The split-and-drift random graph, a null model for speciation

We introduce a new random graph model motivated by biological questions relating to speciation. This random graph is defined as the stationary distribution of a Markov chain on the space of graphs on $\{1, \ldots, n\}$. The dynamics of this Markov chain is governed by two types of events: vertex duplication, where at constant rate a pair of vertices is sampled uniformly and one of these vertices loses its incident edges and is rewired to the other vertex and its neighbors; and edge removal, where each edge disappears at constant rate. Besides the number of vertices $n$, the model has a single parameter $r_n$. Using a coalescent approach, we obtain explicit formulas for the first moments of several graph invariants such as the number of edges or the number of complete subgraphs of order $k$. These are then used to identify five non-trivial regimes depending on the asymptotics of the parameter $r_n$. We derive an explicit expression for the degree distribution, and show that under appropriate rescaling it converges to classical distributions when the number of vertices goes to infinity. Finally, we give asymptotic bounds for the number of connected components, and show that in the sparse regime the number of edges is Poissonian.Comment: added Proposition 2.4 and formal proofs of Proposition 2.3 and 2.

Moving towards appropriability of academic knowledge: a post-actionalist perspective

Based on recent contributions in managerial research, this article aims to suggest a new perspective for appraising and developing knowledge usability by studying the processes underlying its production: appropriation. The underlying problem is the following: how can the academic community help a community of practitioners appropriate knowledge it produced, co-produced or stimulated? First, a preliminary analysis is put forward as regards management sciences and the concept of knowledge 'actionability'. Some limitations are raised (1.). Then, the authors suggest to move from an 'actionability' (rather coherent with a classic vision of management sciences linked to the "sciences of the artificial") to an 'appropriability' perspective (2.). Lastly, the specificities of both perspectives are discussed (3.). Some limitations of this new vision are also pointed out, especially from a psychological standpoint.Management sciences; actionable knowledge; appropriability of knowledge; epistemology; methodology

A New PAC-Bayesian Perspective on Domain Adaptation

We study the issue of PAC-Bayesian domain adaptation: We want to learn, from a source domain, a majority vote model dedicated to a target one. Our theoretical contribution brings a new perspective by deriving an upper-bound on the target risk where the distributions' divergence---expressed as a ratio---controls the trade-off between a source error measure and the target voters' disagreement. Our bound suggests that one has to focus on regions where the source data is informative.From this result, we derive a PAC-Bayesian generalization bound, and specialize it to linear classifiers. Then, we infer a learning algorithmand perform experiments on real data.Comment: Published at ICML 201

PAC-Bayes and Domain Adaptation

We provide two main contributions in PAC-Bayesian theory for domain adaptation where the objective is to learn, from a source distribution, a well-performing majority vote on a different, but related, target distribution. Firstly, we propose an improvement of the previous approach we proposed in Germain et al. (2013), which relies on a novel distribution pseudodistance based on a disagreement averaging, allowing us to derive a new tighter domain adaptation bound for the target risk. While this bound stands in the spirit of common domain adaptation works, we derive a second bound (introduced in Germain et al., 2016) that brings a new perspective on domain adaptation by deriving an upper bound on the target risk where the distributions' divergence-expressed as a ratio-controls the trade-off between a source error measure and the target voters' disagreement. We discuss and compare both results, from which we obtain PAC-Bayesian generalization bounds. Furthermore, from the PAC-Bayesian specialization to linear classifiers, we infer two learning algorithms, and we evaluate them on real data.Comment: Neurocomputing, Elsevier, 2019. arXiv admin note: substantial text overlap with arXiv:1503.0694

