11 Search Results for "Yang, Pengfei"

Document
DOI: 10.4230/LIPIcs.SoCG.2026.90
Better Sampling Bounds for Restricted Delaunay Triangulations and a Star-Shaped Property for Restricted Voronoi Cells

Authors: Jonathan Richard Shewchuk

Published in: LIPIcs, Volume 367, 42nd International Symposium on Computational Geometry (SoCG 2026)

Abstract
The restricted Delaunay triangulation of a closed surface Σ and a finite point set V ⊂ Σ is a subcomplex of the Delaunay tetrahedralization of V whose triangles approximate Σ. It is well known that if V is a sufficiently dense sample of a smooth Σ, then the union of the restricted Delaunay triangles is homeomorphic to Σ. We show that an ε-sample with ε ≤ 0.3245 suffices. By comparison, Dey proves it for a 0.18-sample; our improved sampling bound reduces the number of sample points required by a factor of 3.25. More importantly, we improve a related sampling bound of Cheng et al. for Delaunay surface meshing, reducing the number of sample points required by a factor of 21. The first step of our homeomorphism proof is particularly interesting: we show that for a 0.44-sample, the restricted Voronoi cell of each site v ∈ V is homeomorphic to a disk, and the orthogonal projection of the cell onto T_vΣ (the plane tangent to Σ at v) is star-shaped.
Cite as

Jonathan Richard Shewchuk. Better Sampling Bounds for Restricted Delaunay Triangulations and a Star-Shaped Property for Restricted Voronoi Cells. In 42nd International Symposium on Computational Geometry (SoCG 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 367, pp. 90:1-90:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{shewchuk:LIPIcs.SoCG.2026.90,
  author =	{Shewchuk, Jonathan Richard},
  title =	{{Better Sampling Bounds for Restricted Delaunay Triangulations and a Star-Shaped Property for Restricted Voronoi Cells}},
  booktitle =	{42nd International Symposium on Computational Geometry (SoCG 2026)},
  pages =	{90:1--90:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-418-5},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{367},
  editor =	{Ahn, Hee-Kap and Hoffmann, Michael and Nayyeri, Amir},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2026.90},
  URN =		{urn:nbn:de:0030-drops-258961},
  doi =		{10.4230/LIPIcs.SoCG.2026.90},
  annote =	{Keywords: Restricted Delaunay triangulation, restricted Voronoi diagram, surface sampling, surface mesh generation, surface reconstruction, \epsilon-sample, homeomorphism}
}
Document
DOI: 10.4230/LIPIcs.ICDT.2026.14
Lower Bounds for the Algorithmic Complexity of Learned Indexes

Authors: Luis Alberto Croquevielle, Roman Sokolovskii, and Thomas Heinis

Published in: LIPIcs, Volume 365, 29th International Conference on Database Theory (ICDT 2026)

Abstract
Learned index structures aim to accelerate queries by training machine learning models to approximate the rank function associated with a database attribute. While effective in practice, their theoretical limitations are not fully understood. We present a framework for proving lower bounds on query time for learned indexes, expressed in terms of their space overhead and parameterized by the model class used for approximation. Our formulation captures a broad family of one-dimensional learned indexes, including most existing designs, as piecewise model-based predictors. We solve the problem of lower bounding query time in two steps: first, we use probabilistic tools to control the effect of sampling when the database attribute is drawn from a probability distribution. Then, we analyze the approximation-theoretic problem of how to optimally represent a cumulative distribution function with approximators from a given model class. Within this framework, we derive lower bounds under a range of modeling and distributional assumptions, paying particular attention to the case of piecewise linear and piecewise constant model classes, which are common in practical implementations. Our analysis shows how tools from approximation theory, such as quantization and Kolmogorov widths, can be leveraged to formalize the space-time trade-offs inherent to learned index structures. The resulting bounds illuminate core limitations of these methods.
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Luis Alberto Croquevielle, Roman Sokolovskii, and Thomas Heinis. Lower Bounds for the Algorithmic Complexity of Learned Indexes. In 29th International Conference on Database Theory (ICDT 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 365, pp. 14:1-14:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{croquevielle_et_al:LIPIcs.ICDT.2026.14,
  author =	{Croquevielle, Luis Alberto and Sokolovskii, Roman and Heinis, Thomas},
  title =	{{Lower Bounds for the Algorithmic Complexity of Learned Indexes}},
  booktitle =	{29th International Conference on Database Theory (ICDT 2026)},
  pages =	{14:1--14:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-413-0},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{365},
  editor =	{ten Cate, Balder and Funk, Maurice},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2026.14},
  URN =		{urn:nbn:de:0030-drops-256285},
  doi =		{10.4230/LIPIcs.ICDT.2026.14},
  annote =	{Keywords: Learned Indexes, Stochastic Processes, Approximation Theory}
}
Document
DOI: 10.4230/LIPIcs.WADS.2025.45
Fast Kd-Trees for the Kullback-Leibler Divergence and Other Decomposable Bregman Divergences

