Active model learning and diverse action sampling for task and motion planning

The objective of this work is to augment the basic abilities of a robot by learning to use new sensorimotor primitives to enable the solution of complex long-horizon problems. Solving long-horizon problems in complex domains requires flexible generative planning that can combine primitive abilities in novel combinations to solve problems as they arise in the world. In order to plan to combine primitive actions, we must have models of the preconditions and effects of those actions: under what circumstances will executing this primitive achieve some particular effect in the world? We use, and develop novel improvements on, state-of-the-art methods for active learning and sampling. We use Gaussian process methods for learning the conditions of operator effectiveness from small numbers of expensive training examples collected by experimentation on a robot. We develop adaptive sampling methods for generating diverse elements of continuous sets (such as robot configurations and object poses) during planning for solving a new task, so that planning is as efficient as possible. We demonstrate these methods in an integrated system, combining newly learned models with an efficient continuous-space robot task and motion planner to learn to solve long horizon problems more efficiently than was previously possible.Comment: Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain. https://www.youtube.com/playlist?list=PLoWhBFPMfSzDbc8CYelsbHZa1d3uz-W_

Linear and Range Counting under Metric-based Local Differential Privacy

Local differential privacy (LDP) enables private data sharing and analytics without the need for a trusted data collector. Error-optimal primitives (for, e.g., estimating means and item frequencies) under LDP have been well studied. For analytical tasks such as range queries, however, the best known error bound is dependent on the domain size of private data, which is potentially prohibitive. This deficiency is inherent as LDP protects the same level of indistinguishability between any pair of private data values for each data downer. In this paper, we utilize an extension of $\epsilon$-LDP called Metric-LDP or $E$-LDP, where a metric $E$ defines heterogeneous privacy guarantees for different pairs of private data values and thus provides a more flexible knob than $\epsilon$ does to relax LDP and tune utility-privacy trade-offs. We show that, under such privacy relaxations, for analytical workloads such as linear counting, multi-dimensional range counting queries, and quantile queries, we can achieve significant gains in utility. In particular, for range queries under $E$-LDP where the metric $E$ is the $L^1$-distance function scaled by $\epsilon$, we design mechanisms with errors independent on the domain sizes; instead, their errors depend on the metric $E$, which specifies in what granularity the private data is protected. We believe that the primitives we design for $E$-LDP will be useful in developing mechanisms for other analytical tasks, and encourage the adoption of LDP in practice

Learning Scheduling Algorithms for Data Processing Clusters

Efficiently scheduling data processing jobs on distributed compute clusters requires complex algorithms. Current systems, however, use simple generalized heuristics and ignore workload characteristics, since developing and tuning a scheduling policy for each workload is infeasible. In this paper, we show that modern machine learning techniques can generate highly-efficient policies automatically. Decima uses reinforcement learning (RL) and neural networks to learn workload-specific scheduling algorithms without any human instruction beyond a high-level objective such as minimizing average job completion time. Off-the-shelf RL techniques, however, cannot handle the complexity and scale of the scheduling problem. To build Decima, we had to develop new representations for jobs' dependency graphs, design scalable RL models, and invent RL training methods for dealing with continuous stochastic job arrivals. Our prototype integration with Spark on a 25-node cluster shows that Decima improves the average job completion time over hand-tuned scheduling heuristics by at least 21%, achieving up to 2x improvement during periods of high cluster load

Metadata-Aware Query Processing over Data Streams

Many modern applications need to process queries over potentially infinite data streams to provide answers in real-time. This dissertation proposes novel techniques to optimize CPU and memory utilization in stream processing by exploiting metadata on streaming data or queries. It focuses on four topics: 1) exploiting stream metadata to optimize SPJ query operators via operator configuration, 2) exploiting stream metadata to optimize SPJ query plans via query-rewriting, 3) exploiting workload metadata to optimize parameterized queries via indexing, and 4) exploiting event constraints to optimize event stream processing via run-time early termination. The first part of this dissertation proposes algorithms for one of the most common and expensive query operators, namely join, to at runtime identify and purge no-longer-needed data from the state based on punctuations. Exploitations of the combination of punctuation and commonly-used window constraints are also studied. Extensive experimental evaluations demonstrate both reduction on memory usage and improvements on execution time due to the proposed strategies. The second part proposes herald-driven runtime query plan optimization techniques. We identify four query optimization techniques, design a lightweight algorithm to efficiently detect the optimization opportunities at runtime upon receiving heralds. We propose a novel execution paradigm to support multiple concurrent logical plans by maintaining one physical plan. Extensive experimental study confirms that our techniques significantly reduce query execution times. The third part deals with the shared execution of parameterized queries instantiated from a query template. We design a lightweight index mechanism to provide multiple access paths to data to facilitate a wide range of parameterized queries. To withstand workload fluctuations, we propose an index tuning framework to tune the index configurations in a timely manner. Extensive experimental evaluations demonstrate the effectiveness of the proposed strategies. The last part proposes event query optimization techniques by exploiting event constraints such as exclusiveness or ordering relationships among events extracted from workflows. Significant performance gains are shown to be achieved by our proposed constraint-aware event processing techniques