P. Van Hentenryck

CoCoNet: Convex Conformal Network for Multi-Dimensional Uncertainty Regions

Conformal prediction (CP) provides distribution-free coverage guarantees under exchangeability, but the efficiency of its prediction regions depends critically on the choice of the conformity score. This is particularly important in multivariate regression, where dependencies across output dimensions induce complex joi...

CoCopula: Conformal Calibration and Copula Aggregation for Reliable Renewable Energy Forecasting

A probabilistic aggregation and calibration framework that directly converts site-level probabilistic forecasts into reliable fleet-level forecasts and achieves performance comparable to directly calibrated system-level forecasts is presented, demonstrating its practical value when dedicated fleet-level forecasting mod...

Match the Canvas, Not Just the Answer: Predictability-Aware SFT for Diffusion Language Models

Diffusion language models (DLMs) enable flexible parallel generation by resolving tokens according to the model's evolving predictability rather than a fixed left-to-right order. This flexibility, however, introduces training choices absent from standard autoregressive teacher forcing: which reference tokens are visibl...

to Source Optimizations

Distributed Linear Programming on GPU Clusters at Extreme Scale

SHARDLP is presented, a distributed GPU LP solver that keeps the matrix and primal-dual state partitioned from sharded input through solution output and reaches the published criterion on nine of eleven instances, on the Google PDLP benchmark.

ID-PaS+: Identity-Aware Predict-and-Search for Solving General Mixed-Integer Linear Programs

This work extends the current Predict-and-Search (PaS) framework to parametric general MIPs and introduces ID-PaS+, an identity-aware learning framework that enables the ML model to handle heterogeneous variables more effectively.