arXiv subjects

Kun Ren

Publications and source records attributed to Kun Ren.

7 recordsLinked to original sources

arXiv2026

Deep Optimal Individualized Treatment Rules for Bivariate Survival Outcomes via Adaptive Prediction-Powered Learning

In randomized trials involving multiple treatments, bivariate survival outcomes present significant analytical challenges for making decisions. This paper addresses the problem of deriving optimal individualized treatment rules to maximize the joint survival probability beyond fixed time points $(t_1, t_2)$ through deep neural networks, while accounting for right censoring. We propose a novel approach that models treatment rules via stochastic policies, coupling marginal accelerated failure time models via link function to capture bivariate dependence. To enhance robustness and effectiveness of decision making, we introduce an adaptive prediction-powered method that leverages auxiliary predictions from machine learning models.

stat.ML↗

arXiv2026

Flexible semiparametric modeling with application to Causal Inference

This paper proposes a flexible new framework for constructing Neyman-orthogonal scores in semiparametric models involving infinite-dimensional nuisance parameters. While locally estimation is vital for integrating machine learning into econometrics, deriving orthogonal scores for complex models remains a major challenge. We provide explicit construction strategies for broad classes of settings. The proposed framework ensures asymptotic normality of target parameter estimators in a way that does not depend on the method used to construct the nuisance parameter estimators, provided they are $o_p(n^{-\1/4})$-consistent. We apply the proposed methodology to causal inference with a binary instrumental variable, developing a novel, robust estimator for treatment effects. Numerical studies demonstrate that our approach significantly outperforms naive alternatives in finite samples. An empirical application to the Oregon Health Insurance Experiment illustrates the framework's utility in providing robust causal evidence.

stat.ME↗

arXiv2023

(Re)$^2$H2O: Autonomous Driving Scenario Generation via Reversely Regularized Hybrid Offline-and-Online Reinforcement Learning

Autonomous driving and its widespread adoption have long held tremendous promise. Nevertheless, without a trustworthy and thorough testing procedure, not only does the industry struggle to mass-produce autonomous vehicles (AV), but neither the general public nor policymakers are convinced to accept the innovations. Generating safety-critical scenarios that present significant challenges to AV is an essential first step in testing. Real-world datasets include naturalistic but overly safe driving behaviors, whereas simulation would allow for unrestricted exploration of diverse and aggressive traffic scenarios. Conversely, higher-dimensional searching space in simulation disables efficient scenario generation without real-world data distribution as implicit constraints. In order to marry the benefits of both, it seems appealing to learn to generate scenarios from both offline real-world and online simulation data simultaneously. Therefore, we tailor a Reversely Regularized Hybrid Offline-and-Online ((Re)$^2$H2O) Reinforcement Learning recipe to additionally penalize Q-values on real-world data and reward Q-values on simulated data, which ensures the generated scenarios are both varied and adversarial. Through extensive experiments, our solution proves to produce more risky scenarios than competitive baselines and it can generalize to work with various autonomous driving models. In addition, these generated scenarios are also corroborated to be capable of fine-tuning AV performance.

cs.LG↗

arXiv2019

LogPlayer: Fault-tolerant Exactly-once Delivery using gRPC Asynchronous Streaming

In this paper, we present the design of our LogPlayer that is a component responsible for fault-tolerant delivery of transactional mutations recorded on a WAL to the backend storage shards. LogPlayer relies on gRPC for asynchronous streaming. However, the design provided in this paper can be used with other asynchronous streaming platforms. We model check the correctness of LogPlayer by TLA+. In particular, our TLA+ specification shows that LogPlayer guarantees in-order exactly-once delivery of WAL entries to the storage shards, even in the presence of shards or LogPlayer failures. Our experiments show LogPlayer is capable of efficient delivery with sub-millisecond latency, and it is significantly more efficient than Apache Kafka for designing a WAL system with exactly-once guarantee.

cs.DC↗

arXiv2018

Session Guarantees with Raft and Hybrid Logical Clocks

Eventual consistency is a popular consistency model for geo-replicated data stores. Although eventual consistency provides high performance and availability, it can cause anomalies that make programming complex for application developers. Session guarantees can remove some of these anomalies while causing much lower overhead compared with stronger consistency models. In this paper, we provide a protocol for providing session guarantees for NuKV, a key-value store developed for services with very high availability and performance requirements at eBay. NuKV relies on the Raft protocol for replication inside datacenters, and uses eventual consistency for replication among datacenters. We provide modified versions of conventional session guarantees to avoid the problem of slowdown cascades in systems with large numbers of partitions. We also use Hybrid Logical Clocks to eliminate the need for delaying write operations to satisfy session guarantees. Our experiments show that our protocol provides session guarantees with a negligible overhead when compared with eventual consistency.

cs.DC↗

arXiv2016

Design Principles for Scaling Multi-core OLTP Under High Contention

Although significant recent progress has been made in improving the multi-core scalability of high throughput transactional database systems, modern systems still fail to achieve scalable throughput for workloads involving frequent access to highly contended data. Most of this inability to achieve high throughput is explained by the fundamental constraints involved in guaranteeing ACID --- the addition of cores results in more concurrent transactions accessing the same contended data for which access must be serialized in order to guarantee isolation. Thus, linear scalability for contended workloads is impossible. However, there exist flaws in many modern architectures that exacerbate their poor scalability, and result in throughput that is much worse than fundamentally required by the workload. In this paper we identify two prevalent design principles that limit the multi-core scalability of many (but not all) transactional database systems on contended workloads: the multi-purpose nature of execution threads in these systems, and the lack of advanced planning of data access. We demonstrate the deleterious results of these design principles by implementing a prototype system, ORTHRUS, that is motivated by the principles of separation of database component functionality and advanced planning of transactions. We find that these two principles alone result in significantly improved scalability on high-contention workloads, and an order of magnitude increase in throughput for a non-trivial subset of these contended workloads.

cs.DB↗

arXiv2013

Mechanics of Tunable Helices and Geometric Frustration in Biomimetic Seashells

Helical structures are ubiquitous in nature and engineering, ranging from DNA molecules to plant tendrils, from sea snail shells to nanoribbons. While the helical shapes in natural and engineered systems often exhibit nearly uniform radius and pitch, helical shell structures with changing radius and pitch, such as seashells and some plant tendrils, adds to the variety of this family of aesthetic beauty. Here we develop a comprehensive theoretical framework for tunable helical morphologies, and report the first biomimetic seashell-like structure resulting from mechanics of geometric frustration. In previous studies, the total potential energy is everywhere minimized when the system achieves equilibrium. In this work, however, the local energy minimization cannot be realized because of the geometric incompatibility, and hence the whole system deforms into a shape with a global energy minimum whereby the energy in each segment may not necessarily be locally optimized. This novel approach can be applied to develop materials and devices of tunable geometries with a range of applications in nano/biotechnology.

math-ph↗