Канал: RPI Seminars MIDO

Manifold learning for point-cloud data with applications in biology

Manifold learning for point-cloud data with applications in biology

New Federated Algorithms for Deep Learning with Unbounded Smooth Landscape

New Federated Algorithms for Deep Learning with Unbounded Smooth Landscape

Bilevel Optimization: Stochastic Algorithms and Applications in Inverse Reinforcement Learning

Bilevel Optimization: Stochastic Algorithms and Applications in Inverse Reinforcement Learning

Sparse coding via a Delaunay triangulation. Abiy Tasissa@Tufts Univ.

Sparse coding via a Delaunay triangulation. Abiy Tasissa@Tufts Univ.

A proximal algorithm for sampling, Yongxin Chen@Gtech

A proximal algorithm for sampling, Yongxin Chen@Gtech

Scalable, Projection-Free Optimization Methods

Scalable, Projection-Free Optimization Methods

Better Data Ordering for Stochastic Gradient Descent

Better Data Ordering for Stochastic Gradient Descent

Continuous-in-time Limit for Bayesian Bandits, YuhuaZhu@UCSD

Continuous-in-time Limit for Bayesian Bandits, YuhuaZhu@UCSD

Multiscale inverse problem: Interaction between multiple scales in physical system reconstruction

Multiscale inverse problem: Interaction between multiple scales in physical system reconstruction

Offline and Online Learning and Decision-Making in the Predict-then-Optimize Setting

Offline and Online Learning and Decision-Making in the Predict-then-Optimize Setting

Machine learning meets dynamics & variational Stiefel optimization, Molei Tao@Gatech

Machine learning meets dynamics & variational Stiefel optimization, Molei Tao@Gatech

Implicit biases of optimization algs for NNs: static & dynamic perspectives, Chao Ma@Stanford

Implicit biases of optimization algs for NNs: static & dynamic perspectives, Chao Ma@Stanford

Graph Clustering Dynamics: From Spectral to Mean Shift, Katy Craig@UCSB

Graph Clustering Dynamics: From Spectral to Mean Shift, Katy Craig@UCSB

Shortest Paths in Graphs of Convex Sets, with Applications to Control and Motion Planning

Shortest Paths in Graphs of Convex Sets, with Applications to Control and Motion Planning

Deep Image Prior (and Its Cousin) for Inverse Problems: the Untold Stories

Deep Image Prior (and Its Cousin) for Inverse Problems: the Untold Stories

Inference, Computation, and Games

Inference, Computation, and Games

Learning Nonlocal Operators for Heterogeneous Material Modeling, Yue Yu@Lehigh

Learning Nonlocal Operators for Heterogeneous Material Modeling, Yue Yu@Lehigh

Global Loss Landscape of Neural Networks: Knowns and Unknowns

Global Loss Landscape of Neural Networks: Knowns and Unknowns

Universal Conditional Gradient Sliding for Convex Optimization

Universal Conditional Gradient Sliding for Convex Optimization

Units-equivariant machine learning, Soledad Villar@JHU

Units-equivariant machine learning, Soledad Villar@JHU

Symmetry-preserving machine learning, Wei Zhu@UMASS

Symmetry-preserving machine learning, Wei Zhu@UMASS

How Differential Equations Insight Benefit Deep Learning, Bao Wang@Univ. of Utah

How Differential Equations Insight Benefit Deep Learning, Bao Wang@Univ. of Utah

Algorithms for Deterministically Constrained Stochastic Optimization

Algorithms for Deterministically Constrained Stochastic Optimization

Active strict saddles in nonsmooth optimization

Active strict saddles in nonsmooth optimization

On the Robustness of Deep Neural Networks

On the Robustness of Deep Neural Networks

A Stochastic variance-reduced Primal-dual Method for Convex-concave Saddle point Problems

A Stochastic variance-reduced Primal-dual Method for Convex-concave Saddle point Problems

Equivariant Neural Networks for Learning Spatiotemporal Dynamics, R.Walters, Northeastern University

Equivariant Neural Networks for Learning Spatiotemporal Dynamics, R.Walters, Northeastern University

Inexact Proximal Gradient Method with Subspace Acceleration

Inexact Proximal Gradient Method with Subspace Acceleration

Distributed stochastic non-convex optimization: Optimal regimes and tradeoffs

Distributed stochastic non-convex optimization: Optimal regimes and tradeoffs

Data-driven discovery of interacting particle system using Gaussian processes, S. Tang@UCSB

Data-driven discovery of interacting particle system using Gaussian processes, S. Tang@UCSB

Geometric and Statistical Approaches to Shallow and Deep Clustering, J. Murphy@Tufts University

Geometric and Statistical Approaches to Shallow and Deep Clustering, J. Murphy@Tufts University

