arXiv research

Graph-Triggered Bandits unify rested and restless bandits with graph-defined arm interactions.

New algorithm handles both decaying and non-decaying bandit problems.

The paper tackles rested bandits with non-decreasing and concave rewards, deriving lower bounds and an efficient algorithm.

New algorithm identifies best arm in rested bandit setting.

New algorithms reduce regret in non-stationary bandits with increasing payoffs.

We use online convex optimization (OCO) for setpoint tracking with uncertain, flexible loads. We consider full feedback from the loads, bandit feedback, and two intermediate types of feedback: partial bandit where a subset of the loads are individually observed and the rest are observed in aggregate, and Bernoulli feed…

We consider the multi armed bandit problem in non-stationary environments. Based on the Bayesian method, we propose a variant of Thompson Sampling which can be used in both rested and restless bandit scenarios. Applying discounting to the parameters of prior distribution, we describe a way to systematically reduce the …

Study on information evolution in interactive decision making using multi-armed bandits.

In stochastic multi-armed bandits, the reward distribution of each arm is assumed to be stationary. This assumption is often violated in practice (e.g., in recommendation systems), where the reward of an arm may change whenever is selected, i.e., rested bandit setting. In this paper, we consider the non-parametric rott…

Study tackles infinitely many-armed bandits with rotting rewards, achieving tight regret bounds.

Introduces tensor bandits for multi-dimensional online decision making.

New TS-like algorithms tackle rising bandits with improved regret analysis.

We consider the problem of contextual bandits with stochastic experts, which is a variation of the traditional stochastic contextual bandit with experts problem. In our problem setting, we assume access to a class of stochastic experts, where each expert is a conditional distribution over the arms given a context. We p…

Paper tackles online ranking and diversification in recommender systems.

Motivated by online recommendation and advertising systems, we consider a causal model for stochastic contextual bandits with a latent low-dimensional confounder. In our model, there are $L$ observed contexts and $K$ arms of the bandit. The observed context influences the reward obtained through a latent confounder var…

Paper identifies resting positions using EGG, ECG, respiration rate, and SpO2.

Most recommender systems recommend a list of items. The user examines the list, from the first item to the last, and often chooses the first attractive item and does not examine the rest. This type of user behavior can be modeled by the cascade model. In this work, we study cascading bandits, an online learning variant…

Study optimal adaptive allocation for multi-armed bandits with Markovian rewards.

Study best arm identification in restless bandits with unknown TPMs.

BrainSurfCNN predicts task contrasts from resting-state fingerprints, improving accuracy over baseline.

REST improves robustness and efficiency of sleep monitoring models.

Study best arm identification in restless Markov multi-armed bandits with state-dependent transitions.

The paper extends Hoeffding's inequality for Markov chains using a generalized concentrability condition.

Study on spin-zero rest-mass fields using conformal geometric method.

FlexServe simplifies deployment of PyTorch models as REST endpoints.

Paper tackles robust policy learning with incomplete data.

As the advancement of information security, human recognition as its core technology, has absorbed an increasing amount of attention in the past few years. A myriad of biometric features including fingerprint, face, iris, have been applied to security systems, which are occasionally considered vulnerable to forgery and…

Proposes SOVR loss to improve adversarial robustness by increasing logit margins.

Training of one-vs.-rest SVMs can be parallelized over the number of classes in a straight forward way. Given enough computational resources, one-vs.-rest SVMs can thus be trained on data involving a large number of classes. The same cannot be stated, however, for the so-called all-in-one SVMs, which require solving a …

Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We present a methodical taxonomy of machine learning methods in resting…

Stochastic encoding improves gender classification of brain networks from UK Biobank data.

Resting-state functional MRI (rs-fMRI) is a rich imaging modality that captures spontaneous brain activity patterns, revealing clues about the connectomic organization of the human brain. While many rs-fMRI studies have focused on static measures of functional connectivity, there has been a recent surge in examining th…

Paper tackles identifying an odd arm in a multi-armed bandit with restless Markov processes and trembling hand.

This paper proposes a practical approach for automatic sleep stage classification based on a multi-level feature learning framework and Recurrent Neural Network (RNN) classifier using heart rate and wrist actigraphy derived from a wearable device. The feature learning framework is designed to extract low- and mid-level…

We present a method for fast resting-state fMRI spatial decomposi-tions of very large datasets, based on the reduction of the temporal dimension before applying dictionary learning on concatenated individual records from groups of subjects. Introducing a measure of correspondence between spatial decompositions of rest …

The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are known to undergo substantial reorganization from childhood to adulthood, but wheth…

REST framework predicts stock trends by considering stock-specific and related-stock events.

REST improves robustness of black-box models to geometric transformations.

In this paper we investigate the usage of adversarial perturbations for the purpose of privacy from human perception and model (machine) based detection. We employ adversarial perturbations for obfuscating certain variables in raw data while preserving the rest. Current adversarial perturbation methods are used for dat…

Dynamic functional connectivity (FC) has in recent years become a topic of interest in the neuroimaging community. Several models and methods exist for both functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), and the results point towards the conclusion that FC exhibits dynamic changes. The e…

New algorithms tackle RKHS bandits with reduced complexity and improved performance.

Unified formulation bridges adversarial and nonstationary bandits.

Bayesian model detects altered neural circuits in MCI patients.

Paper solves stochastic contextual linear bandits using linear bandit algorithms.

Paper tackles LDP bandits learning with improved results and sub-linear regret.

Resting-state brain networks represent the intrinsic state of the brain during the majority of cognitive and sensorimotor tasks. However, no study has yet presented concise predictors of task-induced vigilance variability from spectrospatial features of the pre-task, resting-state electroencephalograms (EEG). We asked …

New definition resolves ambiguity in non-stationary bandit classification.

Unified approach for non-stationary and clustered bandits.