Header logo is


2016


Gaussian Process-Based Predictive Control for Periodic Error Correction
Gaussian Process-Based Predictive Control for Periodic Error Correction

Klenske, E. D., Zeilinger, M., Schölkopf, B., Hennig, P.

IEEE Transactions on Control Systems Technology , 24(1):110-121, 2016 (article)

ei pn

PDF DOI [BibTex]

2016


PDF DOI [BibTex]


Dual Control for Approximate Bayesian Reinforcement Learning
Dual Control for Approximate Bayesian Reinforcement Learning

Klenske, E. D., Hennig, P.

Journal of Machine Learning Research, 17(127):1-30, 2016 (article)

ei pn

PDF link (url) [BibTex]

PDF link (url) [BibTex]


no image
Stochastic search with Poisson and deterministic resetting

Bhat, U., De Bacco, C., Redner, S.

Journal of Statistical Mechanics: Theory and Experiment, 2016(8):083401, IOP Publishing, 2016 (article)

pio

Preprint link (url) [BibTex]

Preprint link (url) [BibTex]


no image
Dynamics of beneficial epidemics

Berdahl, A., Brelsford, C., De Bacco, C., Dumas, M., Ferdinand, V., Grochow, J. A., Hébert-Dufresne, L., Kallus, Y., Kempes, C. P., Kolchinsky, A., others,

arXiv preprint arXiv:1604.02096, 2016 (article)

pio

Preprint [BibTex]

Preprint [BibTex]


no image
Rare events statistics of random walks on networks: localisation and other dynamical phase transitions

De Bacco, C., Guggiola, A., Kühn, R., Paga, P.

Journal of Physics A: Mathematical and Theoretical, 49(18):184003, IOP Publishing, 2016 (article)

pio

Preprint link (url) [BibTex]

Preprint link (url) [BibTex]

2015


no image
Probabilistic Interpretation of Linear Solvers

Hennig, P.

SIAM Journal on Optimization, 25(1):234-260, 2015 (article)

ei pn

Web PDF link (url) DOI [BibTex]

2015


Web PDF link (url) DOI [BibTex]


no image
The average number of distinct sites visited by a random walker on random graphs

De Bacco, C., Majumdar, S. N., Sollich, P.

Journal of Physics A: Mathematical and Theoretical, 48(20):205004, IOP Publishing, 2015 (article)

pio

Preprint link (url) [BibTex]

Preprint link (url) [BibTex]


no image
The edge-disjoint path problem on random graphs by message-passing

Altarelli, F., Braunstein, A., Dall’Asta, L., De Bacco, C., Franz, S.

PloS one, 10(12):e0145222, Public Library of Science, 2015 (article)

pio

Code Preprint link (url) Project Page [BibTex]

Code Preprint link (url) Project Page [BibTex]


no image
Probabilistic numerics and uncertainty in computations

Hennig, P., Osborne, M. A., Girolami, M.

Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 471(2179), 2015 (article)

Abstract
We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equations, that return uncertainties in their calculations. Such uncertainties, arising from the loss of precision induced by numerical calculation with limited time or hardware, are important for much contemporary science and industry. Within applications such as climate science and astrophysics, the need to make decisions on the basis of computations with large and complex data have led to a renewed focus on the management of numerical uncertainty. We describe how several seminal classic numerical methods can be interpreted naturally as probabilistic inference. We then show that the probabilistic view suggests new algorithms that can flexibly be adapted to suit application specifics, while delivering improved empirical performance. We provide concrete illustrations of the benefits of probabilistic numeric algorithms on real scientific problems from astrometry and astronomical imaging, while highlighting open problems with these new algorithms. Finally, we describe how probabilistic numerical methods provide a coherent framework for identifying the uncertainty in calculations performed with a combination of numerical algorithms (e.g. both numerical optimizers and differential equation solvers), potentially allowing the diagnosis (and control) of error sources in computations.

ei pn

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Non-equilibrium statistical mechanics of the heat bath for two Brownian particles : Internal degrees of freedom found where there shouldn’t be (Special Issue on New Challenges in Complex Systems Science)

De Bacco, C., Baldovin, F., Orlandini, E.

理工研報告特集号 : ASTE : advances in science, technology and environmentology : special issue, 11, pages: 111-113, 早稲田大学理工学術院総合研究所 (理工学研究所), March 2015 (article)

pio

link (url) [BibTex]

link (url) [BibTex]

2013


Quasi-Newton Methods: A New Direction
Quasi-Newton Methods: A New Direction

Hennig, P., Kiefel, M.

Journal of Machine Learning Research, 14(1):843-865, March 2013 (article)

Abstract
Four decades after their invention, quasi-Newton methods are still state of the art in unconstrained numerical optimization. Although not usually interpreted thus, these are learning algorithms that fit a local quadratic approximation to the objective function. We show that many, including the most popular, quasi-Newton methods can be interpreted as approximations of Bayesian linear regression under varying prior assumptions. This new notion elucidates some shortcomings of classical algorithms, and lights the way to a novel nonparametric quasi-Newton method, which is able to make more efficient use of available information at computational cost similar to its predecessors.

ei ps pn

website+code pdf link (url) [BibTex]

2013


website+code pdf link (url) [BibTex]


no image
Analytical probabilistic modeling for radiation therapy treatment planning

Bangert, M., Hennig, P., Oelfke, U.

Physics in Medicine and Biology, 58(16):5401-5419, 2013 (article)

ei pn

PDF DOI [BibTex]

PDF DOI [BibTex]