Break
in
Workshop: OPT 2023: Optimization for Machine Learning

Poster Session 2

Xiao-Yang Liu ⋅ Guy Kornowski ⋅ Philipp Dahlinger ⋅ Abbas Ehsanfar ⋅ Binyamin Perets ⋅ David Martinez-Rubio ⋅ Sudeep Raja Putta ⋅ Runlong Zhou ⋅ Connor Lawless ⋅ Julian J Stier ⋅ Chen Fan ⋅ Michal Šustr ⋅ James Spann ⋅ Jung Hun Oh ⋅ Yao Xie ⋅ Qi Zhang ⋅ Krishna Acharya ⋅ Sourabh Medapati ⋅ Sharan Vaswani ⋅ Sruthi Gorantla ⋅ Mohamed Elsayed ⋅ Hongyang Zhang ⋅ Reza Asad ⋅ Viktor Pavlovic ⋅ Betty Shea ⋅ Georgy Noarov ⋅ Chuan He ⋅ Daniil Vankov ⋅ Taoan Huang ⋅ Michael Lu ⋅ Anant Mathur ⋅ Konstantin Mishchenko ⋅ Stanley Wei ⋅ Francesco Faccio ⋅ Yuchen Zeng ⋅ Tianyue Zhang ⋅ Chris Junchi Li ⋅ Aaron Mishkin ⋅ Sina Baharlouei ⋅ Chen Xu ⋅ Sasha Abramowitz ⋅ Sebastian Stich ⋅ Felix Dangel

2023 Break
in
Workshop: OPT 2023: Optimization for Machine Learning

Abstract

Posters in this session:

K-Spin Ising Model for Combinatorial Optimizations over Graphs: An Reinforcement Learning Approach
An Algorithm with Optimal Dimension-Dependence for Zero-Order Nonsmooth Nonconvex Stochastic Optimization
Information-Theoretic Trust Regions for Stochastic Gradient-Based Optimization
Exploring Modern Evolution Strategies in Portfolio Optimization
Unnormalized Density Estimation with Root Sobolev Norm Regularization
Accelerated Methods for Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties
Regret Bounds for Optimistic Follow The Leader: Applications in Portfolio Selection and Linear Regression
How Over-Parameterization Slows Down Gradient Descent in Matrix Sensing: The Curses of Symmetry and Initialization
Fair Minimum Representation Clustering
FaDE: Fast DARTS Estimator on Hierarchical NAS Spaces
MSL: An Adaptive Momentem-based Stochastic Line-search Framework
Global CFR: Meta-Learning in Self-Play Regret Minimization
(Un)certainty selection methods for Active Learning on Label Distributions
Optimal Transport for Kernel Gaussian Mixture Models
Large-scale Non-convex Stochastic Constrained Distributionally Robust Optimization
Oracle Efficient Algorithms for Groupwise Regret
Adaptive Gradient Methods at the Edge of Stability
Reducing Predict and Optimize to Convex Feasibility
Optimizing Group-Fair Plackett-Luce Ranking Models for Relevance and Ex-Post Fairness
Utility-based Perturbed Gradient Descent: An Optimizer for Continual Learning
Noise Stability Optimization for Flat Minima with Tight Rates
Surrogate Minimization: An Optimization Algorithm for Training Large Neural Networks with Model Parallelism
Accelerated gradient descent: A guaranteed bound for a heuristic restart strategy
Noise-adaptive (Accelerated) Stochastic Heavy-Ball Momentum
Greedy Newton
High-Dimensional Prediction for Sequential Decision Making
A proximal augmented Lagrangian based algorithm for federated learning with constraints
Last Iterate Convergence of Popov Method for Non-monotone Stochastic Variational Inequalities
Contrastive Predict-and-Search for Mixed Integer Linear Programs
Practical Principled Policy Optimization for Finite MDPs
Feature Selection in Generalized Linear models via the Lasso: To Scale or Not to Scale?
Noise Injection Irons Out Local Minima and Saddle Points
On the Synergy Between Label Noise and Learning Rate Annealing in Neural Network Training
Continually Adapting Optimizers Improve Meta-Generalization
The Expressive Power of Low-Rank Adaptation
From 6235149080811616882909238708 to 29: Vanilla Thompson Sampling Revisited
Accelerating Inexact HyperGradient Descent for Bilevel Optimization
A novel analysis of gradient descent under directional smoothness
f-FERM: A Scalable Framework for Robust Fair Empirical Risk
An alternative approach to train neural networks using monotone variational inequality
Generalisable Agents for Neural Network Optimisation
On the Convergence of Local SGD Under Third-Order Smoothness and Hessian Similarity
Diversity-adjusted adaptive step size
Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC for Large Neural Nets

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