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Ben-Gurion University of the Negev

Academic institutioneurope · il
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Research library518linked papers
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Selected work

Representative Papers

Learning Small Decision Trees with Few Outliers: A Parameterized Perspective

Mar 24, 2024AAAI Conference on Artificial Intelligence

This paper studies learning small decision trees tolerant to at most $t$ misclassifications, focusing on two core variants: minimizing tree size (DTSO) and minimizing tree depth (DTDO). Within the parameterized complexity framework, we establish for the first time that both problems are W[1]-hard with respect to $s+y$ and $d+y$, where $y$ is the number of attributes. However, when parameterized by the misclassification tolerance $t$, both become fixed-parameter tractable (FPT), and we present the first FPT algorithm explicitly depending on $t$. We systematically characterize kernelization complexity, providing tight polynomial kernel existence and impossibility results, thereby completing the kernelization classification for DTSO and DTDO. Our main contributions are: (i) establishing precise computational complexity boundaries; (ii) revealing the “complexity-reducing” role of $t$, which shifts hardness from W[1]-hardness to FPT; and (iii) delivering theoretically complete algorithms and matching lower bounds.

4 citationsRead paper

Towards Open Diversity-Aware Social Interactions

Feb 17, 2025arXiv.org

In the digital era, the rapid proliferation of diverse populations, perspectives, and knowledge lacks corresponding adaptive mechanisms, leading to superficial social relationships and intensified echo chambers. Method: This study proposes and implements the “We Internet” platform, introducing— for the first time—the Diversity-Aware AI framework, which integrates sociology, ethics, and artificial intelligence. It establishes multidimensional modeling and representation learning methods for social diversity and designs a human-AI collaborative, ethics-driven algorithmic architecture with interpretable matching guidance. Contribution/Results: Empirical validation demonstrates that the framework significantly enhances cross-group understanding, mitigates filter bubbles, and deepens collaborative engagement. It provides both a theoretical foundation and an implementable paradigm for open, inclusive, and trustworthy social AI systems.

3 citationsRead paper

A Polynomial Kernel for Deletion to the Scattered Class of Cliques and Trees

Sep 21, 2024International Symposium on Algorithms and Computation

This paper studies the *scattered deletion* problem in graph modification: given a graph $G$ and an integer $k$, determine whether at most $k$ vertices can be deleted so that each connected component of the resulting graph is either a clique or a tree. This is the first nontrivial polynomial kernel for this dispersed target class. We develop a unified framework combining modular decomposition, structural graph analysis, and redundancy-based vertex compression to construct a deterministic polynomial kernel of size $O(k^5)$. Our result fills a fundamental gap in kernelization research for scattered deletion problems, achieves significant input size reduction, and provides a robust preprocessing foundation for designing efficient fixed-parameter tractable (FPT) algorithms.

2 citations1 influentialRead paper

AgentGuardian: Learning Access Control Policies to Govern AI Agent Behavior

Jan 15, 2026

This work addresses the vulnerability of AI agents to malicious inputs or internal errors, which can lead to unauthorized actions and hallucination-induced errors that compromise system security. To mitigate these risks, the paper proposes AgentGuardian, a context-aware adaptive access control framework that monitors agent execution traces to learn legitimate behavioral and input patterns. By integrating real-time input context with control-flow dependencies across multi-step tool invocations, AgentGuardian dynamically constrains agent operations. This approach uniquely combines control-flow analysis with behavioral modeling to effectively defend against agent misuse. Experimental evaluation on two real-world AI agent applications demonstrates that AgentGuardian efficiently detects malicious or misleading inputs, significantly reducing orchestration errors caused by hallucinations while preserving normal functionality.

2 citationsRead paper

Towards General Modality Translation with Contrastive and Predictive Latent Diffusion Bridge

Oct 23, 2025

This work addresses key limitations in cross-modal translation (MT)—including reliance on aligned dimensions, Gaussian prior assumptions, and modality-specific architectures—by proposing a universal, theoretically grounded solution. We introduce the Latent Denoising Diffusion Bridging Model (LDDBM), a framework enabling bidirectional translation between arbitrary modalities without requiring dimension-wise alignment or shared prior assumptions. LDDBM employs a domain-agnostic encoder-decoder architecture that jointly optimizes contrastive alignment loss and predictive loss within a shared latent space, augmented by a latent-space noise prediction mechanism to enhance training stability. Experiments demonstrate that LDDBM significantly outperforms state-of-the-art methods on diverse tasks—including multi-view-to-3D reconstruction, image super-resolution, and multi-view scene synthesis—establishing a new strong baseline for general-purpose cross-modal translation.

2 citationsRead paper
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