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Variational Inference over Combinatorial Spaces
2010
Neural Information Processing Systems
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We propose a new framework that extends variational inference to a wide range of combinatorial spaces. ...
Because of this, in applications to combinatorial spaces simple exact models are often preferred to more complex models that require approximate inference [4] . ...
There has been work on applying variational algorithms to the problem of maximization over combinatorial spaces [11, 12, 13, 14] , but maximization over combinatorial spaces is rather different than summation ...
dblp:conf/nips/Bouchard-CoteJ10
fatcat:hl7jp7pt5vdv3h22gjjmlbahke
Algorithmic approaches to clonal reconstruction in heterogeneous cell populations
2019
Quantitative Biology
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We categorize these methods based on the type of input data that they use (space-resolved or time-resolved), and also based on their computational formulation as either combinatorial or probabilistic. ...
In this paper, we review the theoretical framework and assumptions over which the clonal reconstruction problem is formulated. ...
It uses Bayesian inference based on Markov Chain Monte Carlo (MCMC) sampling algorithm to infer a distribution over phylogenies, where the Dirichlet distribution over all potential phylogenies is used ...
doi:10.1007/s40484-019-0188-3
pmid:32431959
pmcid:PMC7236794
fatcat:rcigo5p6evbylh2s2jh4xshpcu
Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inference
[article]
2021
arXiv
pre-print
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We introduce Variational Combinatorial Sequential Monte Carlo (VCSMC), a powerful framework that establishes variational sequential search to learn distributions over intricate combinatorial structures ...
Bayesian phylogenetic inference is often conducted via local or sequential search over topologies and branch lengths using algorithms such as random-walk Markov chain Monte Carlo (MCMC) or Combinatorial ...
VARIATIONAL COMBINATORIAL SEQUENTIAL MONTE CARLO Variational Objective. ...
arXiv:2106.00075v2
fatcat:jnz5etloqzawxfcaqm5cwswvfq
Large-scale learning of combinatorial transcriptional dynamics from gene expression
2011
Computer applications in the biosciences : CABIOS
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Results: We present a novel method to infer combinatorial regulation of gene expression by multiple transcription factors in largescale transcriptional regulatory networks. ...
Direct experimental measurement of TFs' activities is, however, challenging, resulting in a need to develop statistical tools to infer TF activities from mRNA expression levels of target genes. ...
We then ran the variational EM algorithm to infer the posterior probabilities over TF states and gene-specific parameters, and compared with the true parameter values/ TF states. ...
doi:10.1093/bioinformatics/btr113
pmid:21367870
fatcat:j7mvcqjfsndk3hb3xo353l6xz4
Combinatorial Markov Random Fields
[chapter]
2006
Lecture Notes in Computer Science
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A combinatorial random variable is a discrete random variable defined over a combinatorial set (e.g., a power set of a given set). ...
Since it can be problematic to apply existing inference techniques for graphical models to Comrafs, we design two simple and efficient inference algorithms specific for Comrafs, which are based on combinatorial ...
Finally, defineX c to be a combinatorial r.v. with the event space X c . ...
doi:10.1007/11871842_8
fatcat:62utnn6p45h37bimgl3bcjsnni
Provable Variational Inference for Constrained Log-Submodular Models
2018
Neural Information Processing Systems
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To perform inference in these models we design novel variational inference algorithms, which carefully leverage the combinatorial and probabilistic properties of these objects. ...
These capture log-submodular dependencies of arbitrary order between the variables, but also satisfy hard combinatorial constraints. ...
Bouchard-Côté and Jordan [5] introduce a class of variational techniques over combinatorial spaces, but they make a different set of assumptions -they assume a product space and models that are tractable ...
dblp:conf/nips/DjolongaJ018
fatcat:ag4xpvgvzrh4jnx5o4xckcoyc4
Probabilistic Subgraph Matching Based on Convex Relaxation
[chapter]
2005
Lecture Notes in Computer Science
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close-to-optimal combinatorial matchings within the original solution space. ...
We show that the global minimum of the relaxed convex problem can be interpreted as probability distribution over the original space of matching matrices, providing a basis for efficiently sampling all ...
the original combinatorial solution space. ...
doi:10.1007/11585978_12
fatcat:un4ojokbnnb47limhmm5h727p4
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
[article]
2021
arXiv
pre-print
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To this end, we introduce a parametric variational family modelled by an autoregressive distribution over the space of discrete DAGs. ...