Authors: Tuyen Pham and Hubert Wagner

Published in: LIPIcs, Volume 349, 19th International Symposium on Algorithms and Data Structures (WADS 2025)

Abstract
The contributions of the paper span theoretical and implementational results. First, we prove that Kd-trees can be extended to ℝ^d with the distance measured by an arbitrary Bregman divergence. Perhaps surprisingly, this shows that the triangle inequality is not necessary for correct pruning in Kd-trees. Second, we offer an efficient algorithm and C++ implementation for nearest neighbour search for decomposable Bregman divergences. The implementation supports the Kullback-Leibler divergence (relative entropy) which is a popular distance between probability vectors and is commonly used in statistics and machine learning. This is a step toward broadening the usage of computational geometry algorithms. Our benchmarks show that our implementation efficiently handles both exact and approximate nearest neighbour queries. Compared to a linear search, we achieve two orders of magnitude speedup for practical scenarios in dimension up to 100. Our solution is simpler and more efficient than competing methods.
Cite as

Tuyen Pham and Hubert Wagner. Fast Kd-Trees for the Kullback-Leibler Divergence and Other Decomposable Bregman Divergences. In 19th International Symposium on Algorithms and Data Structures (WADS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 349, pp. 45:1-45:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{pham_et_al:LIPIcs.WADS.2025.45,
  author =	{Pham, Tuyen and Wagner, Hubert},
  title =	{{Fast Kd-Trees for the Kullback-Leibler Divergence and Other Decomposable Bregman Divergences}},
  booktitle =	{19th International Symposium on Algorithms and Data Structures (WADS 2025)},
  pages =	{45:1--45:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-398-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{349},
  editor =	{Morin, Pat and Oh, Eunjin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WADS.2025.45},
  URN =		{urn:nbn:de:0030-drops-242766},
  doi =		{10.4230/LIPIcs.WADS.2025.45},
  annote =	{Keywords: Kd-tree, k-d tree, nearest neighbour search, Bregman divergence, decomposable Bregman divergence, KL divergence, relative entropy, cross entropy, Shannon’s entropy}
}
Document
DOI: 10.4230/OASIcs.Manzini.7
Algorithms for Computing Very Large BWTs: a Short Survey

Authors: Diego Díaz-Domínguez, Lavinia Egidi, Veronica Guerrini, Felipe A. Louza, and Giovanna Rosone

Published in: OASIcs, Volume 131, The Expanding World of Compressed Data: A Festschrift for Giovanni Manzini's 60th Birthday (2025)

Abstract
The Burrows-Wheeler Transform (BWT) is a fundamental string transformation that, although initially introduced for data compression, has been extensively utilized across various domains, including text indexing and pattern matching within large datasets. Although the BWT construction is linear, the constants make the task impractical for large datasets, and as highlighted by Ferragina et al. [Paolo Ferragina et al., 2012], "to use it, one must first build it!". Thus, the construction of the BWT remains a significant challenge. For these reasons, during the past three decades there has been a succession of new algorithms for its construction using techniques that work in external memory or that use text compression. In this survey, we revise some of the most important advancements and tools presented in the past years for computing large BWTs exploiting external memory or text compression approaches without using additional information about the data.
Cite as

