Breaking Feedback Loops in Recommender Systems with Causal Inference

Recommender systems play a key role in shaping modern web ecosystems. These systems alternate between (1) making recommendations (2) collecting user responses to these recommendations, and (3) retraining the recommendation algorithm based on this feedback. During this process the recommender system influences the user behavioral data that is subsequently used to update it, thus creating a feedback loop. Recent work has shown that feedback loops may compromise recommendation quality and homogenize user behavior, raising ethical and performance concerns when deploying recommender systems. To address these issues, we propose the Causal Adjustment for Feedback Loops (CAFL), an algorithm that provably breaks feedback loops using causal inference and can be applied to any recommendation algorithm that optimizes a training loss. Our main observation is that a recommender system does not suffer from feedback loops if it reasons about causal quantities, namely the intervention distributions of recommendations on user ratings. Moreover, we can calculate this intervention distribution from observational data by adjusting for the recommender system's predictions of user preferences. Using simulated environments, we demonstrate that CAFL improves recommendation quality when compared to prior correction methods

numpywren: serverless linear algebra

Linear algebra operations are widely used in scientific computing and machine learning applications. However, it is challenging for scientists and data analysts to run linear algebra at scales beyond a single machine. Traditional approaches either require access to supercomputing clusters, or impose configuration and cluster management challenges. In this paper we show how the disaggregation of storage and compute resources in so-called "serverless" environments, combined with compute-intensive workload characteristics, can be exploited to achieve elastic scalability and ease of management. We present numpywren, a system for linear algebra built on a serverless architecture. We also introduce LAmbdaPACK, a domain-specific language designed to implement highly parallel linear algebra algorithms in a serverless setting. We show that, for certain linear algebra algorithms such as matrix multiply, singular value decomposition, and Cholesky decomposition, numpywren's performance (completion time) is within 33% of ScaLAPACK, and its compute efficiency (total CPU-hours) is up to 240% better due to elasticity, while providing an easier to use interface and better fault tolerance. At the same time, we show that the inability of serverless runtimes to exploit locality across the cores in a machine fundamentally limits their network efficiency, which limits performance on other algorithms such as QR factorization. This highlights how cloud providers could better support these types of computations through small changes in their infrastructure

Hochspezialisierte ambulante Versorgung in Krankenhäusern: eine empirische Abschätzung von Kosten, Erlösen und mögliche Strategien

'Ambulante Behandlungen im Krankenhaus werden vom Gesetzgeber zunehmend gefördert. Angesichts möglicher Kostenunterdeckungen und Mengenbudgetierungen ist die Wahl der besten Strategie für Krankenhäuser bisher unklar. Auf der Basis von 16.171 an 6 Hochschulkliniken in Deutschland prospektiv dokumentierten Konsultationen in Ermächtigungsambulanzen (3.219 Konsultationen) und Hochschulambulanzen (12.952 Konsultationen) wurden von den Autoren Kosten und Erlöse gegenüber gestellt. Der Deckungsgrad lag je nach Ambulanzart bei 27% bis 29% bezogen auf die Gesamtkosten (44% bis 47% bezogen auf die Primärkosten). Da diese Unterdeckung zu einer Unterversorgung der Bevölkerung bei spezialisierter ambulanter Behandlung führen kann, ist auch aus gesundheitspolitischer Sicht eine Überprüfung der Finanzierung sinnvoll.' (Autorenreferat)'The delivery of ambulatory care in hospitals is broadened by legislative actions in Germany. However, best strategies for hospitals are unclear due to consideration of costs, reimbursements and budgeting. We included 16,171 prospectively documented consultations from six university hospitals in Germany. The ratio of reimbursement to total cost was 27% to 29%, concerning to the type of ambulatory care. (44% to 47% concerning to variable costs). This financial deficit could cause a shortage of highly specialized ambulatory services. Health policy interventions might be necessary.' (author's abstract)

Quantum-Phase Transitions of Interacting Bosons and the Supersolid Phase

We investigate the properties of strongly interacting bosons in two dimensions at zero temperature using mean-field theory, a variational Ansatz for the ground state wave function, and Monte Carlo methods. With on-site and short-range interactions a rich phase diagram is obtained. Apart from the homogeneous superfluid and Mott-insulating phases, inhomogeneous charge-density wave phases appear, that are stabilized by the finite-range interaction. Furthermore, our analysis demonstrates the existence of a supersolid phase, in which both long-range order (related to the charge-density wave) and off-diagonal long-range order coexist. We also obtain the critical exponents for the various phase transitions.Comment: RevTex, 20 pages, 10 PostScript figures include