Good Papers

Showing Optimization Show all papers

45%Niche pick
?Niche pickVote to see the score

Generalization Analysis of Biased Stochastic Gradient Methods for Minimax Problems

Shuang Zeng, Yunwen Lei, Yiming Ying

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
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026SpotlightPekingOptimization

How Can SignSGD Outperform SGD? A Functional Scaling Law Perspective

Zilin Wang, Binghui Li, Jia-Nan Wang, Lean Wang and 2 more

Sydney Poster Session 5, Thu, Dec 10, 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

Diversity Maximization: Algorithms for Distant $k$-Subsets

Shayan C Jahan, Hamed Abdi, Ali Ahmadi, Javier Marinković and 1 more

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

Stable Max Coverage Under a Cardinality Constraint

Themistoklis Haris, Fabian Spaeh, Nithin Varma, Yuichi Yoshida

Sydney Poster Session 3, Wed, Dec 9, 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

Scalable Supervised Optimal Transport of Gaussian Mixture Models

Damin Kühn, Michael T Schaub

Sydney Poster Session 5, Thu, Dec 10, 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

Reliable Clustering and Quantization via Distortion-Constrained Optimal Transport

Tianhao Wu, Wei Zhang, Haoran Pang, Haoran YANG and 1 more

Sydney Poster Session 5, Thu, Dec 10, 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

Neural Dual Bounds: Valid-by-Construction JGLP Warm-Starts for MAP and Constrained MAP

Akshay Vyas, Shivvrat Arya, Brij Malhotra, Tahrima Rahman and 2 more

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · 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

Optimizing Analytic Constants via AI-Guided Lean Proof Refinement

Rahul Saha, Alan Li, Anton Xue, Adam Klivans and 3 more

Sydney Poster Session 3, Wed, Dec 9, 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
NeurIPS 2026PekingOptimization

Query Lower Bounds for Approximating the Top Eigenvector of Asymmetric Matrices

Kun Chen, Zhihua Zhang

Sydney Poster Session 5, Thu, Dec 10, 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

GEAR: A GPU-Accelerated Global Solver for Nonlinear Programs via Linear Bound Propagation

Duo Zhou, Hesun Chen, Xiangru Zhong, Grani A. Hanasusanto and 1 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · 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

Differentiable Range-Partition Entropy for Entropy-Sensitive Geometric Algorithms

Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · 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

Fully First-Order Algorithms for Online Non-Convex Bilevel Optimization

Tingkai Jia, Ting Wang, Cheng Chen

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.

45%Niche pick
?Niche pickVote to see the score

Algorithms for Linear Equations with Min and Max Operators Under (Absolutely) Halting Condition

Krishnendu Chatterjee, Ruichen Luo, Raimundo Saona, Jakub Svoboda

Sydney Poster Session 5, Thu, Dec 10, 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

Efficient Algorithms for Distributed Saddle Problems

Ruichen Luo, Anton Rodomanov, Sebastian Stich

Sydney Poster Session 6, Thu, Dec 10, 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
45%Niche pick
?Niche pickVote to see the score

Gibbs Gradient Descent: A Langevin Approach to Optimization

Oussama Zekri, Anna Korba, Nicolas Boulle

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · 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

On Generalization in Bilevel Optimization with Overparameterized Models

Fares El Khoury, Edouard Pauwels, Samuel Vaiter, Michael Arbel

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
45%Niche pick
?Niche pickVote to see the score

Gradient Descent on Two ReLU Neurons: Global Landscape and Bifurcation Dynamics

Binghua Li, Mengzhe Li, Denny Wu, Tianhao Wang

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · 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.