Decentralized Optimization for Statistical Learning over Networks

Decentralized Optimization for Statistical Learning over Networks

Optimal Learning for Structured Bandits

Optimal Learning for Structured Bandits

Data Compression in Distributed Learning

Data Compression in Distributed Learning

The Approximation Theory of Shallow Neural Networks, J Seigel@PSU

The Approximation Theory of Shallow Neural Networks, J Seigel@PSU

Model reduction for slow-fast stochastic systems near unknown invariant manifolds, F. Ye@UAlbany

Model reduction for slow-fast stochastic systems near unknown invariant manifolds, F. Ye@UAlbany

Two-Layer Neural Networks for PDEs: Optimization and Generalization Theory, HaizhaoYang@Purdue

Two-Layer Neural Networks for PDEs: Optimization and Generalization Theory, HaizhaoYang@Purdue

Numerical wave propagation aided by deep learning, R. Tsai@UT Austin

Numerical wave propagation aided by deep learning, R. Tsai@UT Austin

Riemannian Optimization for Projection Robust Optimal Transport

Riemannian Optimization for Projection Robust Optimal Transport

Overcoming the curse of dimensionality for some HJ equations via neural network, Jerome Darbon@Brown

Overcoming the curse of dimensionality for some HJ equations via neural network, Jerome Darbon@Brown

Consistency of Archetypal Analysis, Braxton Osting@UU

Consistency of Archetypal Analysis, Braxton Osting@UU

Graph Regularized Models for Blind Hyperspectral Unmixing, Yifei Lou@UT Dallas

Graph Regularized Models for Blind Hyperspectral Unmixing, Yifei Lou@UT Dallas

On the Curse of Memory in Recurrent Neural Networks. Jiequn Han@Princeton

On the Curse of Memory in Recurrent Neural Networks. Jiequn Han@Princeton

Optimal Transport for Inverse Problems and the Implicit Regularization, Yunan Yang@NYU

Optimal Transport for Inverse Problems and the Implicit Regularization, Yunan Yang@NYU

Asymptotic Network Independence in Distributed Convex Optimization

Asymptotic Network Independence in Distributed Convex Optimization

Understanding Geometric Deep Learning via Signal Processing on Graphs and Manifolds, M. Hirn@MSU

Understanding Geometric Deep Learning via Signal Processing on Graphs and Manifolds, M. Hirn@MSU

Lower complexity bounds of first-order methods for min-max problems

Lower complexity bounds of first-order methods for min-max problems

Incorporating Invariance to Reduce the Complexity of Parametric Models, Alex Cloninger@UCSD

Incorporating Invariance to Reduce the Complexity of Parametric Models, Alex Cloninger@UCSD

First-Order Methods for Convex Constrained Optimization under Error Bound Conditions

First-Order Methods for Convex Constrained Optimization under Error Bound Conditions

A Primal-Dual Algorithm with Line Search for Convex Concave Saddle Point Problems

A Primal-Dual Algorithm with Line Search for Convex Concave Saddle Point Problems

Transport information flows for Bayesian sampling problems, Wuchen Li@Univ. South Carolina

Transport information flows for Bayesian sampling problems, Wuchen Li@Univ. South Carolina

Inverse Optimal Transport: Learning Cost Functions for Optimal Transport, Xiaojing Ye@Georgia SU

Inverse Optimal Transport: Learning Cost Functions for Optimal Transport, Xiaojing Ye@Georgia SU

Deep AUC Maximization and Applications in Medical Image Classification

Deep AUC Maximization and Applications in Medical Image Classification

Comparing Manifold Data Distributions by Diffusion Kernels and Neural Networks, Xiuyuan Cheng@Duke

Comparing Manifold Data Distributions by Diffusion Kernels and Neural Networks, Xiuyuan Cheng@Duke

Some problems in point-set registration, Prof. Yuehaw Khoo@U Chicago

Some problems in point-set registration, Prof. Yuehaw Khoo@U Chicago

Sampled Quasi-Newton Methods and a Symmetric Blockwise Truncated Algorithm for Machine Learning

Sampled Quasi-Newton Methods and a Symmetric Blockwise Truncated Algorithm for Machine Learning

Robust and Flexible Distributed Optimization Algorithms for Networked Systems

Robust and Flexible Distributed Optimization Algorithms for Networked Systems

Poisson learning: Graph-based semi-supervised learning at very low label rates, Jeff Calder@UofM

Poisson learning: Graph-based semi-supervised learning at very low label rates, Jeff Calder@UofM

Regression of functions on low-dimensional manifolds by deep neural networks, Wenjing Liao@Georgia T

Regression of functions on low-dimensional manifolds by deep neural networks, Wenjing Liao@Georgia T

Geometry and Symmetry in (some!) Nonconvex Optimization Problems, John Wright@Columbia University

Geometry and Symmetry in (some!) Nonconvex Optimization Problems, John Wright@Columbia University