations for weighted empirical measures arising in importance sampling," Stochastic Processes and their Applications, vol. 126, no. 1, 2016.
[12]
T. Gudmundsson and H. Hult, "Markov chain monte carlo for computing rare-event probabilities for a heavy-tailed random walk," Journal of Applied Probability, vol. 51, no. 2, pp. 359-376, 2014.
[13]
H. Hult, F. Lindskog and J. Nykvist, "A simple time-consistent model for the forward density process," International Journal of Theoretical and Applied Finance, vol. 16, no. 8, pp. 13500489, 2013.
[14]
J. Blanchet, H. Hult and K. Leder, "Rare-Event Simulation for Stochastic Recurrence Equations with Heavy-Tailed Innovations," ACM Transactions on Modeling and Computer Simulation, vol. 23, no. 4, pp. 22, 2013.
[15]
H. Hult and J. Svensson, "On Importance Sampling with Mixtures for Random Walks with Heavy Tails," ACM Transactions on Modeling and Computer Simulation, vol. 22, no. 2, pp. 8, 2012.
[16]
H. Hult and F. Lindskog, "Ruin probabilities under general investments and heavy-tailed claims," Finance and Stochastics, vol. 15, no. 2, pp. 243-265, 2011.
[17]
H. Hult and G. Samorodnitsky, "Large deviations for point processes based on stationary sequences with heavy tails," Journal of Applied Probability, vol. 47, no. 1, pp. 1-40, 2010.
[18]
H. Hult and G. Samorodnitsky, "Tail probabilities for infinite series of regularly varying random vectors," Bernoulli, vol. 14, no. 3, pp. 838-864, 2008.
[19]
H. Hult and F. Lindskog, "On Kesten's counterexample to the Cramer-Wold device for regular variation," Bernoulli, vol. 12, no. 1, pp. 133-142, 2006.
[20]
H. Hult and F. Lindskog, "On regular variation for infinitely divisible random vectors and additive processes," Advances in Applied Probability, vol. 38, no. 1, pp. 134-148, 2006.
[21]
H. Hult and H. Lindskog, "Regular variation for measures on metric spaces," Publications de l'Institut Mathématique (Beograd), vol. 80, no. 94, pp. 121-140, 2006.
[22]
T. Björk and H. Hult, "A note on Wick products and the fractional Black-Scholes model," Finance and Stochastics, vol. 9, no. 2, pp. 197-209, 2005.
[23]
H. Hult and F. Lindskog, "Extremal behavior of regularly varying stochastic processes," Stochastic Processes and their Applications, vol. 115, no. 2, pp. 249-274, 2005.
[24]
H. Hult, "Approximating some Volterra type stochastic integrals with applications to parameter estimation," Stochastic Processes and their Applications, vol. 105, no. 1, pp. 1-32, 2003.
[25]
H. Hult and F. Lindskog, "Multivariate extremes, aggregation and dependence in elliptical distributions," Advances in Applied Probability, vol. 32, no. 3, pp. 587-608, 2002.
Conference papers
[26]
M. Nordström et al., "On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice," in Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022, 2022.
[27]
M. Nordström et al., "On image segmentation with noisy labels : characterization and volume properties of the optimal solutions to accuracy and dice.," in NeuRIPS 2022, 2022.
[28]
M. Nordström et al., "Calibrated Surrogate Maximization of Dice," in Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 : 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part IV, 2020, pp. 269-278.
[29]
C. Ringqvist, P. Nyquist and H. Hult, "Infinite Swapping Algorithm for Training Restricted Boltzmann Machines," in Monte Carlo and Quasi-Monte Carlo Methods, 2020, pp. 285-307.
[30]
C. Ringqvist et al., "Interpolation in Auto Encoders with Bridge Processes," in Proceedings of the 25th International Conference on Pattern Recognition, ICPR 2020, 2020.
[31]
B. Hargreaves, H. Hult and S. Reda, "Within-die process variations: How accurately can they be statistically modeled?," in 13th Asia and South Pacific Design Automation Conference, 2008.
Books
[32]
F. Lindskog et al., Risk and portfolio analysis : principles and methods. Springer-Verlag New York, 2012.
Chapters in books
[33]
H. Hult et al., "Convex Optimization," in Springer Series in Operations Research and Financial Engineering, : Springer Nature, 2012, pp. 33-38.
[34]
H. Hult et al., "Empirical Methods," in Risk and Portfolio Analysis, : Springer Nature, 2012, pp. 197-229.
[35]
H. Hult et al., "Interest Rates and Financial Derivatives," in Risk and Portfolio Analysis, : Springer Nature, 2012, pp. 3-31.