Aiming to overcome this issue, we propose a form of variational inference over the graphs of Structural Causal Models (SCMs). ...
the combinatorial nature of the space of DAGs [15] . ...
arXiv:2106.07635v1
fatcat:rjiotac6nvhlla6tnv2caoncia
Sequence to Better Sequence: Continuous Revision of Combinatorial Structures
2017
International Conference on Machine Learning
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To avoid combinatorial-search over sequence elements, we specify a generative model with continuous latent factors, which is learned via joint approximate inference using a recurrent variational autoencoder ...
Under this model, gradient methods can be used to efficiently optimize the continuous latent factors with respect to inferred outcomes. ...
By exploiting such simplified geometry, a basic shift in the latent vector space may be able to produce higher-quality revisions than attempts to directly manipulate the combinatorial space of sequence ...
dblp:conf/icml/MuellerGJ17
fatcat:acldrozqvbd6fnqogrhhefwctu
Space in weak propositional proof systems
2016
Bulletin of the European Association for Theoretical Computer Science
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This year's award went to Ilario Bonacina for his thesis Space in weak propositional proof systems, which was supervised by Nicola Galesi at the University of Rome "La Sapienza". ...
Ilario's thesis contributes to a classic and deep topic in theoretical computer science, and settles natural questions on the space complexity of proofs using Resolution and the Polynomial Calculus that ...
This technical construction relies on some combinatorial games over bipartite graphs, the Cover Games, and to some variations of Hall's theorem to objects similar to matchings, V-matchings and VW-matchings ...
dblp:journals/eatcs/Bonacina16
fatcat:7oxgzcxtdnf4bfpncyd27bhshe
Novelty and imitation within the brain: a Darwinian neurodynamic approach to combinatorial problems
2021
Scientific Reports
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AbstractEfficient search in vast combinatorial spaces, such as those of possible action sequences, linguistic structures, or causal explanations, is an essential component of intelligence. ...
We demonstrate that the emerging Darwinian population of readout activity patterns is capable of maintaining and continually improving upon existing solutions over rugged combinatorial reward landscapes ...
Mutations generate variation through which the space of possible solutions is explored. ...
doi:10.1038/s41598-021-91489-5
pmid:34131159
pmcid:PMC8206098
fatcat:hwugkfhghvgelbljb5uecmjuea
CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps
[article]
2018
arXiv
pre-print
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To tackle this issue, we propose a simple but effective algorithm, combinatorial partitioning, which generates a large number of fine-grained output classes by intersecting multiple coarse-grained partitionings ...
In terms of space complexity, classification based methods definitely have great advantages over retrieval based ones, which need to maintain the entire image database. ...
Moreover, our model, like other classification-based approaches, requires much less space than the retrieval-based models for inference. ...
arXiv:1808.02130v1
fatcat:pd4x6iaknndoxkkzui3ltqs43m
Pantypes: Diverse Representatives for Self-Explainable Models
[article]
2024
arXiv
pre-print
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These classifiers are designed to incorporate high transparency in their decisions by basing inference on similarity with learned prototypical objects. ...
We show that pantypes can empower prototypical self-explainable models by occupying divergent regions of the latent space and thus fostering high diversity, interpretability and fairness. ...
The values are the mean and standard deviation over three runs. ...
arXiv:2403.09383v1
fatcat:d5bjfmtdyneylmpmtrdey4hgsa
Directional Statistics on Permutations
[article]
2010
arXiv
pre-print
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As a demonstration of the benefits of the framework we derive an inference procedure for a state-space model over permutations. We demonstrate the approach with applications. ...
It makes the direct definition of a multinomial distribution over permutation space impractical for all but a very small n. In this work we propose an embedding of all n! ...
-Practical results: we develop efficient inference over permutations in a state-space model. * We employ analytical product and marginalization operations. * We show efficient transformation of partially ...
arXiv:1007.2450v1
fatcat:6llt3gmeavcwxjap4r2hnky2vi
Compact Rule Extraction for Hierarchical Phrase-based Translation
2020
Conference of the Association for Machine Translation in the Americas
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The first is a combinatorial optimization approach and the second is a Bayesian model over Hiero grammars using Variational Bayes for inference. ...
We use Variational Bayes (VB) for inference. ...
Variational Inference for our Model We now describe the Variational inference procedure for our model explained earlier in Section 4.1. ...
dblp:conf/amta/SankaranHS20
fatcat:q7l4dzmszjcwlctfjfoyy2vere
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