arXiv research

Run2Survive uses survival analysis for algorithm selection, outperforming traditional methods.

Optimal algorithms learn Gaussian trees and polytrees from data.

The paper proposes a method to predict the performance of data-driven algorithms using surrogate models.

Responds to comments on Bayesian Logic Regression algorithm, provides extensions and tutorial.

There is no free lunch, no single learning algorithm that will outperform other algorithms on all data. In practice different approaches are tried and the best algorithm selected. An alternative solution is to build new algorithms on demand by creating a framework that accommodates many algorithms. The best combination…

HASSO improves SO algorithms by dynamically tuning hyperparameters.

Gradient boosting method enforced with individual fairness.

New algorithms minimize regret with global costs in online learning.

Study shows online learning algorithms incentivize low-quality content, proposing new algorithms to improve quality.

In binary classification problems, mainly two approaches have been proposed; one is loss function approach and the other is uncertainty set approach. The loss function approach is applied to major learning algorithms such as support vector machine (SVM) and boosting methods. The loss function represents the penalty of …

New algorithms solve inverse problems using deep learning, converging faster than traditional methods.

The traditional approach of hand-crafting priors (such as sparsity) for solving inverse problems is slowly being replaced by the use of richer learned priors (such as those modeled by generative adversarial networks, or GANs). In this work, we study the algorithmic aspects of such a learning-based approach from a theor…

A new approach for feature extraction from time series is proposed in this paper. This approach consists of a specific regression model incorporating a discrete hidden logistic process. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. …

Bayesian model compares ML algorithms on various datasets.

A simple approach to offline RL without additional complexity.

A new k-means algorithm using cover trees accelerates clustering.

While neural networks have achieved vastly enhanced performance over traditional iterative methods in many cases, they are generally empirically designed and the underlying structures are difficult to interpret. The algorithm unrolling approach has helped connect iterative algorithms to neural network architectures. Ho…

A new CA-GAN architecture improves minority class data generation in health datasets.

Paper develops Byzantine-resilient algorithms for decentralized learning.

We simplify deep learning convergence analysis using basic math.

Transforms offline greedy algorithms to online algorithms for combinatorial problems.

New approach for algorithms that learn predictors to improve performance.

Meta-algorithm selection aims to choose the best algorithm selector for a given problem instance.

The Min-Hashing approach to sketching has become an important tool in data analysis, information retrial, and classification. To apply it to real-valued datasets, the ICWS algorithm has become a seminal approach that is widely used, and provides state-of-the-art performance for this problem space. However, ICWS suffers…

New algorithms improve dueling bandit performance in multiplayer settings.

Balances and eliminates base algorithms in bandits and RL to bound total regret.

We consider the problem of multi-class classification and a stochastic opti- mization approach to it. We derive risk bounds for stochastic mirror descent algorithm and provide examples of set geometries that make the use of the algorithm efficient in terms of error in k.

Study analyzes Bayesian inference algorithms using dynamical functional approach.

A meta-UCB method combines stochastic bandit algorithms.

New algorithms for sampling and optimization without tuning.

New approach to algorithmic fairness for human-AI collaboration considers compliance with human decisions.

The book explores alternatives to worst-case analysis for algorithm performance.

We present arguments for the formulation of unified approach to different standard continuous inference methods from partial information. It is claimed that an explicit partition of information into a priori (prior knowledge) and a posteriori information (data) is an important way of standardizing inference approaches …

The objective of ordinal embedding is to find a Euclidean representation of a set of abstract items, using only answers to triplet comparisons of the form "Is item $i$ closer to the item $j$ or item $k$?". In recent years, numerous algorithms have been proposed to solve this problem. However, there does not exist a fai…

PROBE algorithm efficiently solves sparse high-dimensional linear regression.

The paper proves statistical consistency and fairness guarantees for a plug-in algorithm.

Paper proposes an efficient algorithm for learning sparse Bayesian networks from discrete high-dimensional data.

Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It cons…

This paper introduces a novel mixture model-based approach for simultaneous clustering and optimal segmentation of functional data which are curves presenting regime changes. The proposed model consists in a finite mixture of piecewise polynomial regression models. Each piecewise polynomial regression model is associat…

A simplified Bayesian approach for online sports rating.

Motivated by concerns that machine learning algorithms may introduce significant bias in classification models, developing fair classifiers has become an important problem in machine learning research. One important paradigm towards this has been providing algorithms for adversarially learning fair classifiers (Zhang e…

MARCO-GE selects clustering algorithms using graph embeddings.

New trading model uses self-organization for real-world complexity.

New approach to learning kernels from data using AIT principles.

An evolutionary algorithm (EA) is developed as an alternative to the EM algorithm for parameter estimation in model-based clustering. This EA facilitates a different search of the fitness landscape, i.e., the likelihood surface, utilizing both crossover and mutation. Furthermore, this EA represents an efficient approac…

In this paper we present an evolutionary optimization approach to solve the risk parity portfolio selection problem. While there exist convex optimization approaches to solve this problem when long-only portfolios are considered, the optimization problem becomes non-trivial in the long-short case. To solve this problem…

Model-based clustering approaches concern the paradigm of exploratory data analysis relying on the finite mixture model to automatically find a latent structure governing observed data. They are one of the most popular and successful approaches in cluster analysis. The mixture density estimation is generally performed …

Estimation of the covariance matrix of asset returns from high frequency data is complicated by asynchronous returns, market mi- crostructure noise and jumps. One technique for addressing both asynchronous returns and market microstructure is the Kalman-EM (KEM) algorithm. However the KEM approach assumes log-normal pr…