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- research-articleMay 2024
Low-Latency Adaptive Distributed Stream Join System Based on a Flexible Join Model
Proceedings of the ACM on Management of Data (PACMMOD), Volume 2, Issue 3Article No.: 150, Pages 1–27https://doi.org/10.1145/3654953Stream join is a fundamental operation in stream processing and has attracted extensive research due to its large resource consumption and serious impact on system performance. As the theoretical basis of stream join systems, the stream join model ...
- research-articleNovember 2023
Concurrent Composition for Interactive Differential Privacy with Adaptive Privacy-Loss Parameters
CCS '23: Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications SecurityPages 1949–1963https://doi.org/10.1145/3576915.3623128In this paper, we study the concurrent composition of interactive mechanisms with adaptively chosen privacy-loss parameters. In this setting, the adversary can interleave queries to existing interactive mechanisms, as well as create new ones. We prove ...
- research-articleJune 2023
Towards a feature-based didactic framework for generating individualized programming tasks for an e-learning environment
ECSEE '23: Proceedings of the 5th European Conference on Software Engineering EducationPages 246–255https://doi.org/10.1145/3593663.3593677Adaptive programming tasks are a promising approach for personalized learning that adapts to each student’s unique needs and abilities. However, developing effective adaptive programming tasks can be challenging, particularly when it comes to selecting ...
- ArticleMay 2023
Behavioural Theory of Reflective Algorithms
AbstractThis “journal-first” paper presents a summary of the behavioural theory of reflective sequential algorithms (RSAs), i.e. sequential algorithms that can modify their own behaviour. The theory comprises a set of language-independent postulates ...
- research-articleMarch 2024
Adaptive clustering using kernel density estimators
The Journal of Machine Learning Research (JMLR), Volume 24, Issue 1Article No.: 275, Pages 12918–12973We derive and analyze a generic, recursive algorithm for estimating all splits in a finite cluster tree as well as the corresponding clusters. We further investigate statistical properties of this generic clustering algorithm when it receives level set ...
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- research-articleNovember 2023
Are Adaptive Galerkin Schemes Dissipative?
Adaptive Galerkin numerical schemes integrate time-dependent partial differential equations with a finite number of basis functions, and a subset of them is selected at each time step. This subset changes over time discontinuously according to the ...
- short-paperNovember 2022
Fast and adaptive BFT state machine replication
Middleware '22 Doctoral Symposium: Proceedings of the 23rd International Middleware Conference Doctoral SymposiumPages 7–10https://doi.org/10.1145/3569950.3569963Recently, Byzantine fault-tolerant (BFT) state machine replication (SMR) experiences renewed research interest with the rise of novel BFT SMR-based distributed ledger technologies (DLTs). In DLTs, BFT SMR is used as a core primitive for maintaining a ...
- research-articleSeptember 2022
An Optimal Approximation for Submodular Maximization Under a Matroid Constraint in the Adaptive Complexity Model
An Exponentially Faster Algorithm for Submodular Maximization Under a Matroid Constraint
This paper studies the problem of submodular maximization under a matroid constraint. It is known since the 1970s that the greedy algorithm obtains a constant-factor ...
In this paper, we study submodular maximization under a matroid constraint in the adaptive complexity model. This model was recently introduced in the context of submodular optimization to quantify the information theoretic complexity of black-box ...
- research-articleJuly 2022
Adaptive Massively Parallel Algorithms for Cut Problems
SPAA '22: Proceedings of the 34th ACM Symposium on Parallelism in Algorithms and ArchitecturesPages 23–33https://doi.org/10.1145/3490148.3538576We study the Weighted Min Cut problem in the Adaptive Massively Parallel Computation (AMPC) model. In 2019, Behnezhad et al. [3] introduced the AMPC model as an extension of the Massively Parallel Computation (MPC) model. In the past decade, research on ...
- research-articleMarch 2022
Adaptive Cognitive Training with Reinforcement Learning
ACM Transactions on Interactive Intelligent Systems (TIIS), Volume 12, Issue 1Article No.: 3, Pages 1–29https://doi.org/10.1145/3476777Computer-assisted cognitive training can help patients affected by several illnesses alleviate their cognitive deficits or healthy people improve their mental performance. In most computer-based systems, training sessions consist of graded exercises, ...
