Effective Evaluation using Logged Bandit Feedback from Multiple Loggers

Accurately evaluating new policies (e.g. ad-placement models, ranking functions, recommendation functions) is one of the key prerequisites for improving interactive systems. While the conventional approach to evaluation relies on online A/B tests, recent work has shown that counterfactual estimators can provide an inexpensive and fast alternative, since they can be applied offline using log data that was collected from a different policy fielded in the past. In this paper, we address the question of how to estimate the performance of a new target policy when we have log data from multiple historic policies. This question is of great relevance in practice, since policies get updated frequently in most online systems. We show that naively combining data from multiple logging policies can be highly suboptimal. In particular, we find that the standard Inverse Propensity Score (IPS) estimator suffers especially when logging and target policies diverge -- to a point where throwing away data improves the variance of the estimator. We therefore propose two alternative estimators which we characterize theoretically and compare experimentally. We find that the new estimators can provide substantially improved estimation accuracy.Comment: KDD 201

Cellular Responses to Cisplatin-Induced DNA Damage

Cisplatin is one of the most effective anticancer agents widely used in the treatment of solid tumors. It is generally considered as a cytotoxic drug which kills cancer cells by damaging DNA and inhibiting DNA synthesis. How cells respond to cisplatin-induced DNA damage plays a critical role in deciding cisplatin sensitivity. Cisplatin-induced DNA damage activates various signaling pathways to prevent or promote cell death. This paper summarizes our current understandings regarding the mechanisms by which cisplatin induces cell death and the bases of cisplatin resistance. We have discussed various steps, including the entry of cisplatin inside cells, DNA repair, drug detoxification, DNA damage response, and regulation of cisplatin-induced apoptosis by protein kinases. An understanding of how various signaling pathways regulate cisplatin-induced cell death should aid in the development of more effective therapeutic strategies for the treatment of cancer

Adaptive TTL-Based Caching for Content Delivery

Content Delivery Networks (CDNs) deliver a majority of the user-requested content on the Internet, including web pages, videos, and software downloads. A CDN server caches and serves the content requested by users. Designing caching algorithms that automatically adapt to the heterogeneity, burstiness, and non-stationary nature of real-world content requests is a major challenge and is the focus of our work. While there is much work on caching algorithms for stationary request traffic, the work on non-stationary request traffic is very limited. Consequently, most prior models are inaccurate for production CDN traffic that is non-stationary. We propose two TTL-based caching algorithms and provide provable guarantees for content request traffic that is bursty and non-stationary. The first algorithm called d-TTL dynamically adapts a TTL parameter using a stochastic approximation approach. Given a feasible target hit rate, we show that the hit rate of d-TTL converges to its target value for a general class of bursty traffic that allows Markov dependence over time and non-stationary arrivals. The second algorithm called f-TTL uses two caches, each with its own TTL. The first-level cache adaptively filters out non-stationary traffic, while the second-level cache stores frequently-accessed stationary traffic. Given feasible targets for both the hit rate and the expected cache size, f-TTL asymptotically achieves both targets. We implement d-TTL and f-TTL and evaluate both algorithms using an extensive nine-day trace consisting of 500 million requests from a production CDN server. We show that both d-TTL and f-TTL converge to their hit rate targets with an error of about 1.3%. But, f-TTL requires a significantly smaller cache size than d-TTL to achieve the same hit rate, since it effectively filters out the non-stationary traffic for rarely-accessed objects

Robust Clustering with Normal Mixture Models: A Pseudo β\beta-Likelihood Approach

As in other estimation scenarios, likelihood based estimation in the normal mixture set-up is highly non-robust against model misspecification and presence of outliers (apart from being an ill-posed optimization problem). We propose a robust alternative to the ordinary likelihood approach for this estimation problem which performs simultaneous estimation and data clustering and leads to subsequent anomaly detection. To invoke robustness, we follow, in spirit, the methodology based on the minimization of the density power divergence (or alternatively, the maximization of the $\beta$-likelihood) under suitable constraints. An iteratively reweighted least squares approach has been followed in order to compute our estimators for the component means (or equivalently cluster centers) and component dispersion matrices which leads to simultaneous data clustering. Some exploratory techniques are also suggested for anomaly detection, a problem of great importance in the domain of statistics and machine learning. Existence and consistency of the estimators are established under the aforesaid constraints. We validate our method with simulation studies under different set-ups; it is seen to perform competitively or better compared to the popular existing methods like K-means and TCLUST, especially when the mixture components (i.e., the clusters) share regions with significant overlap or outlying clusters exist with small but non-negligible weights. Two real datasets are also used to illustrate the performance of our method in comparison with others along with an application in image processing. It is observed that our method detects the clusters with lower misclassification rates and successfully points out the outlying (anomalous) observations from these datasets.Comment: Pre-prin