ICML Poster "convex optimization" Papers
14 papers found
A New Branch-and-Bound Pruning Framework for $\ell_0$-Regularized Problems
Guyard Theo, Cédric Herzet, Clément Elvira et al.
ICML 2024posterarXiv:2406.03504
A Universal Transfer Theorem for Convex Optimization Algorithms Using Inexact First-order Oracles
Phillip Kerger, Marco Molinaro, Hongyi Jiang et al.
ICML 2024posterarXiv:2406.00576
Convex and Bilevel Optimization for Neural-Symbolic Inference and Learning
Charles Dickens, Changyu Gao, Connor Pryor et al.
ICML 2024poster
Differentially Private Domain Adaptation with Theoretical Guarantees
Raef Bassily, Corinna Cortes, Anqi Mao et al.
ICML 2024poster
Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm
Batiste Le Bars, Aurélien Bellet, Marc Tommasi et al.
ICML 2024poster
Minimally Modifying a Markov Game to Achieve Any Nash Equilibrium and Value
Young Wu, Jeremy McMahan, Yiding Chen et al.
ICML 2024posterarXiv:2311.00582
MoMo: Momentum Models for Adaptive Learning Rates
Fabian Schaipp, Ruben Ohana, Michael Eickenberg et al.
ICML 2024posterarXiv:2305.07583
New Sample Complexity Bounds for Sample Average Approximation in Heavy-Tailed Stochastic Programming
Hongcheng Liu, Jindong Tong
ICML 2024poster
On the Last-Iterate Convergence of Shuffling Gradient Methods
Zijian Liu, Zhengyuan Zhou
ICML 2024poster
Performative Prediction with Bandit Feedback: Learning through Reparameterization
Yatong Chen, Wei Tang, Chien-Ju Ho et al.
ICML 2024poster
Projection-Free Variance Reduction Methods for Stochastic Constrained Multi-Level Compositional Optimization
Wei Jiang, Sifan Yang, Wenhao Yang et al.
ICML 2024posterarXiv:2406.03787
Quantum Algorithms and Lower Bounds for Finite-Sum Optimization
Yexin Zhang, Chenyi Zhang, Cong Fang et al.
ICML 2024poster
Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features
Aleksandr Beznosikov, David Dobre, Gauthier Gidel
ICML 2024poster
Stability and Generalization for Stochastic Recursive Momentum-based Algorithms for (Strongly-)Convex One to $K$-Level Stochastic Optimizations
Xiaokang Pan, Xingyu Li, Jin Liu et al.
ICML 2024posterarXiv:2407.05286