arXiv subjects

Jessie Chen

Publications and source records attributed to Jessie Chen.

5 recordsLinked to original sources

arXiv2026

Evaluating Enterprise Analytics Agents: An End-to-End, Trace-Backed Methodology

Enterprise analytics agents are not only text-to-SQL systems. They interpret business intent and choose metric definitions. They select data sources, execute tools, inspect results, and produce natural-language answers. Those answers may influence operational, financial, or executive decisions. Grading final answers hides where these agents fail. A plausible answer can use the wrong source of truth. It can skip a required decomposition, claim causality without support, or change its table interpretation across repeated runs. We present an end-to-end evaluation methodology for analytics agents. The methodology grades agent behavior across three families: semantic understanding, execution quality, and reliability. Grading uses question banks with human-written golden answers, repeated runs, and runtime traces. Each run first passes a run-validity check, then receives tiered, abstention-aware scores that feed a decision framework rather than a release gate. We instantiate the methodology on a controlled internal analytics agent at a large online marketplace. The case study uses 50 analytics questions, two anonymous model configurations, and three randomized repetitions per configuration, yielding 300 traces. The higher-capability configuration reduced early refusal from 73% to 0% and increased real-data answers from 21% to 73%. However, it also exhausted the tool-round budget on 16% of runs. It overran the schema-exploration budget on 77% of traces. It changed its table interpretation on 41 of 50 questions. On finance questions with structured golden answers, source table use and escalation improved, but canonical decomposition remained weak in both configurations. These results show why trust in analytics agents requires end-to-end, trace-backed evaluation rather than SQL correctness or final answer quality alone.

cs.SE↗

arXiv2026

Validation of HRV Studio: A Transparent and Quality-Control-Aware Platform for Heart Rate Variability Analysis

Reproducibility of heart rate variability (HRV) analysis is limited by differences in preprocessing and computational conventions across software platforms. We developed HRV Studio, an open-source PyQt6-based desktop application integrating transparent HRV analysis with automated quality-control (QC) diagnostics. Validation included large-scale agreement with NeuroKit2, targeted Kubios benchmarking, spectral-method comparison, synthetic perturbation testing, recording-duration sensitivity analysis, and arrhythmia-focused QC stress testing. HRV Studio showed near-identical agreement for the widely used time-domain indices RMSSD and SDNN under matched conditions. In the primary five-minute NeuroKit2 comparison, frequency-domain median relative errors were 1.35% for LF, 0.18% for HF, and 1.41% for LF/HF, while VLF remained more convention-sensitive (37.79%). Nonlinear Poincar\'e indices also demonstrated high consistency. Sequence-harmonized Kubios benchmarking confirmed near-identical agreement for time-domain and nonlinear indices and strong agreement for most frequency-domain measures. Extended ten-minute analyses reproduced the same overall pattern with lower disagreement for some convention-sensitive spectral outputs. Synthetic and arrhythmia stress tests maintained 100% numerical stability while consistently triggering QC warnings. Overall, HRV Studio provides a transparent and reproducible platform for HRV research, with strong cross-platform consistency when NN sequences, preprocessing, and analytical conventions are harmonized. Stress-test results indicate computational robustness rather than clinical validation.

physics.med-ph↗

arXiv2026

Optimal Sensor Placement in Gaussian Processes via Column Subset Selection

Gaussian process regression uses data measured at sensor locations to reconstruct a spatially dependent function with quantified uncertainty. However, if only a limited number of sensors can be deployed, it is important to determine how to optimally place the sensors to minimize a measure of the uncertainty in the reconstruction. We consider the Bayesian D-optimal criterion to determine the optimal sensor locations by choosing sensors from a candidate set of sensors. Since this is an NP-hard problem, our approach models sensor placement as a column subset selection problem (CSSP) on the covariance matrix, computed using the kernel function on the candidate sensor points. We propose an algorithm that uses the Golub-Klema-Stewart framework (GKS) to select sensors and provide an analysis of lower bounds on the D-optimality of these sensor placements. To reduce the computational cost in the GKS step, we propose and analyze algorithms for the D-optimal sensor placements using Nystr\"om approximations on the covariance matrix. Moreover, we propose several algorithms that select sensors via Nystr\"om approximation of the covariance matrix, utilizing the randomized Nystr\"om approximation, random pivoted Cholesky and greedy pivoted Cholesky. We demonstrate the performance of our method on two applications: thin liquid film dynamics and sea surface temperature.

math.NA↗

arXiv2025

Governing Automated Strategic Intelligence

Military and economic strategic competitiveness between nation-states will increasingly be defined by the capability and cost of their frontier artificial intelligence models. Among the first areas of geopolitical advantage granted by such systems will be in automating military intelligence. Much discussion has been devoted to AI systems enabling new military modalities, such as lethal autonomous weapons, or making strategic decisions. However, the ability of a country of "CIA analysts in a data-center" to synthesize diverse data at scale, and its implications, have been underexplored. Multimodal foundation models appear on track to automate strategic analysis previously done by humans. They will be able to fuse today's abundant satellite imagery, phone-location traces, social media records, and written documents into a single queryable system. We conduct a preliminary uplift study to empirically evaluate these capabilities, then propose a taxonomy of the kinds of ground truth questions these systems will answer, present a high-level model of the determinants of this system's AI capabilities, and provide recommendations for nation-states to remain strategically competitive within the new paradigm of automated intelligence.

cs.AI↗

arXiv2019

Heterogeneous Collaborative Filtering

Recommendation system is important to a content sharing/creating social network. Collaborative filtering is a widely-adopted technology in conventional recommenders, which is based on similarity between positively engaged content items involving the same users. Conventional collaborative filtering (CCF) suffers from cold start problem and narrow content diversity. We propose a new recommendation approach, heterogeneous collaborative filtering (HCF) to tackle these challenges at the root, while keeping the strength of collaborative filtering. We present two implementation algorithms of HCF for content recommendation and content dissemination. Experiment results demonstrate that our approach improve the recommendation quality in a real world social network for content creating and sharing.

cs.IR↗