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

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
45%Niche pick
?Niche pickVote to see the score

$\gamma$-weakly $\theta$-up-concavity: A Unified Framework for Non-Convex Optimization Beyond DR-Submodular and OSS Functions

Mohammad Pedramfar, Vaneet Aggarwal

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

Dual-Space Preconditioning for Variational Inequalities and Root-Finding Problems

Jan Quan, Konstantinos Oikonomidis, Alexander Bodard, Panagiotis Patrinos

Sydney Poster Session 4, Wed, Dec 9, 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
45%Niche pick
?Niche pickVote to see the score

Orthogonal Updates for the Win: Towards Accelerated Adaptive Minimax Optimization

Zhiwei Zhai, Xinyu Wang, Wenjing Yan, Lei Ding and 1 more

Sydney Poster Session 4, Wed, Dec 9, 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
45%Niche pick
?Niche pickVote to see the score

Curvature-Dependent Lower Bounds for Riemannian Online Convex Optimization

Hibiki Fukushima, Shinji Ito

Sydney Poster Session 4, Wed, Dec 9, 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
45%Niche pick
?Niche pickVote to see the score

On the Necessity of Guidance Decay: From Three-Phase Analysis in Gaussian Mixture Models to Dynamic Optimization

Yiyu Qiu, Ruofeng Yang, Tong Yu, Shuai Li

Sydney Poster Session 4, Wed, Dec 9, 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
45%Niche pick
?Niche pickVote to see the score

Pairwise AUC Optimization Needs Corrective Power: A Unified View

Jia Chen, Zhiyong Yang, Shilong Bao, Qianqian Xu and 1 more

Sydney Poster Session 4, Wed, Dec 9, 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
45%Niche pick
?Niche pickVote to see the score

Finite-Resolution Decision Sufficiency for Linear Optimization

Mohammed Jamal, Soufiane Fafe

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · 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

Sharper Regret Bounds for Shampoo

Dahngoon Kim, Min-hwan Oh

Sydney Poster Session 6, Thu, Dec 10, 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
45%Niche pick
?Niche pickVote to see the score

A Unified Semismooth Newton Approach to Multitask and Multivariate Square-Root Lasso Problems

Dongwon Kim, Taehyoung Kim, Sungdong Lee, Joong-Ho (Johann) Won

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.

45%Niche pick
?Niche pickVote to see the score

Certifiably Optimal Robust Angular Synchronization

Daniel Barath, Keisuke Tateno, Marc Pollefeys, Federico Tombari

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · 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

Hessian-Dependent Sample Complexity in Zeroth-Order Stochastic Optimization: Suboptimality of Convex-Support Sampling and Optimal Sample Complexity

Mengtian Hong, Jason Lee, Qian Yu

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 0/5
medium 0/10
strict 1/5
45%Niche pick
?Niche pickVote to see the score

ZO-F2: Low-variance Fisher preconditioner via bilinear estimation for zeroth-order optimization

Hiroshi Sawada, Yuya Hikima, Kenta Niwa

Sydney Poster Session 6, Thu, Dec 10, 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
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026YandexOptimization

Understanding Gradient Orthogonalization for Deep Learning via Non-Euclidean Trust-Region Optimization

Dmitry Kovalev

Sydney Poster Session 3, Wed, Dec 9, 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

A Frank-Wolfe Approach to Goldstein Stationarity

Swati Padmanabhan, Zitao Song, Zhe Zhang

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

Homological Barriers to Stable Local Nash Dynamics in Quadratic Zero-Sum Games

Ashkan Soleymani, Gabriele Farina, Patrick Jaillet, Georgios Piliouras

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

Runtime Analysis of Cartesian Genetic Programming on MAX: A Proven Exponential Speedup

Duc-Cuong Dang, Roman Kalkreuth, Andre Opris

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 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
45%Niche pick
?Niche pickVote to see the score

High-probability Convergence of Gradient Methods under Markovian Stochasticity

Polina Podzorova, Nikolay Spitsyn, Savelii Chezhegov, Aleksandr Beznosikov

Sydney Poster Session 6, Thu, Dec 10, 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
57%Worth a look
?Worth a lookVote to see the score

Sparse Updates Generalize Better Than Optimization: Stability Analysis for Randomized Subspace Descent

Yifei Liang, Yan Sun, Yifei Cheng, Haobo Fu and 2 more

Sydney Poster Session 1, Tue, Dec 8, 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.

