diff options
| -rw-r--r-- | finale/sections/related.tex | 55 | ||||
| -rw-r--r-- | finale/sparse.bib | 8 |
2 files changed, 26 insertions, 37 deletions
diff --git a/finale/sections/related.tex b/finale/sections/related.tex index 59fb759..5f0289a 100644 --- a/finale/sections/related.tex +++ b/finale/sections/related.tex @@ -1,41 +1,22 @@ \paragraph{Related works.} The study of edge prediction in graphs has been an active field of research for -over a decade~\cite{Nowell08, Leskovec07, AdarA05}.~\cite{GomezRodriguez:2010} -introduced the {\scshape Netinf} algorithm, which approximates the likelihood of -cascades represented as a continuous process. The algorithm, relying on -Maximum-Likelihood and convex relaxations was improved in later -work~\cite{gomezbalduzzi:2011}, but is not known to have any theoretical -guarantees beside empirical validation on synthetic networks. +over a decade~\cite{Nowell08, Leskovec07, AdarA05}. MLE estimation (regularized +and un-regularized) for Graph Inference has been studied both for discrete-time +models \cite{Netrapalli:2012, pouget} and continous-time models +\cite{GomezRodriguez:2010, gomezbalduzzi:2011, Abrahao:13} -Let us denote by $m$ the number of nodes in the graph and for a given node, we -denote by $s$ its in-degree. In what follows, we call a \emph{recovery -guarantee} an upper bound on the number of observations required (expressed as -a function of $s$ and $m$) to learn the incoming edges of a node to a fixed -arbitrary accuracy level. \cite{Netrapalli:2012} studied the discrete-time -version of the independent cascade model and obtained the first ${\cal O}(s^2 -\log m)$ recovery guarantee on general networks, by using unregularized MLE and -making a {\it correlation decay\/} assumption, which limits the number of new -infections at every step. They also suggest a {\scshape Greedy} algorithm, -with provably good performance in tree graphs, which maintains a counter of -each node's antecedents, i.e. the set of nodes infected at a prior time step to -its infection time. The work of~\cite{Abrahao:13} studies the same -continuous-model framework as \cite{GomezRodriguez:2010} and obtains an ${\cal -O}(s^9 \log^2 s \log m)$ support recovery algorithm, without the -\emph{correlation decay} assumption. \cite{du2013uncover,Daneshmand:2014} -propose Lasso regularization of MLE for recovering the weights of the graph -under a continuous-time independent cascade model. The work -of~\cite{du2014influence} is slightly orthogonal to ours since they suggest -learning the \emph{influence} function, rather than the parameters of the -network directly. - -More recently, the continuous process studied in previous work has been -reformulated as a Hawkes process, with recent papers +More recently, the continuous-time processes studied in previous work have been +reformulated as a Hawkes processes, with recent papers \cite{linderman2014discovering, linderman2015scalable, simma2012modeling}, -focusing on Expectation-Maximization, and Variational Inference methods to learn -these processes. Because of the discrete nature of the GLC model, we hope to -bring a better understanding to the link between the properties of a graph and -its \emph{learnability}. We wish to further explore the idea of non-product -priors raised in \cite{linderman2014discovering, linderman2015scalable}, since -the experimental validation of their work focused on simple graph priors. -Finally, the \emph{Active Learning} formulation is, to the best of the -authors' knowledge, novel in this context. +focusing on Expectation-Maximization, Gibbs sampling and Variational Inference +methods. In comparison the discrete-time nature of the GLC model allows us to +scale the inference methods to larger graphs. Furthermore, while the works on +Hawkes processes exclusively used factorized graph priors, we hope that +Bayesian Inference for the GLC model will be able to accommodate more +expressive graph priors more easily. This is a direction we wish to explore in +future works. + +The Active Learning formulation is, to the best of the authors' knowledge, +novel in this context. The Active Learning approach of \cite{shababo} share +some similarities with ours even though their model is not, strictly speaking, +a cascade model (in particular, the time steps are independent). diff --git a/finale/sparse.bib b/finale/sparse.bib index 34e2a84..11e7c27 100644 --- a/finale/sparse.bib +++ b/finale/sparse.bib @@ -1,3 +1,11 @@ +@inproceedings{shababo, + title={Bayesian inference and online experimental design for mapping neural microcircuits}, + author={Shababo, Ben and Paige, Brooks and Pakman, Ari and Paninski, Liam}, + booktitle={Advances in Neural Information Processing Systems}, + pages={1304--1312}, + year={2013} +} + @article{pymc, title={PyMC: Bayesian stochastic modelling in Python}, author={Patil, Anand and Huard, David and Fonnesbeck, Christopher J}, |