Please join us in gather.town (see link above). To see the abstracts of the posters presented in this session, please see below the schedule.

Authors/papers presenting posters in gather.town for this session:

• Optimum-statistical Collaboration Towards Efficient Black-box Optimization, Wenjie Li

• Integer Programming Approaches To Subspace Clustering With Missing Data, Akhilesh Soni

• Stochastic Learning Equation using Monotone Increasing Resolution of Quantization, Jinwuk Seok

• Sign-RIP: A Robust Restricted Isometry Property for Low-rank Matrix Recovery, Jianhao Ma

• Farkas' Theorem of the Alternative for Prior Knowledge in Deep Networks, Jeffery Kline

• Towards Robust and Automatic Hyper-Parameter Tunning, Mahdi S. Hosseini

• High Probability Step Size Lower Bound for Adaptive Stochastic Optimization, Miaolan Xie

• Stochastic Polyak Stepsize with a Moving Target, Robert M Gower

• A Stochastic Momentum Method for Min-max Bilevel Optimization, Quanqi Hu

• Deep Neural Networks pruning via the Structured Perspective Regularization, Matteo Cacciola

• Efficient Calibration of Multi-Agent Market Simulators from Time Series with Bayesian Optimization, Yuanlu Bai

• DESTRESS: Computation-Optimal and Communication-Efficient Decentralized Nonconvex Finite-Sum Optimization, Boyue Li

• Policy Mirror Descent for Regularized RL: A Generalized Framework with Linear Convergence, Shicong Cen

• Simulated Annealing for Neural Architecture Search, Shentong Mo

• Acceleration and Stability of Stochastic Proximal Point Algorithm, Junhyung Lyle Kim

• Barzilai and Borwein conjugate gradient method equipped with a non-monotone line search technique, Sajad Fathi Hafshejani

• On the convergence of stochastic extragradient for bilinear games using restarted iteration averaging, Chris Junchi Li

• Practice-Consistent Analysis of Adam-Style Methods, Zhishuai Guo

• Escaping Local Minima With Stochastic Noise, Harsh Vardhan

• Optimization with Adaptive Step Size Selection from a Dynamical Systems Perspective, Neha S Wadia

• Last-Iterate Convergence of Saddle Point Optimizers via High-Resolution Differential Equations, Tatjana Chavdarova

• Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization, Difan Zou

• Faster Perturbed Stochastic Gradient Methods for Finding Local Minima, Zixiang Chen

• Adam vs. SGD: Closing the generalization gap on image classification, Aman Gupta

• Heavy-tailed noise does not explain the gap between SGD and Adam on Transformers, Frederik Kunstner

• Faster Quasi-Newton Methods for Linear Composition Problems, Betty Shea

• The Geometric Occam Razor Implicit in Deep Learning, Benoit Dherin

• Random-reshuffled SARAH does not need a full gradient computations, Aleksandr Beznosikov