9 papers
Detecting Diffusion-Generated Time Series Under Generator Shift
Zhi Wen Soi, Aditya Shankar, Gert Lek +4
The boundary between real and diffusion-generated time series is becoming increasingly difficult to draw, yet detection in this domain remains underexplored, especially when the ge…
TMPDiff: Temporal Mixed-Precision for Diffusion Models
Basile Lewandowski, Simon Kurz, Aditya Shankar +3
Diffusion models are the go-to method for Text-to-Image generation, but their iterative denoising processes has high inference latency. Quantization reduces compute time by using l…
Harpoon: Generalised Manifold Guidance for Conditional Tabular Diffusion
Aditya Shankar, Yuandou Wang, Rihan Hai +1
Generating tabular data under conditions is critical to applications requiring precise control over the generative process. Existing methods rely on training-time strategies that d…
TimeWak: Temporal Chained-Hashing Watermark for Time Series Data
Zhi Wen Soi, Chaoyi Zhu, Fouad Abiad +4
Synthetic time series generated by diffusion models enable sharing privacy-sensitive datasets, such as patients' functional MRI records. Key criteria for synthetic data include hig…
WaveStitch: Flexible and Fast Conditional Time Series Generation with Diffusion Models
Aditya Shankar, Lydia Y. Chen, Arie van Deursen +1
Generating temporal data under conditions is crucial for forecasting, imputation, and generative tasks. Such data often has metadata and partially observed signals that jointly inf…
Federated Time Series Generation on Feature and Temporally Misaligned Data
Zhi Wen Soi, Chenrui Fan, Aditya Shankar +2
Distributed time series data presents a challenge for federated learning, as clients often possess different feature sets and have misaligned time steps. Existing federated time se…
Parameterizing Federated Continual Learning for Reproducible Research
Bart Cox, Jeroen Galjaard, Aditya Shankar +2
Federated Learning (FL) systems evolve in heterogeneous and ever-evolving environments that challenge their performance. Under real deployments, the learning tasks of clients can a…
Share Secrets for Privacy: Confidential Forecasting with Vertical Federated Learning
Aditya Shankar, Jérémie Decouchant, Dimitra Gkorou +2
Vertical federated learning (VFL) is a promising area for time series forecasting in many applications, such as healthcare and manufacturing. Critical challenges to address include…
SiloFuse: Cross-silo Synthetic Data Generation with Latent Tabular Diffusion Models
Aditya Shankar, Hans Brouwer, Rihan Hai +1
Synthetic tabular data is crucial for sharing and augmenting data across silos, especially for enterprises with proprietary data. However, existing synthesizers are designed for ce…