Diego Díaz-Domínguez, Lavinia Egidi, Veronica Guerrini, Felipe A. Louza, and Giovanna Rosone. Algorithms for Computing Very Large BWTs: a Short Survey. In The Expanding World of Compressed Data: A Festschrift for Giovanni Manzini's 60th Birthday. Open Access Series in Informatics (OASIcs), Volume 131, pp. 7:1-7:28, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{diazdominguez_et_al:OASIcs.Manzini.7,
  author =	{D{\'\i}az-Dom{\'\i}nguez, Diego and Egidi, Lavinia and Guerrini, Veronica and Louza, Felipe A. and Rosone, Giovanna},
  title =	{{Algorithms for Computing Very Large BWTs: a Short Survey}},
  booktitle =	{The Expanding World of Compressed Data: A Festschrift for Giovanni Manzini's 60th Birthday},
  pages =	{7:1--7:28},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-390-4},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{131},
  editor =	{Ferragina, Paolo and Gagie, Travis and Navarro, Gonzalo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Manzini.7},
  URN =		{urn:nbn:de:0030-drops-239151},
  doi =		{10.4230/OASIcs.Manzini.7},
  annote =	{Keywords: Burrows-Wheeler transform, Extended Burrows-Wheeler transform, external memory, text compression, longest common prefix}
}
Document
DOI: 10.4230/LIPIcs.SAT.2025.30
Bridging Language Models and Symbolic Solvers via the Model Context Protocol

Authors: Stefan Szeider

Published in: LIPIcs, Volume 341, 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)

Abstract
This paper presents the MCP Solver, a system that bridges large language models with symbolic solvers through the Model Context Protocol (MCP). The system includes a server and a client component. The server provides an interface to constraint programming (via MiniZinc Python), propositional satisfiability and maximum satisfiability (both via PySAT), and SAT modulo Theories (via Python Z3). The client contains an agent that connects to the server via MCP and uses a language model to autonomously translate problem statements (given in English) into encodings through an incremental editing process and runs the solver. Our experiments demonstrate that this neurosymbolic integration effectively combines the natural language understanding of language models with robust solving capabilities across multiple solving paradigms.
Cite as

Stefan Szeider. Bridging Language Models and Symbolic Solvers via the Model Context Protocol. In 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 341, pp. 30:1-30:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{szeider:LIPIcs.SAT.2025.30,
  author =	{Szeider, Stefan},
  title =	{{Bridging Language Models and Symbolic Solvers via the Model Context Protocol}},
  booktitle =	{28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)},
  pages =	{30:1--30:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-381-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{341},
  editor =	{Berg, Jeremias and Nordstr\"{o}m, Jakob},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2025.30},
  URN =		{urn:nbn:de:0030-drops-237649},
  doi =		{10.4230/LIPIcs.SAT.2025.30},
  annote =	{Keywords: Large Language Models, Agents, Constraint Programming, Satisfiability Solvers, Maximum Satisfiability, SAT Modulo Theories, Model Context Protocol}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2025.36
Efficient Neural Network Verification via Order Leading Exploration of Branch-and-Bound Trees

Authors: Guanqin Zhang, Kota Fukuda, Zhenya Zhang, H.M.N. Dilum Bandara, Shiping Chen, Jianjun Zhao, and Yulei Sui

Published in: LIPIcs, Volume 333, 39th European Conference on Object-Oriented Programming (ECOOP 2025)

Abstract
The vulnerability of neural networks to adversarial perturbations has necessitated formal verification techniques that can rigorously certify the quality of neural networks. As the state-of-the-art, branch-and-bound (BaB) is a "divide-and-conquer" strategy that applies off-the-shelf verifiers to sub-problems for which they perform better. While BaB can identify the sub-problems that are necessary to be split, it explores the space of these sub-problems in a naive "first-come-first-served" manner, thereby suffering from an issue of inefficiency to reach a verification conclusion. To bridge this gap, we introduce an order over different sub-problems produced by BaB, concerning with their different likelihoods of containing counterexamples. Based on this order, we propose a novel verification framework Oliva that explores the sub-problem space by prioritizing those sub-problems that are more likely to find counterexamples, in order to efficiently reach the conclusion of the verification. Even if no counterexample can be found in any sub-problem, it only changes the order of visiting different sub-problems and so will not lead to a performance degradation. Specifically, Oliva has two variants, including Oliva^GR, a greedy strategy that always prioritizes the sub-problems that are more likely to find counterexamples, and Oliva^SA, a balanced strategy inspired by simulated annealing that gradually shifts from exploration to exploitation to locate the globally optimal sub-problems. We experimentally evaluate the performance of Oliva on 690 verification problems spanning over 5 models with datasets MNIST and CIFAR-10. Compared to the state-of-the-art approaches, we demonstrate the speedup of Oliva for up to 25× in MNIST, and up to 80× in CIFAR-10.
Cite as

