132 research outputs found
An ADMM Based Framework for AutoML Pipeline Configuration
We study the AutoML problem of automatically configuring machine learning
pipelines by jointly selecting algorithms and their appropriate
hyper-parameters for all steps in supervised learning pipelines. This black-box
(gradient-free) optimization with mixed integer & continuous variables is a
challenging problem. We propose a novel AutoML scheme by leveraging the
alternating direction method of multipliers (ADMM). The proposed framework is
able to (i) decompose the optimization problem into easier sub-problems that
have a reduced number of variables and circumvent the challenge of mixed
variable categories, and (ii) incorporate black-box constraints along-side the
black-box optimization objective. We empirically evaluate the flexibility (in
utilizing existing AutoML techniques), effectiveness (against open source
AutoML toolkits),and unique capability (of executing AutoML with practically
motivated black-box constraints) of our proposed scheme on a collection of
binary classification data sets from UCI ML& OpenML repositories. We observe
that on an average our framework provides significant gains in comparison to
other AutoML frameworks (Auto-sklearn & TPOT), highlighting the practical
advantages of this framework
Joint Tensor Factorization and Outlying Slab Suppression with Applications
We consider factoring low-rank tensors in the presence of outlying slabs.
This problem is important in practice, because data collected in many
real-world applications, such as speech, fluorescence, and some social network
data, fit this paradigm. Prior work tackles this problem by iteratively
selecting a fixed number of slabs and fitting, a procedure which may not
converge. We formulate this problem from a group-sparsity promoting point of
view, and propose an alternating optimization framework to handle the
corresponding () minimization-based low-rank tensor
factorization problem. The proposed algorithm features a similar per-iteration
complexity as the plain trilinear alternating least squares (TALS) algorithm.
Convergence of the proposed algorithm is also easy to analyze under the
framework of alternating optimization and its variants. In addition,
regularization and constraints can be easily incorporated to make use of
\emph{a priori} information on the latent loading factors. Simulations and real
data experiments on blind speech separation, fluorescence data analysis, and
social network mining are used to showcase the effectiveness of the proposed
algorithm
Factor Models with Real Data: a Robust Estimation of the Number of Factors
Factor models are a very efficient way to describe high dimensional vectors
of data in terms of a small number of common relevant factors. This problem,
which is of fundamental importance in many disciplines, is usually reformulated
in mathematical terms as follows. We are given the covariance matrix Sigma of
the available data. Sigma must be additively decomposed as the sum of two
positive semidefinite matrices D and L: D | that accounts for the idiosyncratic
noise affecting the knowledge of each component of the available vector of data
| must be diagonal and L must have the smallest possible rank in order to
describe the available data in terms of the smallest possible number of
independent factors.
In practice, however, the matrix Sigma is never known and therefore it must
be estimated from the data so that only an approximation of Sigma is actually
available. This paper discusses the issues that arise from this uncertainty and
provides a strategy to deal with the problem of robustly estimating the number
of factors.Comment: arXiv admin note: text overlap with arXiv:1708.0040
Regularization approaches to hyperspectral unmixing
We consider a few different approaches to hyperspectral unmixing of remotely sensed imagery which exploit and extend recent advances in sparse statistical regularization, handling of constraints and dictionary reduction. Hyperspectral unmixing methods often use a conventional least-squares based lasso which assumes that the data follows the Gaussian distribution, we use this as a starting point. In addition, we consider a robust approach to sparse spectral unmixing of remotely sensed imagery which reduces the sensitivity of the estimator to outliers. Due to water absorption and atmospheric effects that affect data collection, hyperspectral images are prone to have large outliers. The framework comprises of several well-principled penalties. A non-convex, hyper-Laplacian prior is incorporated to induce sparsity in the number of active pure spectral components, and total variation regularizer is included to exploit the spatial-contextual information of hyperspectral images. Enforcing the sum-to-one and non-negativity constraint on the models parameters is essential for obtaining realistic estimates. We consider two approaches to account for this: an iterative heuristic renormalization and projection onto the positive orthant, and a reparametrization of the coefficients which gives rise to a theoretically founded method. Since the large size of modern spectral libraries cannot only present computational challenges but also introduce collinearities between regressors, we introduce a library reduction step. This uses the multiple signal classi fication (MUSIC) array processing algorithm, which both speeds up unmixing and yields superior results in scenarios where the library size is extensive. We show that although these problems are non-convex, they can be solved by a properly de fined algorithm based on either trust region optimization or iteratively reweighted least squares. The performance of the different approaches is validated in several simulated and real hyperspectral data experiments
Truthful and Faithful Monetary Policy for a Stablecoin Conducted by a Decentralised, Encrypted Artificial Intelligence
The Holy Grail of a decentralised stablecoin is achieved on rigorous
mathematical frameworks, obtaining multiple advantageous proofs: stability,
convergence, truthfulness, faithfulness, and malicious-security. These
properties could only be attained by the novel and interdisciplinary
combination of previously unrelated fields: model predictive control, deep
learning, alternating direction method of multipliers (consensus-ADMM),
mechanism design, secure multi-party computation, and zero-knowledge proofs.
For the first time, this paper proves:
- the feasibility of decentralising the central bank while securely
preserving its independence in a decentralised computation setting
- the benefits for price stability of combining mechanism design, provable
security, and control theory, unlike the heuristics of previous stablecoins
- the implementation of complex monetary policies on a stablecoin, equivalent
to the ones used by central banks and beyond the current fixed rules of
cryptocurrencies that hinder their price stability
- methods to circumvent the impossibilities of Guaranteed Output Delivery
(G.O.D.) and fairness: standing on truthfulness and faithfulness, we reach
G.O.D. and fairness under the assumption of rational parties
As a corollary, a decentralised artificial intelligence is able to conduct
the monetary policy of a stablecoin, minimising human intervention
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