SCALE: Scalable Cross-Attention Learning with Extrapolation for Agentic Workflow Scheduling

Agentic Large Language Model (LLM) systems decompose complex tasks into workflow Directed Acyclic Graphs (DAGs) whose primitives must be scheduled on heterogeneous clusters. Existing deep reinforcement learning (DRL) schedulers are tied to a fixed cluster size and require retraining whenever the number of servers changes. We propose SCALE (Scalable Cross-Attention Learning with Extrapolation), a DRL scheduler that generalizes to unseen cluster scales without fine-tuning. SCALE employs a cross-attention pointer network where task features query against server features, so the architecture accepts any number of servers by construction. We observe, however, that permutation-invariant architecture alone does not guarantee good performance at new scales - the attention feature undergoes distribution shift as the server count grows. To counter this, we introduce Structured Representation Regularization (SRR): a decorrelation loss combined with a KL penalty toward the standard normal, which keeps feature statistics stable regardless of input size. Trained on 16 nodes and tested directly on 32 and 48 nodes, SCALE reduces average response time by 8.9% at N=48 relative to the same architecture without SRR, confirming that explicit regularization is necessary to close the scale-generalization gap.

Enhanced Estimation Techniques for Certified Radii in Randomized Smoothing

This paper presents novel methods for estimating certified radii in randomized smoothing, a technique crucial for certifying the robustness of neural networks against adversarial perturbations. Our proposed techniques significantly improve the accuracy of certified test-set accuracy by providing tighter bounds on the certified radii. We introduce advanced algorithms for both discrete and continuous domains, demonstrating their effectiveness on CIFAR-10 and ImageNet datasets. The new methods show considerable improvements over existing approaches, particularly in reducing discrepancies in certified radii estimates. We also explore the impact of various hyperparameters, including sample size, standard deviation, and temperature, on the performance of these methods. Our findings highlight the potential for more efficient certification processes and pave the way for future research on tighter confidence sequences and improved theoretical frameworks. The study concludes with a discussion of potential future directions, including enhanced estimation techniques for discrete domains and further theoretical advancements to bridge the gap between empirical and theoretical performance in randomized smoothing.

Efficient Representations for High-Cardinality Categorical Variables in Machine Learning

High\-cardinality categorical variables pose significant challenges in machine learning, particularly in terms of computational efficiency and model interpretability. Traditional one\-hot encoding often results in high\-dimensional sparse feature spaces, increasing the risk of overfitting and reducing scalability. This paper introduces novel encoding techniques, including means encoding, low\-rank encoding, and multinomial logistic regression encoding, to address these challenges. These methods leverage sufficient representations to generate compact and informative embeddings of categorical data. We conduct rigorous theoretical analyses and empirical validations on diverse datasets, demonstrating significant improvements in model performance and computational efficiency compared to baseline methods. The proposed techniques are particularly effective in domains requiring scalable solutions for large datasets, paving the way for more robust and efficient applications in machine learning.

Automating Date Format Detection for Data Visualization

Data preparation, specifically date parsing, is a significant bottleneck in analytic workflows. To address this, we present two algorithms, one based on minimum entropy and the other on natural language modeling that automatically derive date formats from string data. These algorithms achieve over 90% accuracy on a large corpus of data columns, streamlining the data preparation process within visualization environments. The minimal entropy approach is particularly fast, providing interactive feedback. Our methods simplify date format extraction, making them suitable for integration into data visualization tools and databases.

Harmonizing Metadata of Language Resources for Enhanced Querying and Accessibility

This paper addresses the harmonization of metadata from diverse repositories of language resources (LRs). Leveraging linked data and RDF techniques, we integrate data from multiple sources into a unified model based on DCAT and META-SHARE OWL ontology. Our methodology supports text-based search, faceted browsing, and advanced SPARQL queries through Linghub, a newly developed portal. Real user queries from the Corpora Mailing List (CML) were evaluated to assess Linghub capability to satisfy actual user needs. Results indicate that while some limitations persist, many user requests can be successfully addressed. The study highlights significant metadata issues and advocates for adherence to open vocabularies and standards to enhance metadata harmonization. This initial research underscores the importance of API-based access to LRs, promoting machine usability and data subset extraction for specific purposes, paving the way for more efficient and standardized LR utilization.

Automating Neural Architecture Design without Search

Neural structure search (NAS), as the mainstream approach to automate deep neural architecture design, has achieved much success in recent years. However, the performance estimation component adhering to NAS is often prohibitively costly, which leads to the enormous computational demand. Though a large number of efforts have been dedicated to alleviating this pain point, no consensus has been made yet on which is optimal. In this paper, we study the automated architecture design from a new perspective that eliminates the need to sequentially evaluate each neural architecture generated during algorithm execution. Specifically, the proposed approach is built by learning the knowledge of high-level experts in designing state-of-the-art architectures, and then the new architecture is directly generated upon the knowledge learned. We implemented the proposed approach by using a graph neural network for link prediction and acquired the knowledge from NAS-Bench-101. Compared to existing peer competitors, we found a competitive network with minimal cost. In addition, we also utilized the learned knowledge from NAS-Bench-101 to automate architecture design in the DARTS search space, and achieved 97.82% accuracy on CIFAR10, and 76.51% top-1 accuracy on ImageNet consuming only $2\times10^{-4}$ GPU days. This also demonstrates the high transferability of the proposed approach, and can potentially lead to a new, more computationally efficient paradigm in this research direction.