45%Niche pick
?Niche pickVote to see the score

Concise Reasoning Through the Lens of Lagrangian Optimization

Chengqian Gao, Haonan Li, Taylor Killian, Jianshu She and 5 more

Sydney Poster Session 5, Thu, Dec 10, 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

Continuous p-adic Optimization

Julian Salazar, Dimitri Kanevsky, Matt Harvey, Pascal Getreuer and 1 more

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.

45%Niche pick
?Niche pickVote to see the score

Riemannian Lyapunov Framework: Optimization as Closed-Loop Control on Riemannian Manifolds

Yixuan Wang, Omkar Sudhir Patil, Warren Dixon

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

Median-of-Means under Structured Heavy-Tailed Noise: High-Probability Bounds for Clipped Stochastic Optimization

Ahmed El Bajdali, Ohad Shamir, Samuel Horváth, Eduard Gorbunov

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.

45%Niche pick
?Niche pickVote to see the score

On Computing Diverse Solutions in the Earth Movers Distance

Aritra Banik, Mayank Goswami, Abhishek Sahu

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · 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

Accelerated last-iterate convergence of Extragradient via power-law stepsizes

Yue Wu, Weiqiang Zheng, Yang Cai, Haipeng Luo

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · 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

Dynamic Optimistic Constrained OCO with Memory via Delay Equivalence

Mohammed ABDULLAH, George Iosifidis, Salah Eddine ELAYOUBI, Tijani Chahed

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · 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

Your Hypergradient is Skewed: Antithetic Neumann Estimation for Bilevel Optimization

Jason Bohne, Pawel Polak, Gary Kazantsev, David Rosenberg

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · 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

A Cross-Interaction Neural Architecture for Submodular Functions

SOUTRIK SARANGI, Aditya Singh, Vansh Maheshwari, Abir De

Sydney Poster Session 3, Wed, Dec 9, 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.

45%Niche pick
?Niche pickVote to see the score

Neural Optimal Transport in Hilbert Spaces

Jae-Hwan Choi, Jiwoo Yoon, Dohyun Kwon, Jaewoong Choi

Sydney Poster Session 6, Thu, Dec 10, 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
57%Worth a look
?Worth a lookVote to see the score

Submodular Clustering beyond $1-1/e$

Kiarash Banihashem, Mohammadhossein Bateni, Hossein Esfandiari, Samira Goudarzi and 1 more

Atlanta Poster Session 6, Fri, Dec 11, 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 0/5
medium 0/10
strict 1/5
45%Niche pick
?Niche pickVote to see the score

Matrix Recovery Via Symmetric Rank-one Measurements With Random Unit-modulus Vectors

Tongyu Zhou, Wei Zhang

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.

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

Do We Need Asynchronous SGD? On the Near-Optimality of Synchronous Methods

Grigory Begunov, Alexander Tyurin

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 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
45%Niche pick
?Niche pickVote to see the score

Robust Noisy Inductive Matrix Completion with Local Linear Convergence

Xingcai Zhou, Xin Dong, Linglong Kong

Sydney Poster Session 5, Thu, Dec 10, 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

A Stability Analysis of AdamW: Unstable Equilibria and Non-Convergence

Baiyu Su, Lizhang Chen, Qiang Liu

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 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
76%Highly rated
?Highly ratedVote to see the score

Sharp Capacity Scaling of Spectral Optimizers in Learning Associative Memory

Spectral optimizer Muon exceeds SGD associative memory capacity, matching Newton's method with first-order updates and larger critical batch sizes.

Juno Kim, Eshaan Nichani, Denny Wu, Alberto Bietti and 1 more

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

– ReadersNo votes yet
10/20 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: 10 of 20 reviewers recommend it
lenient 3/5
medium 4/10
strict 3/5
74%Highly rated
?Highly ratedVote to see the score

Post-ADC Inference: Valid Inference After Active Data Collection

Post-ADC inference provides valid p-values and confidence intervals for data-dependent targets after active data collection by correcting adaptive sampling and selection biases without assuming the black-box function form.

