arXiv Machine Learning
Jun 8
Uncertainty-Guided Label Rebalancing for CPS Safety Monitoring
arXiv:2603. 25670v3 Announce Type: replace Abstract: Safety monitoring is essential for Cyber-Physical Systems (CPSs).
Confounding-Valid Conformal Inference for Counterfactual KPIs in Wireless Networks
The paper introduces Confounding-Valid Counterfactual Conformal Inference (CV‑CCI), a method that merges abundant observational telemetry with limited randomized data to answer network operators’ ‘what‑if’ questions about key performance indicators (KPIs). CV‑CCI uses the General Synthetic‑Powered Inference principle to maintain finite‑sample coverage guarantees even when hidden confounding is present, while producing tighter prediction sets than existing baselines. Experiments on two radio access network control tasks demonstrate the method’s validity under hidden confounding and its improved efficiency.
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arXiv Machine Learning
Jun 5
Event Detection for Parameter-to-KPI Dependency Learning for AI-RAN
arXiv:2606. 06459v1 Announce Type: new Abstract: Next-generation wireless networks are expected to rely on multiple concurrent AI-driven control functions that optimize different network objectives simultaneously, particularly in AI-integrated and open radio access network architectures such as AI Radio Access Network (AI-RAN) and Open Radio Access Network (O-RAN).
arXiv Machine Learning
Aug 4
CENTILE: A Telemetry Foundation Model Evaluated by the Decisions It Drives
arXiv:2608. 01725v1 Announce Type: cross Abstract: Modern computing and networking infrastructure emits telemetry continuously, yet operators convert it into decisions with a separate predictor per task, entity, and horizon.
arXiv Machine Learning
Sep 15
Conformal Individual Treatment Effect Estimation under Networked Interference
arXiv:2609.15254v1 Announce Type: cross Abstract: Conformal counterfactual prediction constructs prediction sets with finite-sample coverage guarantees for counterfactual outcomes and individual trea...
arXiv AI
Aug 11
TelemetrySuffBench: Is Agent Telemetry Sufficient for Failure-Origin Diagnosis?
arXiv:2608. 07899v1 Announce Type: new Abstract: Agent systems increasingly expose execution traces, yet telemetry that reveals a failure may still be inadequate for identifying where that failure originated.
arXiv Machine Learning
5d ago
Counterfactual Online Conformal Prediction Under Adaptive Logging
The paper addresses the failure of online conformal prediction when predictions influence actions that determine which outcomes are used for calibration. It introduces Propensity-Weighted Online Conformal Prediction (PW‑OCP), an inverse‑propensity‑weighted recursion that debiases calibration, and a doubly robust variant (DR‑OCP) that further reduces bias. Experiments on synthetic decision tasks, open bandit data, and financial rebalancing demonstrate that PW‑OCP and DR‑OCP improve counterfactual coverage and downstream regret while preserving prediction‑set sharpness.
arXiv AI
Jun 15
Capability Minimization as a Safety Primitive: Risk-Aware Causal Gating for Least-Privilege LLM Agents
arXiv:2606. 13884v1 Announce Type: new Abstract: Modern decision systems increasingly rely on learned components whose outputs may be confident yet wrong, exposing downstream actions to costly errors.
arXiv Machine Learning
Jul 3
Prediction Sets for Counterfactual Decisions: Coverage, Optimality, and Conformal Prediction
arXiv:2607. 02206v1 Announce Type: cross Abstract: Predictions are increasingly used to guide high-stakes decisions, from treatment selection to policy making.
arXiv AI
Jun 6
DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN
arXiv:2606. 06261v1 Announce Type: cross Abstract: O-RAN enables a disaggregated baseband stack with programmable functions that communicate over standardized open interfaces.
arXiv Machine Learning
Jun 15
PERRY: Policy Evaluation with Confidence Intervals using Auxiliary Data
arXiv:2507. 20068v2 Announce Type: replace Abstract: Off-policy evaluation (OPE) methods estimate the value of a new reinforcement learning (RL) policy prior to deployment.
arXiv AI
Jun 2
COPF: An Online Framework for Deployment-Stable Counterfactual Fairness in Evolving Graphs
arXiv:2606. 00700v1 Announce Type: cross Abstract: Online link recommendation on evolving graphs is performative: by choosing which candidate links to show users, the system changes which links form and what feedback it later observes.
arXiv Machine Learning
Jul 10
Beyond Fixed False Discovery Rates: Post-Hoc Conformal Selection with E-Variables
arXiv:2604. 11305v3 Announce Type: replace Abstract: Conformal selection (CS) uses calibration data to identify test inputs whose unobserved outcomes are likely to satisfy a pre-specified minimal quality requirement, while controlling the false discovery rate (FDR).