8 papers
Full Conformal Prediction under Stochastic Non-Conformity Measure
Thanawat Sornwanee
The theory of full conformal prediction uses deterministic non-conformity measure, but modern usage of full conformal prediction often relies on machine learning training, making s…
ABC: Any-Subset Autoregression via Non-Markovian Diffusion Bridges in Continuous Time and Space
Gabe Guo, Thanawat Sornwanee, Lutong Hao +3
Generating continuous-time, continuous-space stochastic processes (e.g., videos, weather forecasts) conditioned on partial observations (e.g., first and last frames) is a fundament…
Unbiased Single-Queried Gradient for Combinatorial Objective
Thanawat Sornwanee
In a probabilistic reformulation of a combinatorial problem, we often face an optimization over a hypercube, which corresponds to the Bernoulli probability parameter for each binar…
Allocate Marginal Reviews to Borderline Papers Using LLM Comparative Ranking
Elliot L. Epstein, Rajat Dwaraknath, John Winnicki +1
This paper argues that large ML conferences should allocate marginal review capacity primarily to papers near the acceptance boundary, rather than spreading extra reviews via rando…
Differentiable Integer Linear Programming is not Differentiable & it's not a mere technical problem
Thanawat Sornwanee
We show how the differentiability method employed in the paper ``Differentiable Integer Linear Programming'', Geng, et al., 2025 as shown in its theorem 5 is incorrect. Moreover, t…
LLMs are Overconfident: Evaluating Confidence Interval Calibration with FermiEval
Elliot L. Epstein, John Winnicki, Thanawat Sornwanee +1
Large language models (LLMs) excel at numerical estimation but struggle to correctly quantify uncertainty. We study how well LLMs construct confidence intervals around their own an…