504 research outputs found
Bayesian multitask inverse reinforcement learning
We generalise the problem of inverse reinforcement learning to multiple
tasks, from multiple demonstrations. Each one may represent one expert trying
to solve a different task, or as different experts trying to solve the same
task. Our main contribution is to formalise the problem as statistical
preference elicitation, via a number of structured priors, whose form captures
our biases about the relatedness of different tasks or expert policies. In
doing so, we introduce a prior on policy optimality, which is more natural to
specify. We show that our framework allows us not only to learn to efficiently
from multiple experts but to also effectively differentiate between the goals
of each. Possible applications include analysing the intrinsic motivations of
subjects in behavioural experiments and learning from multiple teachers.Comment: Corrected version. 13 pages, 8 figure
Advances on Matroid Secretary Problems: Free Order Model and Laminar Case
The most well-known conjecture in the context of matroid secretary problems
claims the existence of a constant-factor approximation applicable to any
matroid. Whereas this conjecture remains open, modified forms of it were shown
to be true, when assuming that the assignment of weights to the secretaries is
not adversarial but uniformly random (Soto [SODA 2011], Oveis Gharan and
Vondr\'ak [ESA 2011]). However, so far, there was no variant of the matroid
secretary problem with adversarial weight assignment for which a
constant-factor approximation was found. We address this point by presenting a
9-approximation for the \emph{free order model}, a model suggested shortly
after the introduction of the matroid secretary problem, and for which no
constant-factor approximation was known so far. The free order model is a
relaxed version of the original matroid secretary problem, with the only
difference that one can choose the order in which secretaries are interviewed.
Furthermore, we consider the classical matroid secretary problem for the
special case of laminar matroids. Only recently, a constant-factor
approximation has been found for this case, using a clever but rather involved
method and analysis (Im and Wang, [SODA 2011]) that leads to a
16000/3-approximation. This is arguably the most involved special case of the
matroid secretary problem for which a constant-factor approximation is known.
We present a considerably simpler and stronger -approximation, based on reducing the problem to a matroid secretary
problem on a partition matroid
Possible liquid immiscibility textures in high-magnesia basalts from the Ventersdorp Supergroup, South Africa
The lowermost succession of lavas in the Proterozoic Ventersdorp Supergroup contains light weathering ocelli up to 15 cm in diameter which occur in layers of a darker weathering volcanic material. Some ocelli appear to merge, and discrete light weathering layers may be the ultimate end-stage of this coalescence. Alternatively, coexisting magmas in the neck of the volcano may have been erupted in varying proportions, and turbulence during flow caused spalling of large drops of the lighter weathering material into the other. Several lines of field evidence suggest that two distinct liquids coexisted and were rapidly quenched after eruption. Chemical data for ocelli and matrix are consistent with the hypothesis of liquid immiscibility. The differences in compositions between the coexisting pairs of liquids are small and it is suggested that the original magmas must have been close to the consulute composition
Constrained Non-Monotone Submodular Maximization: Offline and Secretary Algorithms
Constrained submodular maximization problems have long been studied, with
near-optimal results known under a variety of constraints when the submodular
function is monotone. The case of non-monotone submodular maximization is less
understood: the first approximation algorithms even for the unconstrainted
setting were given by Feige et al. (FOCS '07). More recently, Lee et al. (STOC
'09, APPROX '09) show how to approximately maximize non-monotone submodular
functions when the constraints are given by the intersection of p matroid
constraints; their algorithm is based on local-search procedures that consider
p-swaps, and hence the running time may be n^Omega(p), implying their algorithm
is polynomial-time only for constantly many matroids. In this paper, we give
algorithms that work for p-independence systems (which generalize constraints
given by the intersection of p matroids), where the running time is poly(n,p).
Our algorithm essentially reduces the non-monotone maximization problem to
multiple runs of the greedy algorithm previously used in the monotone case.
Our idea of using existing algorithms for monotone functions to solve the
non-monotone case also works for maximizing a submodular function with respect
to a knapsack constraint: we get a simple greedy-based constant-factor
approximation for this problem.
With these simpler algorithms, we are able to adapt our approach to
constrained non-monotone submodular maximization to the (online) secretary
setting, where elements arrive one at a time in random order, and the algorithm
must make irrevocable decisions about whether or not to select each element as
it arrives. We give constant approximations in this secretary setting when the
algorithm is constrained subject to a uniform matroid or a partition matroid,
and give an O(log k) approximation when it is constrained by a general matroid
of rank k.Comment: In the Proceedings of WINE 201
Bayesian nonparametric models for name disambiguation and supervised learning
This thesis presents new Bayesian nonparametric models and approaches for their development,
for the problems of name disambiguation and supervised learning. Bayesian
nonparametric methods form an increasingly popular approach for solving problems
that demand a high amount of model flexibility. However, this field is relatively new,
and there are many areas that need further investigation. Previous work on Bayesian
nonparametrics has neither fully explored the problems of entity disambiguation and
supervised learning nor the advantages of nested hierarchical models. Entity disambiguation
is a widely encountered problem where different references need to be linked
to a real underlying entity. This problem is often unsupervised as there is no previously
known information about the entities. Further to this, effective use of Bayesian
nonparametrics offer a new approach to tackling supervised problems, which are frequently
encountered.
