Posters in this session

• Towards a Better Theoretical Understanding of Independent Subnetwork Training

• Revisiting Random Weight Perturbation for Efficiently Improving Generalization

• Det-CGD: Compressed Gradient Descent with Matrix Stepsizes for Non-Convex Optimization

• Non-Uniform Sampling and Adaptive Optimizers in Deep Learning

• Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation

• Adaptive Quasi-Newton and Anderson Acceleration Framework with Explicit Global (Accelerated) Convergence Rates

• On Optimization Formulations of Finite Horizon MDPs

• Dueling Optimization with a Monotone Adversary

• A Predicting Clipping Asynchronous Stochastic Gradient Descent Method in Distributed Learning

• Average-Constrained Policy Optimization

• Model-Free, Regret-Optimal Best Policy Identification in Online CMDPs

• Vanilla Thompson Sampling Revisited

• Stochastic Optimization under Hidden Convexity

• Why Adam Outperforms Gradient Descent on Language Models: A Heavy-Tailed Class Imbalance Problem

• DynaLay: An Introspective Approach to Dynamic Layer Selection for Deep Networks

• How to Guess a Gradient

• Escaping mediocrity: how two-layer networks learn hard generalized linear models

• Safe Posterior Sampling for Constrained MDPs with Bounded Constraint Violation

• Nesterov Meets Robust Multitask Learning Twice

• Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches

• Adam through a Second-Order Lens

• On the convergence of warped proximal iterations for solving nonmonotone inclusions and applications

• Multi-head CLIP: Improving CLIP with Diverse Representations and Flat Minima

• New Horizons in Parameter Regularization: A Constraint Approach

• Riemannian Optimization for Euclidean Distance Geometry

• Efficient Learning in Polyhedral Games via Best Response Oracles

• Stochastic FISTA Step Search Algorithm for Convex Optimization

• Almost multisecant BFGS quasi-Newton method

• Statistical Inference of Adaptive Inexact Stochastic Newton Method

• On the Interplay Between Stepsize Tuning and Progressive Sharpening

• Decentralized Learning Dynamics in the Gossip Model

• Pruning Neural Networks with Velocity-Constrained Optimization

• Risk Bounds of Accelerated SGD for Overparameterized Linear Regression

• DIRECT Optimisation with Bayesian Insights: Assessing Reliability Under Fixed Computational Budgets

• Parameter-Agnostic Optimization under Relaxed Smoothness

• Optimization dependent generalization bound for ReLU networks based on sensitivity in the tangent bundle

• Understanding the Role of Optimization in Double Descent

• Level Set Teleportation: the Good, the Bad, and the Ugly

• Cup Curriculum: Curriculum Learning on Model Capacity

• Variance Reduced Model Based Methods: New rates and adaptive step sizes

• SGD batch saturation for training wide neural networks

• Follow the flow: Proximal flow inspired multi-step methods

• The Sharp Power Law of Local Search on Expanders