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We propose a graph-topological approach to active learning that directly targets the core challenge of exploration versus exploitation under scarce label budgets. To guide exploration, we introduce a coreset construction algorithm based on…

Machine Learning · Computer Science 2026-04-17 Harris Hardiman-Mostow , Jack Mauro , Adrien Weihs , Andrea L. Bertozzi

Correctness of program transformations in extended lambda calculi with a contextual semantics is usually based on reasoning about the operational semantics which is a rewrite semantics. A successful approach to proving correctness is the…

Programming Languages · Computer Science 2010-12-23 Conrad Rau , Manfred Schmidt-Schauß

In reinforcement learning, the TD($\lambda$) algorithm is a fundamental policy evaluation method with an efficient online implementation that is suitable for large-scale problems. One practical drawback of TD($\lambda$) is its sensitivity…

Machine Learning · Statistics 2014-12-23 Aviv Tamar , Panos Toulis , Shie Mannor , Edoardo M. Airoldi

A key functionality of emerging connected autonomous systems such as smart cities, smart transportation systems, and the industrial Internet-of-Things, is the ability to process and learn from data collected at different physical locations.…

Machine Learning · Computer Science 2021-03-09 Konstantinos Gatsis

We estimate the maximal length of interactions between strategies in HO/N game semantics, in the spirit of the work by Schwichtenberg and Beckmann for the length of reduction in simply typed lambdacalculus. Because of the operational…

Logic in Computer Science · Computer Science 2011-12-15 Pierre Clairambault

Text-to-CAD code generation is a long-horizon task that translates textual instructions into long sequences of interdependent operations. Existing methods typically decode text directly into executable code (e.g., bpy) without explicitly…

Artificial Intelligence · Computer Science 2026-04-14 Shengjie Gong , Wenjie Peng , Hongyuan Chen , Gangyu Zhang , Yunqing Hu , Huiyuan Zhang , Shuangping Huang , Tianshui Chen

Scaling large language models (LLMs) has driven significant advancements, yet it faces diminishing returns and escalating energy demands. This work explores how test-time compute (TTC) can serve as an energy-efficient complement to…

Machine Learning · Computer Science 2025-11-11 Yunho Jin , Gu-Yeon Wei , David Brooks

A statistically principled way of conducting weighted network analysis is still lacking. Comparison of different populations of weighted networks is hard because topology is inherently dependent on wiring cost, where cost is defined as the…

Molecular Networks · Quantitative Biology 2015-05-27 Cedric E. Ginestet , Thomas E. Nichols , Ed T. Bullmore , Andrew Simmons

This paper introduces a new machine architecture for evaluating lambda expressions using the normal-order reduction, which guarantees that every lambda expression will be evaluated if the expression has its normal form and the system has…

Logic in Computer Science · Computer Science 2010-11-05 Anton Salikhmetov

Several modern applications involve huge graphs and require fast answers to reachability queries. In more than two decades since first proposals, several approaches have been presented adopting on-line searches, hop labelling or transitive…

Data Structures and Algorithms · Computer Science 2016-11-09 Nicolas Boria , Gianpiero Cabodi , Paolo Camurati , Marco Palena , Paolo Pasini , Stefano Quer

The framework of Light Logics has been extensively studied to control the complexity of higher-order functional programs. We propose an extension of this framework to multithreaded programs with side effects, focusing on the case of…

Programming Languages · Computer Science 2012-09-27 Antoine Madet

Living systems balance energetic efficiency with the capacity for path-dependent effects. We introduce Tangential Action Spaces (TAS), a geometric framework that models embodied agents as hierarchies of manifolds linked by projections from…

Systems and Control · Electrical Eng. & Systems 2025-09-15 Marcel Blattner

Graph embedding provides an efficient solution for graph analysis by converting the graph into a low-dimensional space which preserves the structure information. In contrast to the graph structure data, the i.i.d. node embedding can be…

Machine Learning · Computer Science 2017-05-16 Hongyun Cai , Vincent W. Zheng , Kevin Chen-Chuan Chang

The semantics of alternating-time temporal logic (ATL) and the more expressive alternating-time {\mu}-calculus (AMC) is standardly given in terms of concurrent game frames (CGF). The information required to interpret AMC formulas is…

Logic in Computer Science · Computer Science 2025-06-03 Daniel Hausmann , Merlin Humml , Simon Prucker , Lutz Schröder

Large language models (LLMs) have rapidly progressed into general-purpose agents capable of solving a broad spectrum of tasks. However, current models remain inefficient at reasoning: they apply fixed inference-time compute regardless of…

This paper analyzes repeated version of the bilateral trade model where the independent payoff relevant private information of the buyer and the seller is correlated across time. Using this setup it makes the following five contributions.…

Theoretical Economics · Economics 2022-02-10 Rohit Lamba

Token representations in high-dimensional latent spaces often exhibit redundancy, limiting computational efficiency and reducing structural coherence across model layers. Hierarchical latent space folding introduces a structured…

Computation and Language · Computer Science 2025-08-11 Fenella Harcourt , Naderdel Piero , Gilbert Sutherland , Daphne Holloway , Harriet Bracknell , Julian Ormsby

Graph Edit Distance (GED) is defined as the minimum cost transformation of one graph into another and is a widely adopted metric for measuring the dissimilarity between graphs. The major problem of GED is that its computation is NP-hard,…

Machine Learning · Computer Science 2026-02-24 Francesco Leonardi , Markus Orsi , Jean-Louis Reymond , Kaspar Riesen

The effectiveness of the machine learning methods for real-world tasks depends on the proper structure of the modeling pipeline. The proposed approach is aimed to automate the design of composite machine learning pipelines, which is…

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