Good Papers

Showing papers from Georgia State University Show all papers

57%Worth a look
?Worth a lookVote to see the score

Beyond 3 Million Tokens: A Multi-Modal Foundation Model for Full-Resolution Heliophysics

Sujit Roy, Johannes Schmude, Ata A Asanjan, Thorsten Kurth and 12 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
1/20 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: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score

Severity-Controlled Prediction Sets for Medication Recommendation

Yu Gu, Zijun Yu, Chi-Kuang Yeh, Xinyu Wang and 1 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

– ReadersNo votes yet
1/20 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: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score

RnR: a meta-solver for causal discovery in undersampled time series data

Mohammadsajad Abavisani, Kseniya Solovyeva, David Danks, Vince D. Calhoun and 1 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

– ReadersNo votes yet
1/20 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: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score

Towards Optimism-Pessimism Trade-off in Model-based Offline-to-Online Reinforcement Learning

Guochen Zhou, Yijun Yang, Qiqi Duan, Qing Su and 5 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 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: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
71%Highly rated
?Highly ratedVote to see the score

Low-Rank Adaptation for Critic Learning in Off-Policy Reinforcement Learning

Low-rank adaptation regularizes critic learning by constraining updates to low-dimensional subspaces via frozen base weights, reducing loss and improving off-policy RL performance.

Yuan Zhuang, Yuexin Bian, Sihong He, Jie Feng and 6 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 4/5
medium 3/10
strict 0/5