PAC-Bayesian Learning and Domain Adaptation

In machine learning, Domain Adaptation (DA) arises when the distribution gen- erating the test (target) data differs from the one generating the learning (source) data. It is well known that DA is an hard task even under strong assumptions, among which the covariate-shift where the source and target distributions diverge only in their marginals, i.e. they have the same labeling function. Another popular approach is to consider an hypothesis class that moves closer the two distributions while implying a low-error for both tasks. This is a VC-dim approach that restricts the complexity of an hypothesis class in order to get good generalization. Instead, we propose a PAC-Bayesian approach that seeks for suitable weights to be given to each hypothesis in order to build a majority vote. We prove a new DA bound in the PAC-Bayesian context. This leads us to design the first DA-PAC-Bayesian algorithm based on the minimization of the proposed bound. Doing so, we seek for a \rho-weighted majority vote that takes into account a trade-off between three quantities. The first two quantities being, as usual in the PAC-Bayesian approach, (a) the complexity of the majority vote (measured by a Kullback-Leibler divergence) and (b) its empirical risk (measured by the \rho-average errors on the source sample). The third quantity is (c) the capacity of the majority vote to distinguish some structural difference between the source and target samples.Comment: https://sites.google.com/site/multitradeoffs2012

On Probability Distributions for Trees: Representations, Inference and Learning

We study probability distributions over free algebras of trees. Probability distributions can be seen as particular (formal power) tree series [Berstel et al 82, Esik et al 03], i.e. mappings from trees to a semiring K . A widely studied class of tree series is the class of rational (or recognizable) tree series which can be defined either in an algebraic way or by means of multiplicity tree automata. We argue that the algebraic representation is very convenient to model probability distributions over a free algebra of trees. First, as in the string case, the algebraic representation allows to design learning algorithms for the whole class of probability distributions defined by rational tree series. Note that learning algorithms for rational tree series correspond to learning algorithms for weighted tree automata where both the structure and the weights are learned. Second, the algebraic representation can be easily extended to deal with unranked trees (like XML trees where a symbol may have an unbounded number of children). Both properties are particularly relevant for applications: nondeterministic automata are required for the inference problem to be relevant (recall that Hidden Markov Models are equivalent to nondeterministic string automata); nowadays applications for Web Information Extraction, Web Services and document processing consider unranked trees

Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning

International audienceLearning probabilistic models over strings is an important issue for many applications. Spectral methods propose elegant solutions to the problem of inferring weighted automata from finite samples of variable-length strings drawn from an unknown target distribution $p$. These methods rely on a singular value decomposition of a matrix $\v{H}_S$, called the empirical Hankel matrix, that records the frequencies of (some of) the observed strings $S$. The accuracy of the learned distribution depends both on the quantity of information embedded in $\v{H}_S$ and on the distance between $\v{H}_S$ and its mean $\v{H}_p$. Existing concentration bounds seem to indicate that the concentration over $\v{H}_p$ gets looser with its dimensions, suggesting that it might be necessary to bound the dimensions of $\v{H}_S$ for learning. We prove new dimension-free concentration bounds for classical Hankel matrices and several variants, based on prefixes or factors of strings, that are useful for learning. Experiments demonstrate that these bounds are tight and that they significantly improve existing (dimension-dependent) bounds. One consequence of these results is that the spectral learning approach remains consistent even if all the observations are recorded within the empirical matrix

An electronic ratchet is required in nanostructured intermediate band solar cells

We investigate in this letter the intrinsic properties that have limited the efficiency of nanostructured intermediate band solar cells. Those devices take advantage of intra-band transitions, which occur on narrow energy width, and present low radiative recombination efficiency. We derive the minimum requirements in terms of those two characteristics to achieve efficiencies in excess of the Shockley-Queisser limit, and show that compatible nanostructures are challenging to obtain. Especially, we evidence that currently experimentally considered materials cannot overcome the best single junction cells. In order to solve those issues, we consider devices including an electronic ratchet mechanism. Firstly, such devices are shown to be much less sensitive on the limitations of the nanostructures characteristics, so that requirements for high efficiencies can be met. Secondly, we show that quantum well devices present advantages over their quantum dots counterparts, although they have attracted much less interest so far

Learning advanced mathematical computations from examples

Using transformers over large generated datasets, we train models to learn mathematical properties of differential systems, such as local stability, behavior at infinity and controllability. We achieve near perfect prediction of qualitative characteristics, and good approximations of numerical features of the system. This demonstrates that neural networks can learn to perform complex computations, grounded in advanced theory, from examples, without built-in mathematical knowledge