6 papers
Prediction Bottlenecks Don't Discover Causal Structure (But Here's What They Actually Do)
Ankit Hemant Lade, Sai Krishna Jasti, Indar Kumar +1
A Mamba state-space model trained only for next-step prediction appears to recover Granger-causal structure through a simple readout , with early experiments…
Supervised Dimensionality Reduction Revisited: Why LDA on Frozen CNN Features Deserves a Second Look
Indar Kumar, Girish Karhana, Sai Krishna Jasti +1
Frozen pretrained image representations are widely used for transfer learning: a backbone is kept fixed, feature vectors are extracted, and a lightweight classifier is trained on t…
Regime-Calibrated Fleet Repositioning with a Spatial Queue-Regret Decomposition
Indar Kumar, Akanksha Tiwari
Ride-hailing and autonomous mobility-on-demand operators reposition idle supply before future demand is fully observed. We study a retrieval-calibrated predict-then-optimize approa…
Bias-Corrected Adaptive Conformal Inference for Multi-Horizon Time Series Forecasting
Ankit Lade, Sai Krishna J., Indar Kumar
Adaptive Conformal Inference (ACI) provides distribution-free prediction intervals with asymptotic coverage guarantees for time series under distribution shift. However, ACI only a…
PCA-Driven Adaptive Sensor Triage for Edge AI Inference
Ankit Hemant Lade, Sai Krishna Jasti, Nikhil Sinha +2
Multi-channel sensor networks in industrial IoT often exceed available bandwidth. We propose PCA-Triage, a streaming algorithm that converts incremental PCA loadings into proportio…
RG-TTA: Regime-Guided Meta-Control for Test-Time Adaptation in Streaming Time Series
Indar Kumar, Akanksha Tiwari, Sai Krishna Jasti +1
Test-time adaptation (TTA) enables neural forecasters to adapt to distribution shifts in streaming time series, but existing methods apply the same adaptation intensity regardless…