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