Cryptanalysis of Physically Unclonable Functions
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Updated
Jun 29, 2024 - Python
Cryptanalysis of Physically Unclonable Functions
Ensemble price forecasting with volatility prediction (XGBoost on cached features). Multiple simulated paths per request; CRPS scoring for calibration and sharpness. Synthetic price data for options and portfolio analytics. Python, XGBoost, NumPy, Pandas, properscoring, Pyth API, PostgreSQL, Pydantic, Docker.
Official pytorch implementation codes for NeurIPS-2023 accepted paper "Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation"
Model stacking for predictive ensembles
DINOv3 as a Frozen Encoder for CRPS-Oriented Probabilistic Rainfall Nowcasting (Neurips W4C Competition)
Distributional crypto-return forecasting via Wasserstein-geodesic extrapolation in quantile-function space. WGeo family wins 12/12 (asset × horizon) cells over 6.75y walk-forward CRPS vs GARCH and classical baselines. v0.4.
Official implementation of 'Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation' (DistVAE) with pytorch (NeurIPS 2023 accepted paper).
Interactive Streamlit dashboard to verify gridded rainfall/precipitation forecasts (deterministic & ensemble, NetCDF + GRIB2) against observations — POD/FAR/CSI/ETS/HSS, EDI/SEDI/EDS/SEDS, Brier/BSS/CRPS, FSS & CRA, reliability/ROC/rank/PIT/Taylor plots, per-cell score maps, bootstrap CIs, CSV/PNG export. Runs on bundled demo data.
Implementation of"Learning Quantile Functions for Temporal Point Processes with Recurrent Neural Splines" (@ AISTATS 2022)
Reproducibility artifact for 'Heads, Not Backbones: Output Heads Dominate Architectures on Fat-Tailed Returns' (arXiv:2606.30037)
Fast distributional random forests with CART, MMD, and sliced-Wasserstein splitting
Links calibrated deep probabilistic forecasting to reinforcement-learning monetary policy through a shared belief state. A CRPS-trained multi-scale LSTM supplies time-varying uncertainty to a POMDP central bank; realistic beliefs collapse the learned Taylor coefficient from ~1.1 to ~0.02 across every seed under both PPO and SAC.
Distributional forecasts scored with CRPS and PIT calibration, plus block-bootstrap tests that separate leaderboard skill from luck
Probabilistic time-series forecasting: from-scratch Kalman-filter state-space model vs ARIMA/GBM baselines, evaluated on CRPS and interval calibration.
Predict rarely measured soil properties (total N, CEC7) by learning the bounded parameters of known soil relations; spatial benchmark with QRF, TabM and TabICLv2 on public WoSIS data.
A modern, privacy-focused companion app designed to help people with Complex Regional Pain Syndrome (CRPS) document symptoms, identify patterns, and better understand their daily condition.
R2 — LLM calibration benchmark: 18-scenario Florida rainfall eval crossing a specificity gradient with a seasonal gradient, CRPS scoring, and ground-truth-free self-consistency checks.
Training-free probabilistic time-series forecasting with Conformal Seasonal Pools (CSP): a numpy-only empirical sampler with quantiles, prediction intervals, and rolling-origin backtesting.
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