Guanqin Zhang, Kota Fukuda, Zhenya Zhang, H.M.N. Dilum Bandara, Shiping Chen, Jianjun Zhao, and Yulei Sui. Efficient Neural Network Verification via Order Leading Exploration of Branch-and-Bound Trees. In 39th European Conference on Object-Oriented Programming (ECOOP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 333, pp. 36:1-36:29, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{zhang_et_al:LIPIcs.ECOOP.2025.36,
  author =	{Zhang, Guanqin and Fukuda, Kota and Zhang, Zhenya and Bandara, H.M.N. Dilum and Chen, Shiping and Zhao, Jianjun and Sui, Yulei},
  title =	{{Efficient Neural Network Verification via Order Leading Exploration of Branch-and-Bound Trees}},
  booktitle =	{39th European Conference on Object-Oriented Programming (ECOOP 2025)},
  pages =	{36:1--36:29},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-373-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{333},
  editor =	{Aldrich, Jonathan and Silva, Alexandra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2025.36},
  URN =		{urn:nbn:de:0030-drops-233281},
  doi =		{10.4230/LIPIcs.ECOOP.2025.36},
  annote =	{Keywords: neural network verification, branch and bound, counterexample potentiality, simulated annealing, stochastic optimization}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2025.19
Profile-Guided Field Externalization in an Ahead-Of-Time Compiler

Authors: Sebastian Kloibhofer, Lukas Makor, Peter Hofer, David Leopoldseder, and Hanspeter Mössenböck

Published in: LIPIcs, Volume 333, 39th European Conference on Object-Oriented Programming (ECOOP 2025)

Abstract
Field externalization is a technique to reduce the footprint of objects by removing fields that most frequently contain zero or null. While researchers have developed ways to bring this optimization into the Java world, these have been limited to research compilers or virtual machines for embedded systems. In this work, we present a novel field externalization technique that uses information from static analysis and profiling to determine externalizable fields. During compilation, we remove those fields and define companion classes. These are used in case of non-default-value writes to the externalized fields. Our approach also correctly handles synchronization to prevent issues in multithreaded environments. We integrated our approach into the modern Java ahead-of-time compiler GraalVM Native Image. We conducted an evaluation on a diverse set of benchmarks that includes standard and microservice-based benchmarks. For standard benchmarks, our approach reduces the total allocated bytes by 2.76% and the maximum resident set size (max-RSS) by 2.55%. For microservice benchmarks, we achieved a reduction of 6.88% for normalized allocated bytes and 2.45% for max-RSS. We computed these improvements via the geometric mean. The median reductions are are 1.46% (alloc. bytes) and 0.22% (max-RSS) in standard benchmarks, as well as 3.63% (alloc. bytes) and 0.20% (max-RSS) in microservice benchmarks.
Cite as

Sebastian Kloibhofer, Lukas Makor, Peter Hofer, David Leopoldseder, and Hanspeter Mössenböck. Profile-Guided Field Externalization in an Ahead-Of-Time Compiler. In 39th European Conference on Object-Oriented Programming (ECOOP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 333, pp. 19:1-19:32, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{kloibhofer_et_al:LIPIcs.ECOOP.2025.19,
  author =	{Kloibhofer, Sebastian and Makor, Lukas and Hofer, Peter and Leopoldseder, David and M\"{o}ssenb\"{o}ck, Hanspeter},
  title =	{{Profile-Guided Field Externalization in an Ahead-Of-Time Compiler}},
  booktitle =	{39th European Conference on Object-Oriented Programming (ECOOP 2025)},
  pages =	{19:1--19:32},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-373-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{333},
  editor =	{Aldrich, Jonathan and Silva, Alexandra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2025.19},
  URN =		{urn:nbn:de:0030-drops-233121},
  doi =		{10.4230/LIPIcs.ECOOP.2025.19},
  annote =	{Keywords: compilation, instrumentation, profiling, fields, externalization, memory footprint reduction, memory footprint optimization}
}
Document
DOI: 10.4230/LIPIcs.CPM.2025.7
FL-RMQ: A Learned Approach to Range Minimum Queries

