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Distribution regression refers to the supervised learning problem where labels are only available for groups of inputs instead of individual inputs.
On the limitations of representing functions on sets
Wagstaff, E., Fuchs, F. B., Engelcke, M., Posner, I., and Osborne, M. (2019) · 1901
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Rep the set: Neural networks for learning set representations
Skianis, K., Nikolentzos, G., Limnios, S., and Vazirgiannis, M. (2019) · 1904
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Embedding and learning with signatures
Fermanian, A. (2019) · 1911
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Integration of paths, geometric invariants and a generalized baker-hausdorff formula
Chen, K. (1957) · 1957
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A unifying view of sparse approximate gaussian process regression
Quiñonero-Candela, J. and Rasmussen, C. E. (2005) · 1959
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Equilibrium thermodynamics
Adkins, C. J. and Adkins, C. J. (1983) · 1983
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An introduction to statistical thermodynamics
Hill, T. L. (1986) · 1986
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Statistical thermodynamics
Schrödinger, E. (1989) · 1989
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Support vector regression machines
Drucker, H., Burges, C. J., Kaufman, L., Smola, A. J., and Vapnik, V. (1997) · 1997
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Differential equations driven by rough signals
Lyons, T. J. (1998) · 1998
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Stochastic analysis of the fractional brownian motion
Decreusefond, L. et al. (1999) · 1999
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A modern course in statistical physics
Reichl, L. E. (1999) · 1999
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Mean reversion across national stock markets and parametric contrarian investment strategies
Balvers, R., Wu, Y., and Gilliland, E. (2000) · 2000
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Kidger, P. and Lyons, T. (2020) · 2001
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Problems in stochastic analysis: Connections between rough paths and non-commutative harmonic analysis
Fawcett, T. (2002) · 2002
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Overview of the radiometric and biophysical performance of the modis vegetation indices
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., and Ferreira, L. G. (2002) · 2002
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Ndvi derived sugarcane area identification and crop condition assessment
Rahman, M. R., Islam, A., and Rahman, M. A. (2004) · 2004
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The global land data assimilation system
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D. (2004) · 2004
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Computing the full signature kernel as the solution of a goursat problem
Cass, T., Lyons, T., Salvi, C., and Yang, W. (2020) · 2006
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A kernel for time series based on global alignments
Cuturi, M., Vert, J.-P., Birkenes, O., and Matsui, T. (2007) · 2007
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Differential equations driven by rough paths
Lyons, T. J., Caruana, M., and Lévy, T. (2007) · 2007
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Supervised learning by training on aggregate outputs
Musicant, D. R., Christensen, J. M., and Olson, J. F. (2007) · 2007
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A hilbert space embedding for distributions
Smola, A., Gretton, A., Song, L., and Schölkopf, B. (2007) · 2007
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Optimal transport: old and new
Villani, C. (2008) · 2008
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Multiple-instance regression with structured data
Wagstaff, K. L., Lane, T., and Roper, A. (2008) · 2008
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Universal kernels on non-standard input spaces
Christmann, A. and Steinwart, I. (2010) · 2010
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Coropa computational rough paths (software library)
et al, T. L. (2010) · 2010
Cited alongside, same era.
Multidimensional stochastic processes as rough paths: theory and applications
Friz, P. K. and Victoir, N. B. (2010) · 2010
Cited alongside, same era.
Soft-dtw: a differentiable loss function for time-series
Cuturi, M. and Blondel, M. (2017) · 2017
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Gpflow: A gaussian process library using tensorflow
De G. Matthews, A. G., Van Der Wilk, M., Nickson, T., Fujii, K., Boukouvalas, A., León-Villagrá, P., Ghahramani, Z., and Hensman, J. (2017) · 2017
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Time-series spectral dataset for croplands in france (2006–2017)
Hubert-Moy, L., Thibault, J., Fabre, E., Rozo, C., Arvor, D., Corpetti, T., and Rapinel, S. (2019) · 2017
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Bayesian approaches to distribution regression
Law, H. C. L., Sutherland, D. J., Sejdinovic, D., and Flaxman, S. (2017) · 2017
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Leveraging the path signature for skeleton-based human action recognition
Yang, W., Lyons, T., Ni, H., Schmid, C., Jin, L., and Chang, J. (2017) · 2017
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Uniqueness for the signature of a path of bounded variation and the reduced path group
Hambly, B. and Lyons, T. (2010) · 2010
Cited alongside, same era.
Application of vegetation indices for agricultural crop yield prediction using neural network techniques
Panda, S. S., Ames, D. P., and Panigrahi, S. (2010) · 2010
Cited alongside, same era.
Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011) · 2011
Cited alongside, same era.
Learning from distributions via support measure machines
Muandet, K., Fukumizu, K., Dinuzzo, F., and Schölkopf, B. (2012) · 2012
Cited alongside, same era.
The expected signature of a stochastic process
Ni, H. (2012) · 2012
Cited alongside, same era.
Sparse arrays of signatures for online character recognition
Graham, B. (2013) · 2013
Cited alongside, same era.
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Deep gaussian process for crop yield prediction based on remote sensing data
You, J., Li, X., Low, M., Lobell, D., and Ermon, S. (2017) · 2017
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Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J. (2017) · 2017
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A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder
Arribas, I. P., Goodwin, G. M., Geddes, J. R., Lyons, T., and Saunders, K. E. (2018) · 2018
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Signature moments to characterize laws of stochastic processes
Chevyrev, I. and Oberhauser, H. (2018) · 2018
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Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration
Gardner, J., Pleiss, G., Weinberger, K. Q., Bindel, D., and Wilson, A. G. (2018) · 2018
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Volatility is rough
Gatheral, J., Jaisson, T., and Rosenbaum, M. (2018) · 2018
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Variational learning on aggregate outputs with gaussian processes
Law, H. C., Sejdinovic, D., Cameron, E., Lucas, T., Flaxman, S., Battle, K., and Fukumizu, K. (2018) · 2018
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The iisignature library: efficient calculation of iterated-integral signatures and log signatures
Reizenstein, J. and Graham, B. (2018) · 2018
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Deep signature transforms
Bonnier, P., Kidger, P., Perez Arribas, I., Salvi, C., and Lyons, T. (2019) · 2019
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A course in functional analysis
Conway, J. B. (2019) · 2019
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Multi-resolution multi-task gaussian processes
Hamelijnck, O., Damoulas, T., Wang, K., and Girolami, M. (2019) · 2019
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Kernels for sequentially ordered data
Király, F. J. and Oberhauser, H. (2019) · 2019
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Using path signatures to predict a diagnosis of alzheimer’s disease
Moore, P., Lyons, T., Gallacher, J., Initiative, A. D. N., et al. (2019) · 2019
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Sig-sdes model for quantitative finance
Arribas, I. P., Salvi, C., and Szpruch, L. (2020) · 2020
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Optimal execution with rough path signatures
Kalsi, J., Lyons, T., and Arribas, I. P. (2020) · 2020
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Parameter estimation for rough differential equations
Papavasiliou, A., Ladroue, C., et al. (2011) · 2073
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