Many modern machine learning problems require solving min-max saddle point games, whose computational complexity is NP-hard in general. To overcome this issue, most available algorithms aim for finding a first-order Nash equilibrium solution that always exists under mild assumptions. However, the proposed algorithms for obtaining such solutions are non-adaptive and also exhibit slow convergence rates in real settings. Further, most algorithms are centralized in nature and cannot be adapted to a decentralized architecture in a straightforward manner. This study aims to address these issues by introducing general two-step adaptive algorithms for obtaining first-order Nash equilibrium solutions of min-max games in both single-node and decentralized architectures. We also obtain the non-asymptotic convergence rates of the algorithms assuming the objective functions satisfy the weak Minty variational inequality condition which is standard in recent literature. Finally, we illustrate the performance of the proposed algorithms by using them to train neural networks which are more robust against adversarial attacks compared to neural networks trained using existing algorithms.

Machine-generated log data can provide valuable insights into many critical areas such as system failures, network security, and performance optimization. The increasing prominence of this data in both volume and complexity requires data mining approaches that are both scalable and flexible. In this paper, we propose a new approach for clustering machine-generated logs which contains a novel combination of the use of the coreset with user feedback. The coreset allows us to efficiently summarize the data in a principled manner such that performance after fitting model parameters on the coreset is similar to the performance that would have been achieved with the full dataset. Furthermore, the formal approach we propose allows users to incorporate two different types of feedback, in the forms of labels and pairwise constraints, to further improve results and better deal with the increasing complexity and variety of log datasets.

MOTIVATION: Computational methods are essential to extract actionable information from raw sequencing data, and to thus fulfill the promise of next-generation sequencing technology. Unfortunately, computational tools developed to call variants from human sequencing data disagree on many of their predictions, and current methods to evaluate accuracy and computational performance are ad hoc and incomplete. Agreement on benchmarking variant calling methods would stimulate development of genomic processing tools and facilitate communication among researchers. RESULTS: We propose SMaSH, a benchmarking methodology for evaluating germline variant calling algorithms. We generate synthetic datasets, organize and interpret a wide range of existing benchmarking data for real genomes and propose a set of accuracy and computational performance metrics for evaluating variant calling methods on these benchmarking data. Moreover, we illustrate the utility of SMaSH to evaluate the performance of some leading single-nucleotide polymorphism, indel and structural variant calling algorithms. AVAILABILITY AND IMPLEMENTATION: We provide free and open access online to the SMaSH tool kit, along with detailed documentation, at smash.cs.berkeley.edu

Newly-released web applications often succumb to a "Success Disaster," where overloaded database machines and resulting high response times destroy a previously good user experience. Unfortunately, the data independence provided by a traditional relational database system, while useful for agile development, only exacerbates the problem by hiding potentially expensive queries under simple declarative expressions. As a result, developers of these applications are increasingly abandoning relational databases in favor of imperative code written against distributed key/value stores, losing the many benefits of data independence in the process. Instead, we propose PIQL, a declarative language that also provides scale independence by calculating an upper bound on the number of key/value store operations that will be performed for any query. Coupled with a service level objective (SLO) compliance prediction model and PIQL's scalable database architecture, these bounds make it easy for developers to write success-tolerant applications that support an arbitrarily large number of users while still providing acceptable performance. In this paper, we present the PIQL query processing system and evaluate its scale independence on hundreds of machines using two benchmarks, TPC-W and SCADr.