Offline Recommender System Evaluation under Unobserved Confounding

Off-Policy Estimation (OPE) methods allow us to learn and evaluate decision-making policies from logged data. This makes them an attractive choice for the offline evaluation of recommender systems, and several recent works have reported successful adoption of OPE methods to this end. An important assumption that makes this work is the absence of unobserved confounders: random variables that influence both actions and rewards at data collection time. Because the data collection policy is typically under the practitioner's control, the unconfoundedness assumption is often left implicit, and its violations are rarely dealt with in the existing literature. This work aims to highlight the problems that arise when performing off-policy estimation in the presence of unobserved confounders, specifically focusing on a recommendation use-case. We focus on policy-based estimators, where the logging propensities are learned from logged data. We characterise the statistical bias that arises due to confounding, and show how existing diagnostics are unable to uncover such cases. Because the bias depends directly on the true and unobserved logging propensities, it is non-identifiable. As the unconfoundedness assumption is famously untestable, this becomes especially problematic. This paper emphasises this common, yet often overlooked issue. Through synthetic data, we empirically show how na\"ive propensity estimation under confounding can lead to severely biased metric estimates that are allowed to fly under the radar. We aim to cultivate an awareness among researchers and practitioners of this important problem, and touch upon potential research directions towards mitigating its effects.Comment: Accepted at the CONSEQUENCES'23 workshop at RecSys '2

Boosted Off-Policy Learning

We investigate boosted ensemble models for off-policy learning from logged bandit feedback. Toward this goal, we propose a new boosting algorithm that directly optimizes an estimate of the policy's expected reward. We analyze this algorithm and prove that the empirical risk decreases (possibly exponentially fast) with each round of boosting, provided a "weak" learning condition is satisfied. We further show how the base learner reduces to standard supervised learning problems. Experiments indicate that our algorithm can outperform deep off-policy learning and methods that simply regress on the observed rewards, thereby demonstrating the benefits of both boosting and choosing the right learning objective

Towards Dynamic Control of Wettability by Using Functionalized Altitudinal Molecular Motors on Solid Surfaces

We report the synthesis of altitudinal molecular motors that contain functional groups in their rotor part. In an approach to achieve dynamic control over the properties of solid surfaces, a hydrophobic perfluorobutyl chain and a relatively hydrophilic cyano group were introduced to the rotor part of the motors. Molecular motors were attached to quartz surfa-ces by using interfacial 1,3-dipolar cycloadditions. To test the effect of the functional groups on the rotary motion, photochemical and thermal isomerization studies of the motors were per-formed both in solution and when attached to the surface. We found that the substituents have no significant effect on the thermal and photochemical processes, and the functionalized motors preserved their rotary function both in solution and on a quartz surface. Preliminary results on the influence of the functional groups on surface wettability are also described

Double Clipping: Less-Biased Variance Reduction in Off-Policy Evaluation

"Clipping" (a.k.a. importance weight truncation) is a widely used variance-reduction technique for counterfactual off-policy estimators. Like other variance-reduction techniques, clipping reduces variance at the cost of increased bias. However, unlike other techniques, the bias introduced by clipping is always a downward bias (assuming non-negative rewards), yielding a lower bound on the true expected reward. In this work we propose a simple extension, called $\textit{double clipping}$, which aims to compensate this downward bias and thus reduce the overall bias, while maintaining the variance reduction properties of the original estimator.Comment: Presented at CONSEQUENCES '23 workshop at RecSys 2023 conference in Singapor

Measurements of Vector Bosons Produced in Association with Jets

The latest D0 and CDF measurements of the W + jets and Z/{gamma}* + jets processes are described, along with a discussion of the comparisons that have been made to LO and NLO perturbative QCD predictions. The direct production of W{sup {+-}}/Z bosons in association with jets is a process of crucial importance at hadron collider experiments. The presence of a vector boson in the hard scatter means that these interactions occur at a scale that should make perturbative QCD applicable, and thus it is an excellent channel to test such predictions. Furthermore, many of the potential discovery channels for the Higgs boson and beyond standard model processes share a final state signature with the W{sup {+-}}/Z + jets process. It is thus vital for the success of existing and future hadron collider experiments that this process is understood, and recently there has been a huge amount of work put into the modeling of this process, with the appearance of many new Monte Carlo generators that are already widely used at both the Tevatron and LHC. In Sections 2 and 3 the latest W + jets and Z/{gamma}* + jets measurements from the Tevatron are presented, and in Section 4 we discuss the results and implications of some of the theory comparisons that have thus far been made

Experimental evaluation of interfacial adhesion strength of cold sprayed Ti-6Al-4V thick coatings using an adhesive-free test method

Cold spray (CS) is a rapidly growing solid-state additive material deposition technique often used for repair of high-value metallic components. This study aims at evaluating the interfacial adhesion strength of cold sprayed Ti-6Al-4V (Ti-64) coatings deposited onto Ti-64 substrates for repair applications. An adhesive-free test method, referred as modified Collar-Pin Pull-off Test was developed based on Sharivker's (1967) original design, in order to overcome the limitations of existing test approaches (both adhesive-based and adhesive-free). This method was designed to allow measurement of adhesion strength of high strength coatings such as CS Ti-64, where adhesion strength is higher than 70-90 MPa. A parametric study was performed to assess the effect of coating thickness, scanning speed, track spacing, toolpath pattern, and substrate surface preparation on the coating adhesion strength. A finite element model was also used to evaluate the stress distribution during the pull-off test, and to check the validity of the proposed test method. The proposed adhesive-free test method was found to be capable of measuring coatings with adhesion strengths beyond the upper limit of conventional adhesive-based methods such as ASTM C633. Among the investigated cases, the highest value of coating adhesion strength was measured around 122 MPa, in the case of CS Ti-64 deposited on ground Ti-64 substrates

Stability and Generalization in Structured Prediction

Abstract Structured prediction models have been found to learn effectively from a few large examplessometimes even just one. Despite empirical evidence, canonical learning theory cannot guarantee generalization in this setting because the error bounds decrease as a function of the number of examples. We therefore propose new PAC-Bayesian generalization bounds for structured prediction that decrease as a function of both the number of examples and the size of each example. Our analysis hinges on the stability of joint inference and the smoothness of the data distribution. We apply our bounds to several common learning scenarios, including max-margin and soft-max training of Markov random fields. Under certain conditions, the resulting error bounds can be far more optimistic than previous results and can even guarantee generalization from a single large example