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Found 28 papers, 2 papers with code
Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement
We validate the superiority of the proposed method in our real-time high precision positioning system against several popular state-of-the-art robust regression methods.
Which Factorization Machine Modeling is Better: A Theoretical Answer with Optimal Guarantee
This bound is sub-optimal comparing to the information theoretical lower bound $\mathcal{O}(kd)$.
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component.
Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning
Ideally, parallel mini-batch SGD can achieve a linear speed-up of the training time (with respect to the number of workers) compared with SGD over a single worker.
Large-scale Distance Metric Learning with Uncertainty
Furthermore, we can show that the metric is learned from latent examples only, but it can preserve the large margin property even for the original data.
Learning with Non-Convex Truncated Losses by SGD
Learning with a {\it convex loss} function has been a dominating paradigm for many years.
Robust Optimization over Multiple Domains
Hence, we propose to learn the model and the adversarial distribution simultaneously with the stochastic algorithm for efficiency.
Multinomial Logit Bandit with Linear Utility Functions
In each round, the player selects a $K$-cardinality subset from $N$ candidate items, and receives a reward which is governed by a {\it multinomial logit} (MNL) choice model considering both item utility and substitution property among items.
Extremely Low Bit Neural Network: Squeeze the Last Bit Out with ADMM
Although deep learning models are highly effective for various learning tasks, their high computational costs prohibit the deployment to scenarios where either memory or computational resources are limited.
Similarity Learning via Adaptive Regression and Its Application to Image Retrieval
The similarity between pairs of images can be measured by the distances between their high dimensional representations, and the problem of learning the appropriate similarity is often addressed by distance metric learning.
Towards Making High Dimensional Distance Metric Learning Practical
In this work, we present a dual random projection frame for DML with high dimensional data that explicitly addresses the limitation of dimensionality reduction for DML.
An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection
In this paper, we consider the problem of column subset selection.
Theory of Dual-sparse Regularized Randomized Reduction
In this paper, we study randomized reduction methods, which reduce high-dimensional features into low-dimensional space by randomized methods (e. g., random projection, random hashing), for large-scale high-dimensional classification.
On Data Preconditioning for Regularized Loss Minimization
In this work, we study data preconditioning, a well-known and long-existing technique, for boosting the convergence of first-order methods for regularized loss minimization.
CUR Algorithm with Incomplete Matrix Observation
Our goal is to develop a low rank approximation algorithm, similar to CUR, based on (i) randomly sampled rows and columns from A, and (ii) randomly sampled entries from A.
Fine-Grained Visual Categorization via Multi-stage Metric Learning
To this end, we proposed a multi-stage metric learning framework that divides the large-scale high dimensional learning problem to a series of simple subproblems, achieving $\mathcal{O}(d)$ computational complexity.
Analysis of Distributed Stochastic Dual Coordinate Ascent
Extraordinary performances have been observed and reported for the well-motivated updates, as referred to the practical updates, compared to the naive updates.
Efficient Object Detection and Segmentation for Fine-Grained Recognition
The algorithm first detects low-level regions that could potentially belong to the object and then performs a full-object segmentation through propagation.
Stochastic gradient descent algorithms for strongly convex functions at O(1/T) convergence rates
With a weighting scheme proportional to t, a traditional stochastic gradient descent (SGD) algorithm achieves a high probability convergence rate of O({\kappa}/T) for strongly convex functions, instead of O({\kappa} ln(T)/T).
One-Pass AUC Optimization
AUC is an important performance measure and many algorithms have been devoted to AUC optimization, mostly by minimizing a surrogate convex loss on a training data set.
Efficient Distance Metric Learning by Adaptive Sampling and Mini-Batch Stochastic Gradient Descent (SGD)
Although stochastic gradient descent (SGD) has been successfully applied to improve the efficiency of DML, it can still be computationally expensive because in order to ensure that the solution is a PSD matrix, it has to, at every iteration, project the updated distance metric onto the PSD cone, an expensive operation.
Large Scale Strongly Supervised Ensemble Metric Learning, with Applications to Face Verification and Retrieval
Learning Mahanalobis distance metrics in a high- dimensional feature space is very difficult especially when structural sparsity and low rank are enforced to improve com- putational efficiency in testing phase.
Stochastic Gradient Descent with Only One Projection
Although many variants of stochastic gradient descent have been proposed for large-scale convex optimization, most of them require projecting the solution at {\it each} iteration to ensure that the obtained solution stays within the feasible domain.
Recovering the Optimal Solution by Dual Random Projection
Random projection has been widely used in data classification.
An Efficient Primal-Dual Prox Method for Non-Smooth Optimization
We study the non-smooth optimization problems in machine learning, where both the loss function and the regularizer are non-smooth functions.
Deep Coding Network
This paper proposes a principled extension of the traditional single-layer flat sparse coding scheme, where a two-layer coding scheme is derived based on theoretical analysis of nonlinear functional approximation that extends recent results for local coordinate coding.
Stochastic Relational Models for Large-scale Dyadic Data using MCMC
Stochastic relational models provide a rich family of choices for learning and predicting dyadic data between two sets of entities.
Predictive Matrix-Variate t Models
It is becoming increasingly important to learn from a partially-observed random matrix and predict its missing elements.