The main original contribution of this thesis is a set of new structured Dirichlet process
mixture models for name disambiguation and supervised learning that can also
have a wide range of applications. These models use techniques from Bayesian statistics,
including hierarchical and nested Dirichlet processes, generalised linear models,
Markov chain Monte Carlo methods and optimisation techniques such as BFGS. The
new models have tangible advantages over existing methods in the field as shown with
experiments on real-world datasets including citation databases and classification and
regression datasets.
I develop the unsupervised author-topic space model for author disambiguation that
uses free-text to perform disambiguation unlike traditional author disambiguation approaches.
The model incorporates a name variant model that is based on a nonparametric
Dirichlet language model. The model handles both novel unseen name variants and
can model the unknown authors of the text of the documents. Through this, the model
can disambiguate authors with no prior knowledge of the number of true authors in the
dataset. In addition, it can do this when the authors have identical names.
I use a model for nesting Dirichlet processes named the hybrid NDP-HDP. This
model allows Dirichlet processes to be clustered together and adds an additional level of
structure to the hierarchical Dirichlet process. I also develop a new hierarchical extension
to the hybrid NDP-HDP. I develop this model into the grouped author-topic model
for the entity disambiguation task. The grouped author-topic model uses clusters to model the co-occurrence of entities in documents, which can be interpreted as research
groups. Since this model does not require entities to be linked to specific words in a
document, it overcomes the problems of some existing author-topic models. The model
incorporates a new method for modelling name variants, so that domain-specific name
variant models can be used.
Lastly, I develop extensions to supervised latent Dirichlet allocation, a type of supervised
topic model. The keyword-supervised LDA model predicts document responses
more accurately by modelling the effect of individual words and their contexts directly.
The supervised HDP model has more model flexibility by using Bayesian nonparametrics
for supervised learning. These models are evaluated on a number of classification
and regression problems, and the results show that they outperform existing supervised
topic modelling approaches. The models can also be extended to use similar information
to the previous models, incorporating additional information such as entities and
document titles to improve prediction
ASTEC -- the Aarhus STellar Evolution Code
The Aarhus code is the result of a long development, starting in 1974, and
still ongoing. A novel feature is the integration of the computation of
adiabatic oscillations for specified models as part of the code. It offers
substantial flexibility in terms of microphysics and has been carefully tested
for the computation of solar models. However, considerable development is still
required in the treatment of nuclear reactions, diffusion and convective
mixing.Comment: Astrophys. Space Sci, in the pres
The stone adze and obsidian assemblage from the Talasiu site, Kingdom of Tonga
Typological and geochemical analyses of stone adzes and other stone tools have played a significant role in identifying directionality of colonisation movements in early migratory events in the Western Pacific. In later phases of Polynesian prehistory, stone adzes are important status goods which show substantial spatial and temporal variation. However, there is a debate when standardisation of form and manufacture appeared, whether it can be seen in earliest populations colonising the Pacific or whether it is a later development. We present in this paper a stone adze and obsidian tool assemblage from an early Ancestral Polynesian Society Talasiu site on Tongatapu, Kingdom of Tonga. The site shows a wide variety of adze types; however, if raw material origin is taken into account, emerging standardisation in adze form might be detected. We also show that Tongatapu was strongly connected in a network of interaction to islands to the North, particularly Samoa, suggesting that these islands had permanent populations
Measurements of the Branching Fractions and Helicity Amplitudes in B --> D* rho Decays
Using 9.1 fb-1 of e+ e- data collected at the Upsilon(4S) with the CLEO
detector using the Cornell Electron Storage Ring, measurements are reported for
both the branching fractions and the helicity amplitudes for the decays B- ->
D*0 rho- and B0bar -> D*+ rho-. The fraction of longitudinal polarization in
B0bar -> D*+ rho- is found to be consistent with that in B0bar -> D*+ l- nubar
at q^2 = M^2_rho, indicating that the factorization approximation works well.
The longitudinal polarization in the B- mode is similar. The measurements also
show evidence of non-trivial final-state interaction phases for the helicity
amplitudes.Comment: 11 pages postscript, also available through
http://w4.lns.cornell.edu/public/CLNS, submitted to PR
- …