arXiv research

New methods optimize experiment selection for sequential data, improving model accuracy.

We identify and analyze selection structure in sequential data.

NGP selects N features from P using neural networks in a greedy, iterative process.

Algorithm learns optimal arm selection in unsupervised sequential selection with contextual information.

New algorithm for clustering data streams with no substitutions.

Unified statistical framework for LSTM model selection.

Thompson Sampling tackles USS, a sequential selection problem without feedback.

EviTrack improves sequential prediction in delayed disambiguation scenarios.

A framework for optimizing prompt selection in generative language models.

One way to solve lasso problems when the dictionary does not fit into available memory is to first screen the dictionary to remove unneeded features. Prior research has shown that sequential screening methods offer the greatest promise in this endeavor. Most existing work on sequential screening targets the context of …

Data-driven model selection reduces regret in sequential decisions.

Proposes a max-utility arm selection strategy for reducing cumulative regret in sequential query recommendations.

A two-step approach efficiently selects hyperparameters for FCMs.

Paper presents algorithm for optimal job selection with dynamic scoring.

Efficiently selects top-m designs for various contexts using sequential sampling.

The paper proposes a method to test features selected by SeqFS-DA with controlled FPR.

This research improves interpretability in sequential explanations using mental models.

Algorithm clusters items by sequentially selecting features, minimizing observations.

Variable selection for optimal treatment regime in a clinical trial or an observational study is getting more attention. Most existing variable selection techniques focused on selecting variables that are important for prediction, therefore some variables that are poor in prediction but are critical for decision-making…

Solves selecting the best optimizing system problems.

Adaptive robust strategy improves online portfolio selection by managing market trends and costs.

In their standard form Gaussian processes (GPs) provide a powerful non-parametric framework for regression and classificaton tasks. Their one limiting property is their $\mathcal{O}(N^{3})$ scaling where $N$ is the number of training data points. In this paper we present a framework for GP training with sequential sele…

Composite likelihood inference of fractional Gaussian processes with sequentially optimal subset selection

SEEK algorithm selects minimal state in reinforcement learning for better policy learning.

OLPA optimizes online user-centric selection with probing, achieving near-optimal regret bounds.

aLTT selects hyperparameters efficiently with statistical guarantees.

Proposes SNML for selecting word2vec Skip-gram dimensionality.

Proposes a method to accelerate safe sequential learning using offline data.

DARTS optimizes covariate selection in trials with limited data.

The paper examines how slightly biasing towards under-represented groups in sequential selection processes can lead to long-term fairness.

Sequential portfolio selection has attracted increasing interests in the machine learning and quantitative finance communities in recent years. As a mathematical framework for reinforcement learning policies, the stochastic multi-armed bandit problem addresses the primary difficulty in sequential decision making under …

One of the most tedious tasks in the application of machine learning is model selection, i.e. hyperparameter selection. Fortunately, recent progress has been made in the automation of this process, through the use of sequential model-based optimization (SMBO) methods. This can be used to optimize a cross-validation per…

A new feature selection method using attention for neural networks.

Most prior work on active learning of classifiers has focused on sequentially selecting one unlabeled example at a time to be labeled in order to reduce the overall labeling effort. In many scenarios, however, it is desirable to label an entire batch of examples at once, for example, when labels can be acquired in para…

New method selects features for sequential decision making.

Online method selects candidates from data streams, ensuring irreversible decisions.

This paper concerns a method of selecting a subset of features for a sequential logit model. Tanaka and Nakagawa (2014) proposed a mixed integer quadratic optimization formulation for solving the problem based on a quadratic approximation of the logistic loss function. However, since there is a significant gap between …

Computer simulators are nowadays widely used to understand complex physical systems in many areas such as aerospace, renewable energy, climate modeling, and manufacturing. One fundamental issue in the study of computer simulators is known as experimental design, that is, how to select the input settings where the compu…

GP-TS optimizes TLM pre-training hyperparameters efficiently.

Applications in machine learning, optimization, and control require the sequential selection of a few system elements, such as sensors, data, or actuators, to optimize the system performance across multiple time steps. However, in failure-prone and adversarial environments, sensors get attacked, data get deleted, and a…

Adaptive cascade submodular maximization tackles sequential selection under uncertainty.

AI learns to learn sequentially without forgetting.

Diffusion approximations optimize sequential experimentation for uncertain parameters.

The problem of active diagnosis arises in several applications such as disease diagnosis, and fault diagnosis in computer networks, where the goal is to rapidly identify the binary states of a set of objects (e.g., faulty or working) by sequentially selecting, and observing, (noisy) responses to binary valued queries. …

GIB improves neural network generalization by dynamically selecting task-relevant features across different sequential environments.

Develops methods for cooperative Bayesian inference.

Under a Bayesian framework, we formulate the fully sequential sampling and selection decision in statistical ranking and selection as a stochastic control problem, and derive the associated Bellman equation. Using value function approximation, we derive an approximately optimal allocation policy. We show that this poli…

New method reduces memory usage for high-dimensional variable selection.