43,386 research outputs found
Phase retrieval with a multivariate Von Mises prior: from a Bayesian formulation to a lifting solution
In this paper, we investigate a new method for phase recovery when prior
information on the missing phases is available. In particular, we propose to
take into account this information in a generic fashion by means of a
multivariate Von Mises dis- tribution. Building on a Bayesian formulation (a
Maximum A Posteriori estimation), we show that the problem can be expressed
using a Mahalanobis distance and be solved by a lifting optimization procedure.Comment: Preprint of the paper published in the proc. of ICASSP'1
Graphical Models and Symmetries : Loopy Belief Propagation Approaches
Whenever a person or an automated system has to reason in uncertain domains, probability theory is necessary. Probabilistic graphical models allow us to build statistical models that capture complex dependencies between random variables. Inference in these models, however, can easily become intractable. Typical ways to address this scaling issue are inference by approximate message-passing, stochastic gradients, and MapReduce, among others. Exploiting the symmetries of graphical models, however, has not yet been considered for scaling statistical machine learning applications. One instance of graphical models that are inherently symmetric are statistical relational models. These have recently gained attraction within the machine learning and AI communities and combine probability theory with first-order logic, thereby allowing for an efficient representation of structured relational domains. The provided formalisms to compactly represent complex real-world domains enable us to effectively describe large problem instances. Inference within and training of graphical models, however, have not been able to keep pace with the increased representational power. This thesis tackles two major aspects of graphical models and shows that both inference and training can indeed benefit from exploiting symmetries. It first deals with efficient inference exploiting symmetries in graphical models for various query types. We introduce lifted loopy belief propagation (lifted LBP), the first lifted parallel inference approach for relational as well as propositional graphical models. Lifted LBP can effectively speed up marginal inference, but cannot straightforwardly be applied to other types of queries. Thus we also demonstrate efficient lifted algorithms for MAP inference and higher order marginals, as well as the efficient handling of multiple inference tasks. Then we turn to the training of graphical models and introduce the first lifted online training for relational models. Our training procedure and the MapReduce lifting for loopy belief propagation combine lifting with the traditional statistical approaches to scaling, thereby bridging the gap between statistical relational learning and traditional statistical machine learning
Turning people into couch potatoes is not the cure for sports concussion
No abstract available
Recommended from our members
Neurons and symbols: a manifesto
We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of
neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty
On the Complexity and Approximation of Binary Evidence in Lifted Inference
Lifted inference algorithms exploit symmetries in probabilistic models to
speed up inference. They show impressive performance when calculating
unconditional probabilities in relational models, but often resort to
non-lifted inference when computing conditional probabilities. The reason is
that conditioning on evidence breaks many of the model's symmetries, which can
preempt standard lifting techniques. Recent theoretical results show, for
example, that conditioning on evidence which corresponds to binary relations is
#P-hard, suggesting that no lifting is to be expected in the worst case. In
this paper, we balance this negative result by identifying the Boolean rank of
the evidence as a key parameter for characterizing the complexity of
conditioning in lifted inference. In particular, we show that conditioning on
binary evidence with bounded Boolean rank is efficient. This opens up the
possibility of approximating evidence by a low-rank Boolean matrix
factorization, which we investigate both theoretically and empirically.Comment: To appear in Advances in Neural Information Processing Systems 26
(NIPS), Lake Tahoe, USA, December 201
Lifted Relax, Compensate and then Recover: From Approximate to Exact Lifted Probabilistic Inference
We propose an approach to lifted approximate inference for first-order
probabilistic models, such as Markov logic networks. It is based on performing
exact lifted inference in a simplified first-order model, which is found by
relaxing first-order constraints, and then compensating for the relaxation.
These simplified models can be incrementally improved by carefully recovering
constraints that have been relaxed, also at the first-order level. This leads
to a spectrum of approximations, with lifted belief propagation on one end, and
exact lifted inference on the other. We discuss how relaxation, compensation,
and recovery can be performed, all at the firstorder level, and show
empirically that our approach substantially improves on the approximations of
both propositional solvers and lifted belief propagation.Comment: Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty
in Artificial Intelligence (UAI2012
Classical Cryptographic Protocols in a Quantum World
Cryptographic protocols, such as protocols for secure function evaluation
(SFE), have played a crucial role in the development of modern cryptography.
The extensive theory of these protocols, however, deals almost exclusively with
classical attackers. If we accept that quantum information processing is the
most realistic model of physically feasible computation, then we must ask: what
classical protocols remain secure against quantum attackers?
Our main contribution is showing the existence of classical two-party
protocols for the secure evaluation of any polynomial-time function under
reasonable computational assumptions (for example, it suffices that the
learning with errors problem be hard for quantum polynomial time). Our result
shows that the basic two-party feasibility picture from classical cryptography
remains unchanged in a quantum world.Comment: Full version of an old paper in Crypto'11. Invited to IJQI. This is
authors' copy with different formattin
A Critical Look at Decentralized Personal Data Architectures
While the Internet was conceived as a decentralized network, the most widely
used web applications today tend toward centralization. Control increasingly
rests with centralized service providers who, as a consequence, have also
amassed unprecedented amounts of data about the behaviors and personalities of
individuals.
Developers, regulators, and consumer advocates have looked to alternative
decentralized architectures as the natural response to threats posed by these
centralized services. The result has been a great variety of solutions that
include personal data stores (PDS), infomediaries, Vendor Relationship
Management (VRM) systems, and federated and distributed social networks. And
yet, for all these efforts, decentralized personal data architectures have seen
little adoption.
This position paper attempts to account for these failures, challenging the
accepted wisdom in the web community on the feasibility and desirability of
these approaches. We start with a historical discussion of the development of
various categories of decentralized personal data architectures. Then we survey
the main ideas to illustrate the common themes among these efforts. We tease
apart the design characteristics of these systems from the social values that
they (are intended to) promote. We use this understanding to point out numerous
drawbacks of the decentralization paradigm, some inherent and others
incidental. We end with recommendations for designers of these systems for
working towards goals that are achievable, but perhaps more limited in scope
and ambition
- …