arXiv research

Paper develops a statistical model for summarizing event sequences.

We study the dynamics of the linear and non-linear serial dependencies in financial time series in a rolling window framework. In particular, we focus on the detection of episodes of statistically significant two- and three-point correlations in the returns of several leading currency exchange rates that could offer so…

New memory allocation scheme improves image generation performance.

Serial problems can't be efficiently parallelized, affecting machine learning models.

We study the relation between serial correlation of financial returns and volatility at intraday level for the S&P500 stock index. At daily and weekly level, serial correlation and volatility are known to be negatively correlated (LeBaron effect). While confirming that the LeBaron effect holds also at intraday level, w…

The paper proposes a generic learning method for complex structures.

Stochastic momentum methods trade compute efficiency for serial runtime.

Study predicts hearing recovery in MD patients using TEOAE signals.

The paper proposes estimators for bid-ask spreads with and without serial dependence.

The book chapter discusses tail risk analysis for financial data using extreme value statistics.

The study sets criteria for efficient communication in distributed online learning.

We investigate serial correlation, periodic, aperiodic and scaling behaviour of eigenmodes, i.e. daily price fluctuation time-series derived from eigenvectors, of correlation matrices of shares listed on the Johannesburg Stock Exchange (JSE) from January 1993 to December 2002. Periodic, or calendar, components are dete…

We study the impact of volatility on intraday serial correlation, at time scales of less than 20 minutes, exploiting a data set with all transaction on SPX500 futures from 1993 to 2001. We show that, while realized volatility and intraday serial correlation are linked, this relation is driven by unexpected volatility o…

In practice daily volatility of portfolio returns is transformed to longer holding periods by multiplying by the square-root of time which assumes that returns are not serially correlated. Under this assumption this procedure of scaling can also be applied to contributions to volatility of the assets in the portfolio. …

Algorithm beats best constant rebalancing portfolio in long-term investment.

CoT enhances transformer accuracy on serial tasks by enabling serial computation.

W-RNN improves text classification by extracting serialized text semantics.

In this paper we investigate the adaptive market efficiency of the agricultural commodity futures market, using a sample of eight futures contracts. Using a battery of nonlinear tests, we uncover the nonlinear serial dependence in the returns series. We run the Hinich portmanteau bicorrelation test to uncover the momen…

A new algorithm for competing agents in a two-sided market setting.

Diffusion models explained via cognitive science.

Study preference-based reinforcement learning in episodic kernel MDPs.

Algorithm tackles constrained reinforcement learning with concave-convex and knapsack constraints.

Algorithm improves multi-armed bandit performance by transferring reward samples.

BerlinUCB learns from episodic rewards in nonstationary contexts.

ParaMonte::Python streamlines Bayesian data analysis with fast Monte Carlo and MCMC routines.

We present a general framework for accelerating a large class of widely used Markov chain Monte Carlo (MCMC) algorithms. Our approach exploits fast, iterative approximations to the target density to speculatively evaluate many potential future steps of the chain in parallel. The approach can accelerate computation of t…

Improved regret bound for online learning in unknown MDPs.

Algorithm reduces episode count for CMDPs with constraints.

Many interesting real world domains involve reinforcement learning (RL) in partially observable environments. Efficient learning in such domains is important, but existing sample complexity bounds for partially observable RL are at least exponential in the episode length. We give, to our knowledge, the first partially …

Algorithm improves online learning in adversarial bandits.

New algorithm learns reward signals from episodic returns for better reinforcement learning.

The Sharpe ratio, which is defined as the ratio of the excess expected return of an investment to its standard deviation, has been widely cited in the financial literature by researchers and practitioners. However, very little attention has been paid to the statistical properties of the estimation of the ratio. Lo (200…

Estimates Hurst exponent of log-volatility using KS statistic, addressing serial correlation in financial data.

Reinforcement learning (RL) algorithms have made huge progress in recent years by leveraging the power of deep neural networks (DNN). Despite the success, deep RL algorithms are known to be sample inefficient, often requiring many rounds of interaction with the environments to obtain satisfactory performance. Recently,…

Tiny episodic memory significantly improves continual learning performance.

We introduce a new class of reinforcement learning methods referred to as {\em episodic multi-armed bandits} (eMAB). In eMAB the learner proceeds in {\em episodes}, each composed of several {\em steps}, in which it chooses an action and observes a feedback signal. Moreover, in each step, it can take a special action, c…

Model learns continuously from text without forgetting.

Paper proposes DAC-ML, a cognitive architecture that learns quickly from few episodes.

Proposes rCV to preserve serial correlations in time-series models.

Enhanced image recognition models learn from human-like memory and shape biases.

We propose Episodic Backward Update (EBU) - a novel deep reinforcement learning algorithm with a direct value propagation. In contrast to the conventional use of the experience replay with uniform random sampling, our agent samples a whole episode and successively propagates the value of a state to its previous states.…

Method predicts LFSM increments from past observations using codifference.

New rule reduces exploration regret to logarithmic, improving bad episode handling.

Recurrent major mood episodes and subsyndromal mood instability cause substantial disability in patients with bipolar disorder. Early identification of mood episodes enabling timely mood stabilisation is an important clinical goal. Recent technological advances allow the prospective reporting of mood in real time enabl…

New algorithms reduce dynamic regret in online MDPs with changing losses.

Extends Thompson sampling for RL with fewer episodes.

Single episode policy transfer in RL without access to rewards.

Improved reinforcement learning for episodes with varying action sets.