Authors: Paolo Ferragina and Filippo Lari

Published in: LIPIcs, Volume 331, 36th Annual Symposium on Combinatorial Pattern Matching (CPM 2025)

Abstract
We address the problem of designing and implementing a data structure for the Range Minimum Query problem. We show a surprising connection between this classical problem and the geometry of a properly defined set of points in the Cartesian plane. Building on this insight, we hinge upon a well-known result in Computational Geometry to introduce the first RMQ solution that exploits (i.e., learns) the distribution of such 2D-points via proper error-bounded linear approximations. Because of these features, we name the resulting data structure: Fully-Learned RMQ, shortly FL-RMQ. We prove theoretical bounds for its space usage and query time, covering both worst-case scenarios and average-case performance for uniformly distributed inputs. These bounds compare favorably with the ones achievable by the best-known indexing solutions (i.e., the ones that allow access to the indexed array), especially when the input data follow some geometric regularities that we characterize in the paper, thus providing principled evidence of FL-RMQ being a novel data-aware solution to the RMQ problem. We corroborate our theoretical findings with a wide set of experiments showing that FL-RMQ offers more robust space-time trade-offs than the other known practical indexing solutions on both artificial and real-world datasets. We believe that our novel approach to the RMQ problem is noteworthy not only for its interesting space-time trade-offs, but also because it is flexible enough to be applied easily to the encoding variant of RMQ (i.e., the one that does not allow access to the indexed array), and moreover, because it paves the way to research opportunities on possibly other problems.
Cite as

Paolo Ferragina and Filippo Lari. FL-RMQ: A Learned Approach to Range Minimum Queries. In 36th Annual Symposium on Combinatorial Pattern Matching (CPM 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 331, pp. 7:1-7:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{ferragina_et_al:LIPIcs.CPM.2025.7,
  author =	{Ferragina, Paolo and Lari, Filippo},
  title =	{{FL-RMQ: A Learned Approach to Range Minimum Queries}},
  booktitle =	{36th Annual Symposium on Combinatorial Pattern Matching (CPM 2025)},
  pages =	{7:1--7:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-369-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{331},
  editor =	{Bonizzoni, Paola and M\"{a}kinen, Veli},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CPM.2025.7},
  URN =		{urn:nbn:de:0030-drops-231014},
  doi =		{10.4230/LIPIcs.CPM.2025.7},
  annote =	{Keywords: Range-Minimum query, Learned data structures, Compact data structures, Experimental results}
}
Document
DOI: 10.4230/LIPIcs.FORC.2025.14
Model Ensembling for Constrained Optimization

Authors: Ira Globus Harris, Varun Gupta, Michael Kearns, and Aaron Roth

Published in: LIPIcs, Volume 329, 6th Symposium on Foundations of Responsible Computing (FORC 2025)

