Export Citations
Publications
Save this search
Please login to be able to save your searches and receive alerts for new content matching your search criteria.
- research-article
Probabilistic invariant learning with randomized linear classifiers
NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing Systems•December 2023, Article No.: 3266, pp 74736-74752Designing models that are both expressive and preserve known invariances of tasks is an increasingly hard problem. Existing solutions tradeoff invariance for computational or memory resources. In this work, we show how to leverage randomness and design ...
- extended-abstract
CCO - Cloud Cost Optimizer
SYSTOR '23: Proceedings of the 16th ACM International Conference on Systems and Storage•June 2023, pp 137-137• https://doi.org/10.1145/3579370.3594746Cloud computing can be complex, but optimal management of it doesn't have to be. In this paper, we present the design and implementation of a scalable multi-Cloud Cost Optimizer (CCO) that calculates the optimal deployment scheme for a given workload ...
- Article
3-Valued Circuit SAT for STE with Automatic Refinement
Automated Technology for Verification and Analysis•October 2007, pp 457-473• https://doi.org/10.1007/978-3-540-75596-8_32AbstractSymbolic Trajectory Evaluation (STE) is a powerful technique for hardware model checking. It is based on a 3-valued symbolic simulation, using 0,1 and X (”unknown”), where the X is used to abstract away values of the circuit nodes.
Most STE tools ...
- research-article
SMEGA2: Distributed Asynchronous Deep Neural Network Training With a Single Momentum Buffer
ICPP '22: Proceedings of the 51st International Conference on Parallel Processing•August 2022, Article No.: 3, pp 1-10• https://doi.org/10.1145/3545008.3545010As the field of deep learning progresses, and neural networks become larger, training them has become a demanding and time consuming task. To tackle this problem, distributed deep learning must be used to scale the training of deep neural networks to ...
- research-article
DLACEP: A Deep-Learning Based Framework for Approximate Complex Event Processing
SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data•June 2022, pp 340-354• https://doi.org/10.1145/3514221.3526136Complex event processing (CEP) is employed to detect user-specified patterns of events in data streams. CEP mechanisms operate by maintaining all sets of events that can potentially be composed into a pattern match. This approach can be wasteful when ...
- research-article
AutoMon: Automatic Distributed Monitoring for Arbitrary Multivariate Functions
SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data•June 2022, pp 310-324• https://doi.org/10.1145/3514221.3517866Approaches for evaluating functions over distributed data streams are increasingly important as data sources become more geographically distributed. However, existing methodologies are limited to small classes of functions, requiring non-trivial effort ...
- research-article
HYPERSONIC: A Hybrid Parallelization Approach for Scalable Complex Event Processing
SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data•June 2022, pp 1093-1107• https://doi.org/10.1145/3514221.3517829The ability to promptly and efficiently detect arbitrarily complex patterns in massive real-time data streams is a crucial requirement in many modern applications. The ever-growing scale of these applications and the sophistication of the patterns ...
- Article
Communication-Efficient Distributed Online Prediction by Dynamic Model Synchronization
Machine Learning and Knowledge Discovery in Databases, pp 623-639• https://doi.org/10.1007/978-3-662-44848-9_40AbstractWe present the first protocol for distributed online prediction that aims to minimize online prediction loss and network communication at the same time. This protocol can be applied wherever a prediction-based service must be provided timely for ...
- research-article
DARLING: data-aware load shedding in complex event processing systems
Proceedings of the VLDB Endowment, Volume 15, Issue 3•November 2021, pp 541-554 • https://doi.org/10.14778/3494124.3494137Complex event processing (CEP) is widely employed to detect user-defined combinations, or patterns, of events in massive streams of incoming data. Numerous applications such as healthcare, fraud detection, and more, use CEP technologies to capture ...
- Article
Incremental Sensitivity Analysis for Kernelized Models
Machine Learning and Knowledge Discovery in Databases•September 2020, pp 383-398• https://doi.org/10.1007/978-3-030-67661-2_23AbstractDespite their superior accuracy to simpler linear models, kernelized models can be prohibitively expensive in applications where the training set changes frequently, since training them is computationally intensive. We provide bounds for the ...
- research-article
It's not what machines can learn, it's whatwe cannot teach
ICML'20: Proceedings of the 37th International Conference on Machine Learning•July 2020, Article No.: 1004, pp 10831-10841Can deep neural networks learn to solve any task, and in particular problems of high complexity? This question attracts a lot of interest, with recent works tackling computationally hard tasks such as the traveling salesman problem and satisfiability. In ...
- research-article
Memory Elasticity Benchmark
SYSTOR '20: Proceedings of the 13th ACM International Systems and Storage Conference•May 2020, pp 1-12• https://doi.org/10.1145/3383669.3398277Cloud computing handles a vast share of the world's computing, but it is not as efficient as it could be due to its lack of support for memory elasticity. An environment that supports memory elasticity can dynamically change the size of the application'...
- Article
Online Linear Models for Edge Computing
Machine Learning and Knowledge Discovery in Databases•September 2019, pp 645-661• https://doi.org/10.1007/978-3-030-46150-8_38AbstractMaintaining an accurate trained model on an infinite data stream is challenging due to concept drifts that render a learned model inaccurate. Updating the model periodically can be expensive, and so traditional approaches for computationally ...
- research-article
Real-Time Multi-Pattern Detection over Event Streams
SIGMOD '19: Proceedings of the 2019 International Conference on Management of Data•June 2019, pp 589-606• https://doi.org/10.1145/3299869.3319869Rapid advances in data-driven applications over recent years have intensified the need for efficient mechanisms capable of monitoring and detecting arbitrarily complex patterns in massive data streams. This task is usually performed by complex event ...
- research-article
Stochastic resource allocation
VEE 2019: Proceedings of the 15th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments•April 2019, pp 122-136• https://doi.org/10.1145/3313808.3313815Suboptimal resource utilization among public and private cloud providers prevents them from maximizing their economic potential. Long-term allocated resources are often idle when they might have been subleased for a short period. Alternatively, ...
- Article
Preventing Collusion in Cloud Computing Auctions
Economics of Grids, Clouds, Systems, and Services•September 2018, pp 24-38• https://doi.org/10.1007/978-3-030-13342-9_3AbstractCloud providers are moving towards auctioning cloud resources rather than renting them using fixed prices. Vickrey-Clarke-Groves (VCG) auctions are likely to be used for that purpose, since they maximize social welfare—the participants’ aggregate ...
- research-article
Lightweight Monitoring of Distributed Streams
ACM Transactions on Database Systems, Volume 43, Issue 2•June 2018, Article No.: 9, pp 1-37 • https://doi.org/10.1145/3226113As data becomes dynamic, large, and distributed, there is increasing demand for what have become known as distributed stream algorithms. Since continuously collecting the data to a central server and processing it there is infeasible, a common approach ...
- research-article
Efficient adaptive detection of complex event patterns
Proceedings of the VLDB Endowment, Volume 11, Issue 11•July 2018, pp 1346-1359 • https://doi.org/10.14778/3236187.3236190Complex event processing (CEP) is widely employed to detect occurrences of predefined combinations (patterns) of events in massive data streams. As new events are accepted, they are matched using some type of evaluation structure, commonly optimized ...
- research-article
Join query optimization techniques for complex event processing applications
Proceedings of the VLDB Endowment, Volume 11, Issue 11•July 2018, pp 1332-1345 • https://doi.org/10.14778/3236187.3236189Complex event processing (CEP) is a prominent technology used in many modern applications for monitoring and tracking events of interest in massive data streams. CEP engines inspect real-time information flows and attempt to detect combinations of ...