4 citations · 10 across the 21 of their papers we have counts for
9 papers · 1 filter
A Large-Dimensional Analysis of Least-Squares Federated Learning via Generalized Total Variation Minimization
Ekkehard Schnoor, Malik Tiomoko, Alexander Jung
Federated learning over decentralized networks inherently trades off the suppression of local finite-sample noise through collaboration against the bias that collaboration introduc…
Instance-Adaptive Prompts as Context for Time-Series Foundation Models
Zehao Xiao, Shifeng Xie, Lei Zan +5
Longer histories can improve time-series foundation models (TSFMs), but require substantially higher inference cost. We therefore ask whether contextual information can be provided…
LLM-Generated Feature Pools for Time Series Anomaly Detection
Youssef Attia El Hili, Malik Tiomoko, Corinne Ancourt
We study how far a simple statistical pipeline can go on univariate time series anomaly detection under a strict selection protocol. The method extracts a small pool of statistics…
Tabby: An Open Pretraining Recipe for Time Series Foundation Models
Shifeng Xie, Bahaeddine Abdessalem, Zehao Xiao +9
In this report, we release Tabby, a long context probabilistic time series foundation model, together with a complete and open recipe of how it was built. Tabby adopts an encoder-o…
FlowTSFM: Turning Encoder Depth into Quantile Transport
Bahaeddine Abdessalem, Shifeng Xie, Zehao Xiao +6
Encoder-based time series foundation models (TSFMs) typically rely on deep stacks of independently parameterized Transformer layers, where only the final forecast is supervised and…
EvoCause: LLM-Guided Evolution of Causal Graphs for Root Cause Analysis
Lei Zan, Keli Zhang, Shifeng Xie +7
Modern telecommunication, cloud, and microservice systems emit correlated alarm cascades when components fail. Root cause analysis (RCA) aims to identify the small set of alarms th…
Post-Training in Time Series Foundation Models: A Unifying Framework
Shifeng Xie, Ambroise Odonnat, Zehao Xiao +7
Time series foundation models (TSFMs) have emerged as general-purpose models for time series analysis, but pretraining alone is often insufficient for reliable downstream deploymen…
-TCAV: A Unified Framework for Testing with Concept Activation Vectors
Ekkehard Schnoor, Jawher Said, Malik Tiomoko +2
Concept Activation Vectors (CAV) and the Testing with CAVs (TCAV) method are fundamental tools for concept-based explainability in deep learning, yet their practical utility is lim…
Characterization of Gaussian Universality Breakdown in High-Dimensional Empirical Risk Minimization
Chiheb Yaakoubi, Cosme Louart, Malik Tiomoko +1
We study high-dimensional convex empirical risk minimization (ERM) under general non-Gaussian data designs. By heuristically extending the Convex Gaussian Min-Max Theorem (CGMT) to…