Shuichi Nishino, Tomohiro Shiraishi, Teruyuki Katsuoka, Ichiro Takeuchi

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

– ReadersNo votes yet
9/20 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.

76%Highly rated
?Highly ratedVote to see the score

Time-Sensitive Anytime-Valid Testing

A time-sensitive testing-by-betting framework favors early rejection via time-weighted rewards, yielding Bellman-optimal e-processes and an exponential-decay-optimal criterion recovering classical growth-rate optimality at large scales.

Eugenio Clerico, Tobias Wegel, Iskander Azangulov, Patrick Rebeschini

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
10/20 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.

80%Must read
?Must readVote to see the score

Optimal Hidden-Target Learning for Online Inventory Optimization on General Convex Sets

Hidden-target projection minimizes regret for online inventory optimization on general convex sets, improving dependence on common-demand probability to inverse square root with matching lower bound, plus polylogarithmic and adaptive dynamic guarantees.

Anthony Pineci, Yunzong Xu

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

70%Highly rated
?Highly ratedVote to see the score

Information-Geometric Forward Policy Training in GFlowNets

GFlowNet forward-policy training uses trajectory Fisher-Rao geometry to derive structure-aware natural gradients and decomposable second-moment updates.

Yordan Raykov, Rodrigo Veiga

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · 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.

71%Highly rated
?Highly ratedVote to see the score

Sinkhorn Based Associative Memory Retrieval Using Spherical Hellinger Kantorovich Dynamics

A Sinkhorn-based dense associative memory for point-cloud measures uses spherical Hellinger-Kantorovich dynamics to retrieve patterns with exponential capacity and robust convergence.

Aratrika Mustafi, Soumya Mukherjee

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 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 2/5
medium 3/10
strict 1/5
72%Highly rated
?Highly ratedVote to see the score

Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer

Regularized Muon induces a Hamiltonian probability gradient flow with mirror-descent structure, yielding exponential convergence under gradient dominance and mean-field propagation of chaos.

Aratrika Mustafi, Soumya Mukherjee, Bharath Sriperumbudur

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

– ReadersNo votes yet
8/20 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: 8 of 20 reviewers recommend it
lenient 3/5
medium 3/10
strict 2/5
86%Must read
?Must readVote to see the score

Curvature Beyond Positivity: Greedy Guarantees for Arbitrary Submodular Functions

Curvature is extended to arbitrary submodular functions, yielding greedy multiplicative approximation guarantees that apply even to negative-valued objectives.

Yixin Chen, Alan Kuhnle

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

– ReadersNo votes yet
14/20 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: 14 of 20 reviewers recommend it
lenient 2/5
medium 8/10
strict 4/5
72%Highly rated
?Highly ratedVote to see the score

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization

A dual-anchor mechanism accelerates stochastic root-finding to O(ε⁻³) without variance reduction or regularization, reaching near-optimal O(ε⁻²) for strongly monotone cases.

TaeHo Yoon, Nicolas Loizou

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

– ReadersNo votes yet
8/20 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: 8 of 20 reviewers recommend it
lenient 2/5
medium 4/10
strict 2/5
74%Highly rated
?Highly ratedVote to see the score

Sobolev Regularized MMD Gradient Flow

Sobolev-regularized MMD gradient flow penalizes witness function gradients to ensure global convergence without isoperimetric assumptions, applying to both sampling and generative modeling.

Chenyang Tian, Bharath Sriperumbudur, Arthur Gretton, Zonghao Chen

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

– ReadersNo votes yet
9/20 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.

72%Highly rated
?Highly ratedVote to see the score

Multi-Variable Conformal Prediction: Optimizing Prediction Sets without Data Splitting

Multi-variable conformal prediction extends calibration to vector-valued scores with multiple variables, removing data splitting while preserving coverage and yielding smaller, more stable prediction sets.