[36]
H. Hult et al., "Multivariate Models," in Risk and Portfolio Analysis, : Springer Nature, 2012, pp. 273-330.
[37]
H. Hult et al., "Parametric Models and Their Tails," in Springer Series in Operations Research and Financial Engineering, : Springer Nature, 2012, pp. 231-271.
[38]
H. Hult et al., "Preface," in Risk and Portfolio Analysis : Principles and Methods, : Springer Nature, 2012, pp. vii-x.
[39]
H. Hult et al., "Quadratic Hedging Principles," in Risk and Portfolio Analysis, : Springer Nature, 2012, pp. 39-83.
[40]
H. Hult et al., "Quadratic Investment Principles," in Risk and Portfolio Analysis, : Springer Nature, 2012, pp. 85-126.
[41]
H. Hult et al., "Risk Measurement Principles," in Risk and Portfolio Analysis, : Springer Nature, 2012, pp. 159-194.
[42]
H. Hult et al., "Utility-Based Investment Principles," in Risk and Portfolio Analysis, : Springer Nature, 2012, pp. 127-157.
Non-peer reviewed
Articles
[43]
M. Nordström et al., "Interactive Deep Learning-Based Delineation of Gross Tumor Volume for Postoperative Glioma Patients," Medical physics (Lancaster), vol. 46, no. 6, pp. E426-E427, 2019.
[44]
M. Nordström et al., "Pareto Dose Prediction Using Fully Convolutional Networks Operating in 3D," Medical physics (Lancaster), vol. 45, no. 6, pp. E176-E176, 2018.
Conference papers
[45]
J. Blanchet, H. Hult and K. Leder, "Importance sampling for stochastic recurrence equations with heavy-tailed increments," in Proceedings of the 2011 Winter Simulation Conference, 2011, pp. 3824-3831.
Reports
[46]
H. Hult and F. Lindskog, "Heavy-tailed insurance portfolios : buffer capital and ruin probabilities," , Technical Report, Cornell University, ORIE, 1441, 2006.
Other
[47]
H. Hultin et al., "A generative model of a limit order book using recurrent neural networks," (Manuscript).
[48]
H. Hult and J. Nykvist, "A note on efficient importance sampling for one-dimensional diffusions," (Manuscript).
[49]
H. Hult et al., "A weak convergence approach to large deviations for stochastic approximations," (Manuscript).
[50]
[51]
M. Favero and H. Hult, "Asymptotic analysis of backward sampling algorithms in Kingman's coalescent," (Manuscript).
[52]
M. Favero and H. Hult, "Asymptotic behaviour of sampling and backward transition probabilities of the coalescent with parent dependent mutations," (Manuscript).
[53]
H. Hult and J. Svensson, "Efficient calculation of risk measures by importance sampling -- the heavy tailed case," (Manuscript).
[54]
H. Hult and J. Nykvist, "Efficient importance sampling to assess the risk of voltage collapse in power systems," (Manuscript).
[55]
H. Hult and J. Nykvist, "Efficient importance sampling to compute loss probabilities in financial portfolios," (Manuscript).
[56]
B. Djehiche, H. Hult and P. Nyquist, "Importance sampling for a Markovian intensity model with applications to credit risk," (Manuscript).
[57]
A. Lindhe and H. Hult, "Large Deviation Techniques for Evaluating Variational Autoencoders," (Manuscript).
[58]
M. Nordström, H. Hult and A. Maki, "Marginal Thresholding in Noisy Image Segmentation," (Manuscript).
[59]
T. Gudmundsson and H. Hult, "Markov chain Monte Carlo for rare-event simulation for Markov chains," (Manuscript).
[60]
T. Gudmundsson and H. Hult, "Markov chain Monte Carlo for rare-event simulation for light-tailed random walk," (Manuscript).
[61]
T. Gudmundsson and H. Hult, "Markov chain Monte Carlo for rare-event simulation for stochastic recurrence equations with heavy-tailed innovations," (Manuscript).
[62]
B. Djehiche, H. Hult and P. Nyquist, "Min-max representations of viscosity solutions of Hamilton-Jacobi equations and applications in rare-event simulation," (Manuscript).
[63]
[64]
H. Hultin, H. Hult and A. Proutiere, "On the convergence of policy gradients with parameter-based exploration," (Manuscript).
[65]
H. Hult, A. Lindhe and P. Nyquist, "On the projected Aubry set of the rate function associated with large deviations for stochastic approximations," (Manuscript).
[66]
[67]
H. Hultin et al., "Reinforcement learning for optimal execution in high resolution Markovian limit order book models," (Manuscript).
[68]
[69]
M. Favero and H. Hult, "Weak convergence of the scaled jump chain and number of mutations of the Kingman coalescent," (Manuscript).
Latest sync with DiVA:
2024-11-21 01:07:44