Abstract
Many instances of decision making under objective uncertainty can be decomposed into two steps: predicting the objective function and then optimizing for the best feasible action under the estimate of the objective vector. We study the problem of ensembling models for optimization of uncertain linear objectives under arbitrary constraints. We imagine we are given a collection of predictive models mapping a feature space to multi-dimensional real-valued predictions, which form the coefficients of a linear objective that we would like to optimize. We give two ensembling methods that can provably result in transparent decisions that strictly improve on all initial policies. The first method operates in the "white box" setting in which we have access to the underlying prediction models and the second in the "black box" setting in which we only have access to the induced decisions (in the downstream optimization problem) of the constituent models, but not their underlying point predictions. They are transparent or trustworthy in the sense that the user can reliably predict long-term ensemble rewards even if the instance by instance predictions are imperfect.
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Ira Globus Harris, Varun Gupta, Michael Kearns, and Aaron Roth. Model Ensembling for Constrained Optimization. In 6th Symposium on Foundations of Responsible Computing (FORC 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 329, pp. 14:1-14:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{globusharris_et_al:LIPIcs.FORC.2025.14,
  author =	{Globus Harris, Ira and Gupta, Varun and Kearns, Michael and Roth, Aaron},
  title =	{{Model Ensembling for Constrained Optimization}},
  booktitle =	{6th Symposium on Foundations of Responsible Computing (FORC 2025)},
  pages =	{14:1--14:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-367-6},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{329},
  editor =	{Bun, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2025.14},
  URN =		{urn:nbn:de:0030-drops-231412},
  doi =		{10.4230/LIPIcs.FORC.2025.14},
  annote =	{Keywords: model ensembling, trustworthy AI, decision-making under uncertainty}
}
Document
Vision
DOI: 10.4230/TGDK.1.1.3
Knowledge Engineering Using Large Language Models

Authors: Bradley P. Allen, Lise Stork, and Paul Groth

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract
Knowledge engineering is a discipline that focuses on the creation and maintenance of processes that generate and apply knowledge. Traditionally, knowledge engineering approaches have focused on knowledge expressed in formal languages. The emergence of large language models and their capabilities to effectively work with natural language, in its broadest sense, raises questions about the foundations and practice of knowledge engineering. Here, we outline the potential role of LLMs in knowledge engineering, identifying two central directions: 1) creating hybrid neuro-symbolic knowledge systems; and 2) enabling knowledge engineering in natural language. Additionally, we formulate key open research questions to tackle these directions.
Cite as

Bradley P. Allen, Lise Stork, and Paul Groth. Knowledge Engineering Using Large Language Models. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 3:1-3:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{allen_et_al:TGDK.1.1.3,
  author =	{Allen, Bradley P. and Stork, Lise and Groth, Paul},
  title =	{{Knowledge Engineering Using Large Language Models}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{3:1--3:19},
  ISSN =	{2942-7517},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.3},
  URN =		{urn:nbn:de:0030-drops-194777},
  doi =		{10.4230/TGDK.1.1.3},
  annote =	{Keywords: knowledge engineering, large language models}
}
Document
DOI: 10.4230/LIPIcs.CONCUR.2020.8
A Near-Linear-Time Algorithm for Weak Bisimilarity on Markov Chains

Authors: David N. Jansen, Jan Friso Groote, Ferry Timmers, and Pengfei Yang

Published in: LIPIcs, Volume 171, 31st International Conference on Concurrency Theory (CONCUR 2020)

Abstract
This article improves the time bound for calculating the weak/branching bisimulation minimisation quotient on state-labelled discrete-time Markov chains from O(m n) to an expected-time O(m log⁴ n), where n is the number of states and m the number of transitions. For these results we assume that the set of state labels AP is small (|AP| ∈ O(m/n log⁴ n)). It follows the ideas of Groote et al. (ACM ToCL 2017) in combination with an efficient algorithm to handle decremental strongly connected components (Bernstein et al., STOC 2019).
Cite as

David N. Jansen, Jan Friso Groote, Ferry Timmers, and Pengfei Yang. A Near-Linear-Time Algorithm for Weak Bisimilarity on Markov Chains. In 31st International Conference on Concurrency Theory (CONCUR 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 171, pp. 8:1-8:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{jansen_et_al:LIPIcs.CONCUR.2020.8,
  author =	{Jansen, David N. and Groote, Jan Friso and Timmers, Ferry and Yang, Pengfei},
  title =	{{A Near-Linear-Time Algorithm for Weak Bisimilarity on Markov Chains}},
  booktitle =	{31st International Conference on Concurrency Theory (CONCUR 2020)},
  pages =	{8:1--8:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-160-3},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{171},
  editor =	{Konnov, Igor and Kov\'{a}cs, Laura},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CONCUR.2020.8},
  URN =		{urn:nbn:de:0030-drops-128209},
  doi =		{10.4230/LIPIcs.CONCUR.2020.8},
  annote =	{Keywords: Behavioural Equivalence, weak Bisimulation, Markov Chain}
}
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