arXiv research

The paper develops a reinforcement learning model to estimate ad impact considering delayed and cumulative effects.

Organic updates (from a member's network) and sponsored updates (or ads, from advertisers) together form the newsfeed on LinkedIn. The newsfeed, the default homepage for members, attracts them to engage, brings them value and helps LinkedIn grow. Engagement and Revenue on feed are two critical, yet often conflicting ob…

In the cost per click (CPC) pricing model, an advertiser pays an ad network only when a user clicks on an ad; in turn, the ad network gives a share of that revenue to the publisher where the ad was impressed. Still, advertisers may be unsatisfied with ad networks charging them for "valueless" clicks, or so-called accid…

This work shifts focus from prediction to intervention in social systems.

Study optimal execution in a transient price impact model with multiple traders.

In this paper, we propose an offline counterfactual policy estimation framework called Genie to optimize Sponsored Search Marketplace. Genie employs an open box simulation engine with click calibration model to compute the KPI impact of any modification to the system. From the experimental results on Bing traffic, we s…

New model reveals significant impact of data and parameter variations on machine learning benchmarks.

Machine learning models, especially based on deep architectures are used in everyday applications ranging from self driving cars to medical diagnostics. It has been shown that such models are dangerously susceptible to adversarial samples, indistinguishable from real samples to human eye, adversarial samples lead to in…

In markets for online advertising, some advertisers pay only when users respond to ads. So publishers estimate ad response rates and multiply by advertiser bids to estimate expected revenue for showing ads. Since these estimates may be inaccurate, the publisher risks not selecting the ad for each ad call that would max…

Click-through rate (CTR) prediction is a critical task in online advertising systems. A large body of research considers each ad independently, but ignores its relationship to other ads that may impact the CTR. In this paper, we investigate various types of auxiliary ads for improving the CTR prediction of the target a…

Study evaluates AD methods for fraud detection in online credit card payments.

Investment strategies in financial markets can lead to instability due to market impacts.

The study investigates noise effects on parameter estimation for Ornstein-Uhlenbeck processes.

ProtoX-AD: A self-explainable time series anomaly detection framework

Nowadays, a lot of scientific efforts are concentrated on the diagnosis of Alzheimer's Disease (AD) applying deep learning methods to neuroimaging data. Even for 2017, there were published more than a hundred papers dedicated to AD diagnosis, whereas only a few works considered a problem of mild cognitive impairments (…

Storchastic improves stochastic AD for complex models in RL and VI.

We assume a continuous-time price impact model similar to Almgren-Chriss but with the added assumption that the price impact parameters are stochastic processes modeled as correlated scalar Markov diffusions. In this setting, we develop trading strategies for a trader who desires to liquidate his inventory but faces pr…

Speech datasets for identifying Alzheimer's disease (AD) are generally restricted to participants performing a single task, e.g. describing an image shown to them. As a result, models trained on linguistic features derived from such datasets may not be generalizable across tasks. Building on prior work demonstrating th…

RDP-GAN improves GAN privacy by adding random noises to loss function.

Study on numerical reliability of AD for MaxPool in neural nets.

Estimates cross-impact on derivatives markets using E-Mini futures and options.

Introduces PIT-plot for prioritizing projects based on their impact.

This paper describes a practical system for Multi Touch Attribution (MTA) for use by a publisher of digital ads. We developed this system for JD.com, an eCommerce company, which is also a publisher of digital ads in China. The approach has two steps. The first step ('response modeling') fits a user-level model for purc…

Little is known about how different types of advertising affect brand attitudes. We investigate the relationships between three brand attitude variables (perceived quality, perceived value and recent satisfaction) and three types of advertising (national traditional, local traditional and digital). The data represent t…

We construct a price impact model between stocks in a correlated market. For the price change of a given stock induced by the short-run liquidity of this stock itself and of the information about other stocks, we introduce a self- and a cross-impact function of the time lag. We model the average cross-response function…

New data improves market impact estimation methods.

New AD methods improve likelihood estimation for partially observed systems.

CAD-DA controls anomaly detection under domain adaptation.

Study examines APOE's impact on AD progression using a novel DEBM approach.

New method quantifies systemic risk of firms in supply networks.

Estimates self- and cross-impact concavity and decay patterns in financial markets.

Exact simulation method for market impact estimation under various execution strategies.

This paper investigates the impact of dark pools on price discovery (the efficiency of prices on stock exchanges to aggregate information). Assets are traded in either an exchange or a dark pool, with the dark pool offering better prices but lower execution rates. Informed traders receive noisy and heterogeneous signal…

New algorithm for bandits with delayed action effects, reducing regret.

Improved method for private quantile estimation in datasets with atoms.

The price impact for a single trade is estimated by the immediate response on an event time scale, i.e., the immediate change of midpoint prices before and after a trade. We work out the price impacts across a correlated financial market. We quantify the asymmetries of the distributions and of the market structures of …

Complex dynamical systems driven by the unravelling of information can be modelled effectively by treating the underlying flow of information as the model input. Complicated dynamical behaviour of the system is then derived as an output. Such an information-based approach is in sharp contrast to the conventional mathem…

Study shows adding correlated features doesn't improve LSTM model interpretability for oil stocks.

Proposes a new model to measure trade impact and information content in fluctuating markets.

Study examines market impact of small orders in futures contracts.

First, we analyze the variance of the Cross Validation (CV)-based estimators used for estimating the performance of classification rules. Second, we propose a novel estimator to estimate this variance using the Influence Function (IF) approach that had been used previously very successfully to estimate the variance of …

Let $G$ be a real connected Lie group with a left invariant metric $d$, $\mathfrak{g}$ its Lie algebra. In this paper we present a set of interesting upper and lower bounds for $|d\exp_{x}(y)|,\ x,y \in \mathfrak{g}$. If $\textrm{ad}_x$ is diagonalizable, these bounds only depend on eigenvalues of $\textrm{ad}_x$, but …

Paper optimizes broker performance by estimating execution costs.

New method uses impact IRR to assess impact investments.

We report on the occurrence of an anomaly in the price impacts of small transaction volumes following a change in the fee structure of an electronic market. We first review evidence for the existence of a master curve for price impact on the Johannesburg Stock Exchange (JSE). On attempting to re-estimate a master curve…

New model explains why metaorder impact estimation is hard with public data.

It has been understood that the "local" existence of the Markowitz' optimal portfolio or the solution to the local-risk minimization problem is guaranteed by some specific mathematical structures on the underlying assets price processes known in the literature as "{\it Structure Conditions}". In this paper, we consider…

Satellite imagery and ML improve livelihood measurements and estimate electrification's impact.