- research-articleJanuary 2022
Error Estimation and Adaptivity for Stochastic Collocation Finite Elements Part I: Single-Level Approximation
SIAM Journal on Scientific Computing (SISC), Volume 44, Issue 5Pages A3393–A3412https://doi.org/10.1137/21M1446745A general adaptive refinement strategy for solving linear elliptic partial differential equations with random data is proposed and analysed herein. The adaptive strategy extends the a posteriori error estimation framework introduced by Guignard and ...
- research-articleJanuary 2022
Multirate Partially Explicit Scheme for Multiscale Flow Problems
SIAM Journal on Scientific Computing (SISC), Volume 44, Issue 3Pages A1775–A1806https://doi.org/10.1137/21M1440293For time-dependent problems with high-contrast multiscale coefficients, the time step size for explicit methods is affected by the magnitude of the coefficient parameter. With a suitable construction of multiscale space, one can achieve a stable temporal ...
- research-articleJanuary 2022
Edge-Promoting Adaptive Bayesian Experimental Design for X-ray Imaging
SIAM Journal on Scientific Computing (SISC), Volume 44, Issue 3Pages B506–B530https://doi.org/10.1137/21M1409330This work considers sequential edge-promoting Bayesian experimental design for (discretized) linear inverse problems, exemplified by X-ray tomography. The process of computing a total variation--type reconstruction of the absorption inside the imaged body ...
- research-articleJanuary 2022
Convergence of Anisotropic Mesh Adaptation via Metric Optimization
SIAM Journal on Numerical Analysis (SINUM), Volume 60, Issue 3Pages 1281–1306https://doi.org/10.1137/20M1338721Adaptive finite element methods (AFEMs) are an increasingly common means of automatically controlling error in numerical simulations. Proofs of convergence and rate of convergence exist for AFEMs; however, these proofs typically rely upon a nested ...
- research-articleNovember 2021
Adaptive Two-Stage Bregman Method for Variational Inequalities
Cybernetics and Systems Analysis (KLU-CASA), Volume 57, Issue 6Pages 959–967https://doi.org/10.1007/s10559-021-00421-2AbstractThe authors analyze the two-stage Popov method with Bregman divergence and a new adaptive rule for choosing the step size, which does not require the Lipschitz constants to be known and operator values at additional points to be calculated. For ...
- research-articleJuly 2021
Lock-free Contention Adapting Search Trees
ACM Transactions on Parallel Computing (TOPC), Volume 8, Issue 2Article No.: 10, Pages 1–38https://doi.org/10.1145/3460874Concurrent key-value stores with range query support are crucial for the scalability and performance of many applications. Existing lock-free data structures of this kind use a fixed synchronization granularity. Using a fixed synchronization granularity ...
- short-paperJune 2021Best Demo
Dendrite: Bolt-on Adaptivity for Data Systems
SIGMOD '21: Proceedings of the 2021 International Conference on Management of DataPages 2726–2730https://doi.org/10.1145/3448016.3452755Client application workloads for data systems are known to vary in load and access patterns over time. This variability can place undue stress on data systems, tying up resources and degrading performance. To meet this challenge, systems must adapt by ...
- research-articleApril 2021
A General Multi-method Approach to Data-Driven Redesign of Tutoring Systems
- Yun Huang,
- Nikki G. Lobczowski,
- J. Elizabeth Richey,
- Elizabeth A. McLaughlin,
- Michael W. Asher,
- Judith M. Harackiewicz,
- Vincent Aleven,
- Kenneth R. Koedinger
LAK21: LAK21: 11th International Learning Analytics and Knowledge ConferencePages 161–172https://doi.org/10.1145/3448139.3448155Analytics of student learning data are increasingly important for continuous redesign and improvement of tutoring systems and courses. There is still a lack of general guidance on converting analytics into better system design, and on combining ...
- research-articleJanuary 2021
MetaGrad: adaptation using multiple learning rates in online learning
The Journal of Machine Learning Research (JMLR), Volume 22, Issue 1Article No.: 161, Pages 7261–7321We provide a new adaptive method for online convex optimization, MetaGrad, that is robust to general convex losses but achieves faster rates for a broad class of special functions, including exp-concave and strongly convex functions, but also various ...
- research-articleJanuary 2021
Piecewise Constant Decision Rules via Branch-and-Bound Based Scenario Detection for Integer Adjustable Robust Optimization
INFORMS Journal on Computing (INFORMS-IJOC), Volume 33, Issue 1Pages 390–400https://doi.org/10.1287/ijoc.2019.0934Multistage problems with uncertain parameters and integer decisions variables are among the most difficult applications of robust optimization (RO). The challenge in these problems is to find optimal here-and-now decisions, taking into account that the ...