The paper proposes a robust, multi-stage pipeline combining rule-based classification and machine learning to map noisy retail product names to standardized consumption categories, finding that simple…

This paper studies a dynamic assortment problem on a two-sided service platform with incomplete information and heterogeneous customers, and develops a data-driven algorithm to learn parameters and op…

This paper studies a dynamic assortment problem on a two-sided service platform with incomplete information and heterogeneous customers, and develops a data-driven algorithm to learn parameters and op…

The paper presents BEATS, a human-in-the-loop LLM framework for bootstrapping product attribute taxonomies from scratch.

The paper introduces a novel paradigm where a fine-tuned LLM acts as an ancillary predictor to forecast likely advertisers, significantly improving ad recommendation systems by augmenting candidate ge…

TIMEGATE introduces a resource-aware policy layer that manages continual ML adaptation by dynamically budgeting time and evaluation resources, achieving significant compute and energy savings without…

This paper proposes a novel framework, DS-MLP, for click-through rate prediction in online advertising and recommendation systems.

The paper introduces a new anytime-valid inference method to correct split selection in online decision trees, providing robust statistical guarantees for streaming data that existing methods lack.

The paper proposes DAMEL, a dual-axis multi-expert learning algorithm that simultaneously reduces both prediction bias and variance in class-imbalanced learning by leveraging multiple experts across b…

The paper introduces WebKnoGraph, an open-source framework for systematically evaluating internal linking strategies on websites by modeling the site as a graph and assessing trade-offs between author…

The paper introduces PRAXIS, a novel algorithm that efficiently approximates the computation of 'Rashomon sets' for decision trees, significantly reducing memory and runtime complexity.

The study demonstrates that conditioning AI brand recommendations on a user's persona significantly alters the recommended product set, particularly for mid-market brands, and this effect is largest o…

This paper proposes and evaluates the KAN-LSTM model, demonstrating that Kolmogorov-Arnold Networks (KANs) significantly outperform traditional deep learning models for accurate and parameter-efficien…

This paper analyzes the Solana Rug Pull ecosystem by creating a large-scale, manually verified dataset of fraudulent tokens, identifying three key behavioral patterns, and characterizing the resulting…

This paper develops an explainable and deployable machine learning system for highly accurate phishing detection across diverse, heterogeneous datasets, achieving up to 99.78% accuracy using transform…

This paper introduces a machine learning system that detects phishing emails by analyzing contextual features from the entire email body content, achieving 95.41% accuracy using Logistic Regression.

The paper proposes a lightweight hybrid MLP framework that uses structural URL features to achieve highly accurate and computationally efficient real-time phishing URL detection, outperforming several…

The paper proposes a medication-aware framework that integrates medication adherence with financial transaction monitoring to significantly improve the detection of financial exploitation in Alzheimer…

The paper proposes a market-feedback adaptive retrieval system for frozen LLMs in financial RAG, significantly improving event-impact prediction by learning which evidence sources to prioritize.

The paper introduces BlueFin, a challenging benchmark for evaluating LLM agents on complex financial spreadsheet tasks, finding that even frontier models perform poorly, scoring less than 50% on avera…