arXiv research

Polynomial-time method solves complex combinatorial semi-bandits.

New algorithm reduces combinatorial semi-bandit regret efficiently.

We formulate and analyze a graphical model selection method for inferring the conditional independence graph of a high-dimensional nonstationary Gaussian random process (time series) from a finite-length observation. The observed process samples are assumed uncorrelated over time and having a time-varying marginal dist…

In this paper, we establish sample path large and moderate deviation principles for log-price processes in Gaussian stochastic volatility models, and study the asymptotic behavior of exit probabilities, call pricing functions, and the implied volatility. In addition, we prove that if the volatility function in an uncor…

The paper proves the convergence of Q-value for Gaussian rewards.

Method predicts future rewards from past actions in a linear Gaussian system.

Truncated Lévy flights are random walks in which the arbitrarily large steps of a Lévy flight are eliminated. Since this makes the variance finite, the central limit theorem applies, and as time increases the probability distribution of the increments becomes Gaussian. Here, truncated Lévy flights with correlated fluct…

Deep Gaussian processes (DGPs) can model complex marginal densities as well as complex mappings. Non-Gaussian marginals are essential for modelling real-world data, and can be generated from the DGP by incorporating uncorrelated variables to the model. Previous work on DGP models has introduced noise additively and use…

New algorithm reduces regret in multi-armed bandit problems with Gaussian rewards.

New bounds for kernel regression under non-Gaussian noise.

The paper maps time-series onto networks to reveal hidden joint information.

Thompson Sampling bounds for contextual bandits with sub-Gaussian rewards.

Based on the Multifractal Detrended Fluctuation Analysis (MFDFA) and on the Wavelet Transform Modulus Maxima (WTMM) methods we investigate the origin of multifractality in the time series. Series fluctuating according to a qGaussian distribution, both uncorrelated and correlated in time, are used. For the uncorrelated …

A new method resolves non-identifiability in reward modeling using anchor labels.

Paper improves SLCB regret bound for bounded noise.

Deep learning identifies space objects from uncorrelated observations.

A new method optimizes in nonstationary environments with many arms efficiently.

New algorithm for recommending best arms with aggregated feedback.

New algorithm uses machine learning to predict rewards for decision-making problems.

New acquisition function for extreme rewards in bandits.

Nonparametric Thompson Sampling achieves optimal regret for risk-averse bandits with sub-Gaussian rewards.

Study tests adequacy of FARIMA models with uncorrelated but non-independent errors.

We here adopt Bayesian nonparametric mixture models to extend multi-armed bandits in general, and Thompson sampling in particular, to scenarios where there is reward model uncertainty. In the stochastic multi-armed bandit, the reward for the played arm is generated from an unknown distribution. Reward uncertainty, i.e.…

Iterative tilting fine-tunes diffusion models for reward-tilted distributions.

A bandit algorithm reduces regret in noisy, communication-constrained feedback.

We present a simple and general result that the sign of the variations or increments of uncorrelated times series are predictable with a remarkably high success probability of 75% for symmetric sign distributions. The origin of this paradoxical result is explained in details. We also present some tests on synthetic, fi…

Inspired by the Reward-Biased Maximum Likelihood Estimate method of adaptive control, we propose RBMLE -- a novel family of learning algorithms for stochastic multi-armed bandits (SMABs). For a broad range of SMABs including both the parametric Exponential Family as well as the non-parametric sub-Gaussian/Exponential f…

Factorial moments are convenient tools in nuclear physics to characterize the multiplicity distributions when phase-space resolution ($Δ$) becomes small. For uncorrelated particle production within $Δ$, Gaussian statistics holds and factorial moments $F_q$ are equal to unity for all orders $q$. Correlations between par…

A new algorithm balances global reward and group constraints in federated multi-armed bandits.

This paper tackles unpaired data in multi-view learning, proposing a new framework and models.

We consider the Black--Scholes model of financial market modified to capture the stochastic nature of volatility observed at real financial markets. For volatility driven by the Ornstein--Uhlenbeck process, we establish the existence of equivalent martingale measure in the market model. The option is priced with respec…

A new algorithm uses concavity in Gaussian processes to optimize decisions in bandit problems.

The paper tackles resource allocation for arms with unknown and random rewards, achieving optimal regret bounds.

New algorithms for generalized linear bandits with unknown reward functions.

The study analyzes how neural reward models learn features for policy optimization in a Gaussian single-index model.

We uncover scaling laws and statistical structure in complex datasets.

The paper analyzes the reward improvement of aligned policies in large language models.

New method identifies structural parameters without assuming uncorrelated errors.

We find a remarkable time persistence of various proxies for the kurtosis (p-kurtosis) of the intraday returns distribution for the S&P500 index and this permits a significant measure of their evolution from 1983 to 2004. There appears a long time scale dramatic variation of the p-kurtosis uncorrelated with the variati…

New model selects uncorrelated and discriminative features for unsupervised feature selection.

Improved option pricing for SABR model using Gauss-Hermite quadrature.

We characterize the sample size required for accurate graphical model selection from non-stationary samples. The observed data is modeled as a vector-valued zero-mean Gaussian random process whose samples are uncorrelated but have different covariance matrices. This model contains as special cases the standard setting …

New algorithm reduces worst-case regret for heavy-tailed bandits.

State entropy regularization improves robustness in reinforcement learning, especially under structured perturbations.

This paper extends the MAB problem to consider risk-reward tradeoffs.

We investigate the use of bootstrapping in the bandit setting. We first show that the commonly used non-parametric bootstrapping (NPB) procedure can be provably inefficient and establish a near-linear lower bound on the regret incurred by it under the bandit model with Bernoulli rewards. We show that NPB with an approp…

A novel approach uses an ensemble of Gaussian processes for robust and adaptive reinforcement learning.

Thompson Sampling shows polynomial regret for combinatorial semi-bandits with subgaussian rewards.