Good Papers

Showing papers from The Institute of Statistical Mathematics Show all papers

45%Niche pick
?Niche pickVote to see the score

Incorporating Neural Network Structure in the Bayesian Learning Rule

Eiki Shimizu, Mohammad Emtiyaz Khan, Thomas Möllenhoff

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score

Toward Minimal-dimensional Convex Calibrated Surrogate Losses for Classification with Rejection

Yuzhou Cao, Han Bao, Bo An

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
70%Highly rated
?Highly ratedVote to see the score

Learning Survival Models with Right-Censored Reporting Delays

Joint parametric modeling of event and reporting hazards with right-censored delays yields consistent estimators via Monte Carlo EM and improves timely risk evaluation under administrative censoring via transfer learning.

Yuta Shikuri, Hironori Fujisawa

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
5/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 5 of 20 reviewers recommend it
lenient 4/5
medium 0/10
strict 1/5
71%Highly rated
?Highly ratedVote to see the score

Flow Matching from Viewpoint of Proximal Operators

Optimal transport conditional flow matching equals exact proximal operators via extended Brenier potentials without density assumptions, yields explicit vector fields, converges with batch size, and contracts exponentially normal to manifold-supported targets.

Kenji Fukumizu, Wei Huang, Han Bao, Shuntuo Xu and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
7/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 7 of 20 reviewers recommend it
lenient 2/5
medium 3/10
strict 2/5