arXiv research

A new method for uplift modeling using learning-to-rank techniques.

We consider a financial contract that delivers a single cash flow given by the terminal value of a cumulative gains process. The problem of modelling and pricing such an asset and associated derivatives is important, for example, in the determination of optimal insurance claims reserve policies, and in the pricing of r…

This work investigates fundamental questions related to learning features in convolutional neural networks (CNN). Empirical findings across multiple architectures such as VGG, ResNet, Inception, DenseNet and MobileNet indicate that weights near the center of a filter are larger than weights on the outside. Current regu…

Understanding and predicting the popularity of online items is an important open problem in social media analysis. Considerable progress has been made recently in data-driven predictions, and in linking popularity to external promotions. However, the existing methods typically focus on a single source of external influ…

DO-IQS recovers optimal stopping region from expert trajectories, addressing specific challenges.

Algorithm extracts deterministic PDFA from probabilistic models with improved performance.

Many modern commercial sites employ recommender systems to propose relevant content to users. While most systems are focused on maximizing the immediate gain (clicks, purchases or ratings), a better notion of success would be the lifetime value (LTV) of the user-system interaction. The LTV approach considers the future…

Data science enhances knot theory by analyzing invariant relations.

A new framework promotes trustworthy user-generated datasets by ensuring no user benefits from misreporting.

Optimal exit strategies of CPT gamblers in unfair gambles

Simulation framework assesses ROI of chronic disease adherence and policy timing.

Optimistic algorithms and Thompson sampling use info-theory for better reinforcement learning.

This paper quantifies the impact of Dow Jones Sustainability Index listing on stock returns.

Paper proposes compressive ICA algorithms for ICA model.

This paper examines an optimal investment problem in a continuous-time (essentially) complete financial market with a finite horizon. We deal with an investor who behaves consistently with principles of Cumulative Prospect Theory, and whose utility function on gains is bounded above. The well-posedness of the optimisat…

Perceptron is a classic online algorithm for learning a classification function. In this paper, we provide a novel extension of the perceptron algorithm to the learning to rank problem in information retrieval. We consider popular listwise performance measures such as Normalized Discounted Cumulative Gain (NDCG) and Av…

This paper extends stock trading results to include stop-loss orders.

New method optimizes resource allocation for uncertain tasks.

In this letter, we derive the optimal discriminant functions for modulation classification based on the sampled distribution distance. The proposed method classifies various candidate constellations using a low complexity approach based on the distribution distance at specific testpoints along the cumulative distributi…

Novel approach to contextual bandits using self-supervised learning.

Improved product recommendations using deep learning.

Study shows code-level optimizations significantly impact deep RL algorithms.

With the industry trend of shifting from a traditional hierarchical approach to flatter management structure, crowdsourced performance assessment gained mainstream popularity. One fundamental challenge of crowdsourced performance assessment is the risks that personal interest can introduce distortions of facts, especia…

Develops a model for gambling decisions under time inconsistency.

iPromoter-BnCNN identifies and classifies six types of sigma promoters.

It is of increasing importance to develop learning methods for ranking. In contrast to many learning objectives, however, the ranking problem presents difficulties due to the fact that the space of permutations is not smooth. In this paper, we examine the class of rank-linear objective functions, which includes popular…

Computational identification of promoters is notoriously difficult as human genes often have unique promoter sequences that provide regulation of transcription and interaction with transcription initiation complex. While there are many attempts to develop computational promoter identification methods, we have no reliab…

Gradient boosting predicts promotion efficiency using multiple performance indicators.

How is popularity gained online? Is being successful strictly related to rapidly becoming viral in an online platform or is it possible to acquire popularity in a steady and disciplined fashion? What are other temporal characteristics that can unveil the popularity of online content? To answer these questions, we lever…

Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval. In this paper, we develop learning to rank formulations for hashing, aimed at directly optimizing ranking-based evaluation metrics such as Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). We first o…

New method for RL with general utilities using variational policy gradient.

The paper improves asymmetric causality tests by addressing inefficiencies and statistical significance issues.

Dynamic promotion optimization for e-commerce platforms within financial constraints.

In distributional reinforcement learning (RL), the estimated distribution of value function models both the parametric and intrinsic uncertainties. We propose a novel and efficient exploration method for deep RL that has two components. The first is a decaying schedule to suppress the intrinsic uncertainty. The second …

Study quantifies how LLMs capture higher-order statistical structure using cumulant expansion.

Study of historic stock returns distributions, highlighting asymmetry and outliers.

This chapter reviews statistical tools for reinforcement learning.

A batched Gaussian Process bandit optimization method achieves near-optimal regret bounds.

Bayesian optimisation has gained great popularity as a tool for optimising the parameters of machine learning algorithms and models. Somewhat ironically, setting up the hyper-parameters of Bayesian optimisation methods is notoriously hard. While reasonable practical solutions have been advanced, they can often fail to …

The CSA-ES is an Evolution Strategy with Cumulative Step size Adaptation, where the step size is adapted measuring the length of a so-called cumulative path. The cumulative path is a combination of the previous steps realized by the algorithm, where the importance of each step decreases with time. This article studies …

TimeMCL forecasts diverse time series futures using neural networks and WTA loss.

Using methods introduced by Scargle in 1978 we derive a cumulative version of the Lomb periodogram that exhibits frequency independent statistics when applied to cumulative noise. We show how this cumulative Lomb periodogram allows us to estimate the significance of log-periodic signatures in the S&P 500 anti-bubble th…

EDRBO optimizes Bayesian optimization with continuous contexts using ensemble models and robust methods.

The era of data deluge has sparked the interest in graph-based learning methods in a number of disciplines such as sociology, biology, neuroscience, or engineering. In this paper, we introduce a graph recurrent neural network (GRNN) for scalable semi-supervised learning from multi-relational data. Key aspects of the no…

With advances in information acquisition technologies, multi-view data become ubiquitous. Multi-view learning has thus become more and more popular in machine learning and data mining fields. Multi-view unsupervised or semi-supervised learning, such as co-training, co-regularization has gained considerable attention. A…

This study proposes hidden state curiosity to enhance RL models' resilience against noise.

Paper develops a method to learn optimal sparsity-promoting regularizers for linear inverse problems.

Extends double linear policy with time-varying weights and proves robust positive expectation.