296 research outputs found
Second-Order Kernel Online Convex Optimization with Adaptive Sketching
Kernel online convex optimization (KOCO) is a framework combining the
expressiveness of non-parametric kernel models with the regret guarantees of
online learning. First-order KOCO methods such as functional gradient descent
require only time and space per iteration, and, when the only
information on the losses is their convexity, achieve a minimax optimal
regret. Nonetheless, many common losses in kernel
problems, such as squared loss, logistic loss, and squared hinge loss posses
stronger curvature that can be exploited. In this case, second-order KOCO
methods achieve regret, which
we show scales as , where
is the effective dimension of the problem and is usually much smaller than
. The main drawback of second-order methods is their
much higher space and time complexity. In this paper, we
introduce kernel online Newton step (KONS), a new second-order KOCO method that
also achieves regret. To address the
computational complexity of second-order methods, we introduce a new matrix
sketching algorithm for the kernel matrix , and show that for
a chosen parameter our Sketched-KONS reduces the space and time
complexity by a factor of to space and
time per iteration, while incurring only times more regret
Maximum Optimality Margin: A Unified Approach for Contextual Linear Programming and Inverse Linear Programming
In this paper, we study the predict-then-optimize problem where the output of
a machine learning prediction task is used as the input of some downstream
optimization problem, say, the objective coefficient vector of a linear
program. The problem is also known as predictive analytics or contextual linear
programming. The existing approaches largely suffer from either (i)
optimization intractability (a non-convex objective function)/statistical
inefficiency (a suboptimal generalization bound) or (ii) requiring strong
condition(s) such as no constraint or loss calibration. We develop a new
approach to the problem called \textit{maximum optimality margin} which designs
the machine learning loss function by the optimality condition of the
downstream optimization. The max-margin formulation enjoys both computational
efficiency and good theoretical properties for the learning procedure. More
importantly, our new approach only needs the observations of the optimal
solution in the training data rather than the objective function, which makes
it a new and natural approach to the inverse linear programming problem under
both contextual and context-free settings; we also analyze the proposed method
under both offline and online settings, and demonstrate its performance using
numerical experiments.Comment: to be published in ICML 202
Multimodal non-linear latent semantic method for information retrieval
La búsqueda y recuperación de datos multimodales es una importante tarea dentro del campo de búsqueda y recuperación de información, donde las consultas y los elementos de la base de datos objetivo están representados por un conjunto de modalidades, donde cada una de ellas captura un aspecto de un fenómeno de interés. Cada modalidad contiene información complementaria y común a otras modalidades. Con el fin de tomar ventaja de
la información adicional distribuida a través de las distintas modalidades han sido desarrollados muchos algoritmos y métodos que utilizan las propiedades estadísticas en los datos multimodales para encontrar correlaciones implícitas, otros aprenden a calcular distancias heterogéneas, otros métodos aprenden a proyectar los datos desde el espacio de entrada hasta un espacio semántico común, donde las diferentes modalidades son comparables y se puede construir un ranking a partir de ellas. En esta tesis se presenta el diseño de un sistema para la búsqueda y recuperación de información multimodal que aprende varias proyecciones no lineales a espacios semánticos
latentes donde las distintas modalidades son representadas en conjunto y es posible realizar comparaciones y medidas de similitud para construir rankings multimodales. Adicionalmente se propone un método kernelizado para la proyección de datos a un espacio semántico latente usando la información de las etiquetas como método de supervisión para construir
índice multimodal que integra los datos multimodales y la información de las etiquetas; este método puede proyectar los datos a tres diferentes espacios semánticos donde varias configuraciones de búsqueda y recuperación de información pueden ser aplicadas. El sistema y el método propuestos fueron evaluados en un conjunto de datos compuesto por casos médicos, donde cada caso consta de una imagen de tejido prostático, un reporte de
texto del patólogo y un valor de Gleason score como etiqueta de supervisión. Combinando la información multimodal y la información en las etiquetas se generó un índice multimodal
que se utilizó para realizar la tarea de búsqueda y recuperación de información por contenido obteniendo resultados sobresalientes. Las proyecciones no-lineales permiten al modelo una mayor flexibilidad y capacidad de representación. Sin embargo calcular estas proyecciones no-lineales en un conjunto de datos enorme es computacionalmente costoso, para reducir este costo y habilitar el modelo para procesar datos a gran escala, la técnica del budget fue utilizada, mostrando un buen compromiso entre efectividad y velocidad.Multimodal information retrieval is an information retrieval sub-task where queries and database target elements are composed of several modalities or views. A modality is a representation of complex phenomena, captured and measured by different sensors or information sources, each one encodes some information about it. Each modality representation contains complementary and shared information about the phenomenon of interest,
this additional information can be used to improve the information retrieval process. Several methods have been developed to take advantage of additional information distributed across different modalities. Some of them exploit statistical properties in multimodal data to find correlations and implicit relationships, others learn heterogeneous distance functions, and others learn linear and non-linear projections that transform data from the original input space to a common latent semantic space where different modalities are comparable. In spite of the attention dedicated to this issue, multimodal information retrieval is still an open problem. This thesis presents a multimodal information retrieval system designed to learn several mapping functions to transform multimodal data to a latent semantic space, where different modalities are combined and can be compared to build a multimodal ranking and perform a multimodal information retrieval task. Additionally, a multimodal kernelized latent semantic embedding method is proposed to construct a supervised multimodal index, integrating
multimodal data and label supervision. This method can perform mappings to three different spaces where some information retrieval task setups can be performed.
The proposed system and method were evaluated in a multimodal medical case-based retrieval task where data is composed of whole-slide images of prostate tissue samples, pathologist’s text report and Gleason score as a supervised label. Multimodal data and labels were combined to produce a multimodal index. This index was used to retrieve multimodal information and achieves outstanding results compared with previous works on this topic. Non-linear mappings provide more flexibility and representation capacity to the proposed model. However, constructing the non-linear mapping in a large dataset using kernel methods can be computationally costly. To reduce the cost and allow large scale applications, the budget technique was introduced, showing good performance between speed and effectiveness.COLCIENCIASJóvenes investigadores 761/2016Línea de investigación: Ciencias de la computaciónMaestrí
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