Good Papers

Showing papers from UC Irvine Show all papers

45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026U California, IrvineUC IrvineGraph generation

xVGAE: A Hierarchical Variational Graph Autoencoder for Exchangeable Graphs

Daniele Micheletti, Federica Zoe Ricci, Erik Sudderth

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
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026OralCarnegie MellonUC IrvineU California, IrvineCMU, Strategy Robot, Optimized MOptimization

Follow the Regularized Leader Does Not Converge in Constrained Optimization

Ioannis Anagnostides, Ioannis Panageas, Nikolas Patris, Tuomas Sandholm

Sydney Poster Session 3, Wed, Dec 9, 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 0/5
medium 0/10
strict 1/5
80%Must read
?Must readVote to see the score
NeurIPS 2026Singapore University of TechnoloNational Technical University ofAthena Research Center/ ArchimedUC IrvineLearning theory

Online Learning on Hidden-Convex Losses via Algorithmic Equivalence: Optimal Regret, Geometric Barrier, and Bandit Feedback

Online gradient descent achieves optimal O(sqrt(T)) regret for hidden-convex losses via sharper discrete equivalence, with a necessary Hessian compatibility condition and O(T^{3/4}) bandit regret.

Anas Barakat, Andreas Kontogiannis, Vasilis Pollatos, Ioannis Panageas and 1 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
12/20 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: 12 of 20 reviewers recommend it
lenient 1/5
medium 6/10
strict 5/5
71%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026PurdueUC IrvineGoogle ResearchOptimization

The Query Complexity of Local Search in Rounds on General Graphs

This paper bounds the query complexity of multi-round local search on general graphs, proving deterministic upper and randomized lower bounds that extend grid results to arbitrary connected graphs.

Simina Branzei, Ioannis Panageas, Dimitris Paparas

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
6/20 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: 6 of 20 reviewers recommend it
lenient 3/5
medium 2/10
strict 1/5