Cost-sensitive Learning for Utility Optimization in Online Advertising Auctions

One of the most challenging problems in computational advertising is the prediction of click-through and conversion rates for bidding in online advertising auctions. An unaddressed problem in previous approaches is the existence of highly non-uniform misprediction costs. While for model evaluation these costs have been taken into account through recently proposed business-aware offline metrics -- such as the Utility metric which measures the impact on advertiser profit -- this is not the case when training the models themselves. In this paper, to bridge the gap, we formally analyze the relationship between optimizing the Utility metric and the log loss, which is considered as one of the state-of-the-art approaches in conversion modeling. Our analysis motivates the idea of weighting the log loss with the business value of the predicted outcome. We present and analyze a new cost weighting scheme and show that significant gains in offline and online performance can be achieved.Comment: First version of the paper was presented at NIPS 2015 Workshop on E-Commerce: https://sites.google.com/site/nips15ecommerce/papers Third version of the paper will be presented at AdKDD 2017 Workshop: adkdd17.wixsite.com/adkddtargetad201

Implicit Surface Modelling as an Eigenvalue Problem

We discuss the problem of fitting an implicit shape model to a set of points sampled from a co-dimension one manifold of arbitrary topology. The method solves a non-convex optimisation problem in the embedding function that defines the implicit by way of its zero level set. By assuming that the solution is a mixture of radial basis functions of varying widths we attain the globally optimal solution by way of an equivalent eigenvalue problem, without using or constructing as an intermediate step the normal vectors of the manifold at each data point. We demonstrate the system on two and three dimensional data, with examples of missing data interpolation and set operations on the resultant shapes

Accelerated Convergence for Counterfactual Learning to Rank

Counterfactual Learning to Rank (LTR) algorithms learn a ranking model from logged user interactions, often collected using a production system. Employing such an offline learning approach has many benefits compared to an online one, but it is challenging as user feedback often contains high levels of bias. Unbiased LTR uses Inverse Propensity Scoring (IPS) to enable unbiased learning from logged user interactions. One of the major difficulties in applying Stochastic Gradient Descent (SGD) approaches to counterfactual learning problems is the large variance introduced by the propensity weights. In this paper we show that the convergence rate of SGD approaches with IPS-weighted gradients suffers from the large variance introduced by the IPS weights: convergence is slow, especially when there are large IPS weights. To overcome this limitation, we propose a novel learning algorithm, called CounterSample, that has provably better convergence than standard IPS-weighted gradient descent methods. We prove that CounterSample converges faster and complement our theoretical findings with empirical results by performing extensive experimentation in a number of biased LTR scenarios -- across optimizers, batch sizes, and different degrees of position bias.Comment: SIGIR 2020 full conference pape

Analytical Verification of the IDS-iSYS Intact Amino-terminal Propeptide of Type 1 Procollagen (P1NP)

Peer reviewe

Application of the arbitrary Eulerian Lagrangian finite element formulation to the thermomechanical simulation of casting processes, with focus on pipe shrinkage prediction

International audienceThe Arbitrary Lagrangian-Eulerian formulation (ALE) has become an indispensable component of finite element thermomechanical computations of casting processes. As it is an intermediate formulation between the Lagrangian formulation (material convected mesh) and the Eulerian one (fixed mesh), it allows the simultaneous computation of important phenomena: Deformation and stresses affecting solidified regions, yielding the computation of air gap evolution at part/mold interfaces. In such regions, the formulation is essentially Lagrangian. Thermosolutal convection flow in the non solidified regions; here the ALE formulation tends to a pure Eulerian one (stationary mesh). Free surface evolution at top of risers, leading to the prediction of pipe defects (macroshrinkage). In this case the ALE formulation allows the follow up of the free surface. After a brief reminder of the constitutive equations to be used in thermomechanical modeling of solidification, the mechanical equations are presented and their resolution in the context of FEM-ALE. We insist on the transport analysis, a key-point of ALE, and present a validation of the original scheme that is used here. Finally, we focus on the prediction of pipe shrinkage formation and show two industrial examples

Is cystatin C useful for the detection and the estimation of low glomerular filtration rate in heart transplant patients?

Although previously studied in patients with chronic kidney disease, there is less data for the use of cystatin C and cystatin C-based formulas in heart transplant recipients. The ability of creatinine and cystatin C to detect renal failure (glomerular filtration rate [GFR] below 60 mL/min/1.73 m(2)) in heart transplant patients has been compared. The accuracy and precision of a creatinine-based formula (Modification of Diet in Renal Disease [MDRD]) versus a cystatin C-based formula (Rule's formula) to estimate GFR have also been studied. GFR was measured using the (51)Crethylenediamine tetraacetic acid tracer in 27 patients. There was no significant difference between GFR and the reciprocal of creatinine or cystatin C. Receiver operating characteristic curves for cystatin C and creatinine were similar. Both formulas were well correlated with the GFR. The bias of the cystatin C-based was significantly better than one of the MDRD formula, but the standard deviation appeared better for the MDRD formula (bias of +3.9 mL/min/1.73 m(2) versus +12 mL/min/1.73 m(2) and SD of 8.5 versus 11.6, respectively). Plasma cystatin C has no clear advantage over serum creatinine to detect renal failure in heart transplanted patients