Laura Lützow, Simone Garatti, Marco Campi, Lars Lindemann and 1 more

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

– ReadersNo votes yet
8/20 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.

72%Highly rated
?Highly ratedVote to see the score

Difference of Convex Programming in the Wasserstein Space with Applications to MMD Optimization

A difference-of-convex convex-concave procedure is lifted to Wasserstein space for non-convex measure optimization, yielding almost-stationary iterates and explicit decompositions for MMD and energy distance with faster convergence.

Clément Bonet, Pierre-Cyril Aubin-Frankowski, Youssef Mroueh

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
8/20 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.

70%Highly rated
?Highly ratedVote to see the score

From Cursed to Competitive: Closing the ZO–FO Gap via Input-to-State Stability

Using input-to-state stability, zeroth-order optimization achieves first-order convergence rates without extra dimension dependence when perturbations are small.

Amir Ali Farzin, Philipp Braun, Iman Shames

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 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 1/5
medium 3/10
strict 1/5
70%Highly rated
?Highly ratedVote to see the score

Achieving Better Local Regret Bound for Online Non-Convex Bilevel Optimization

An adaptive algorithm achieves optimal O(1+V_T) local regret for online non-convex bilevel optimization with O(T log T) gradient evaluations, and a window-based method attains optimal Ω(T/W²) window-averaged regret via single-loop updates.

Tingkai Jia, Haiguang Wang, Ting Wang, Cheng Chen

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 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.

71%Highly rated
?Highly ratedVote to see the score

Learning-Augmented Approximation for Unrelated-Machines Makespan Scheduling

A learning-augmented algorithm for unrelated-machine makespan scheduling uses heavy-job predictions to achieve (1+ε)-approximation that smoothly degrades to 2-approximation as error grows.

Kaito Baba, Evripidis Bampis, Georgios Mitropoulos

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · 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 1/10
strict 2/5
74%Highly rated
?Highly ratedVote to see the score

Understanding the Curse of Unrolling

Non-asymptotic analysis explains the curse of unrolling, early derivative divergence when differentiating through iterative algorithms, and shows that truncating early iterations mitigates it while reducing memory, with warm-starting providing implicit truncation in bilevel optimization.

Sheheryar Mehmood, Florian Knoll, Peter Ochs

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
9/20 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.

70%Highly rated
?Highly ratedVote to see the score

Factor Augmented High-Dimensional SGD

FSGD is a streaming SGD method that uses latent factor representations for high-dimensional tasks, achieving scalable optimization with theoretical convergence guarantees including factor estimation error.

Shubo Li, Yuefeng Han, Xiufan Yu

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

– ReadersNo votes yet
4/20 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.

71%Highly rated
?Highly ratedVote to see the score

Compute Efficiency and Serial Runtime Tradeoffs for Stochastic Momentum Methods

Heavy ball and ASGD face compute-efficiency versus serial-runtime tradeoffs in linear regression, with heavy ball extending SGD's efficient batch window by up to √κ and ASGD trading small-batch efficiency for runtime on fast-decaying spectra.

Depen Morwani, Alexandru Meterez, Pranav Nair, Sham Kakade

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 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.

70%Highly rated
?Highly ratedVote to see the score

Constrained Stochastic Spectral Preconditioning Converges for Nonconvex Objectives

Proximal preconditioned stochastic gradient methods extend Muon/Scion to nonconvex constrained optimization with heavy-tailed noise convergence and faster variance-reduced variants.

Konstantinos Oikonomidis, Jan Quan, Kimon Antonakopoulos, Antonio Silveti-Falls and 2 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 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.

72%Highly rated
?Highly ratedVote to see the score

Mirror Descent-Ascent for mean-field min-max problems

Mirror descent-ascent achieves O(N^{-1/2}) and O(N^{-2/3}) convergence to mean-field Nash equilibria via infinite-dimensional dual Bregman analysis.

Razvan-Andrei Lascu, Mateusz Majka, Lukasz Szpruch

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

– ReadersNo votes yet
8/20 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.

Show 20 more papers