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09/12/2023
Adapt and Diffuse: Sample-adaptive Reconstruction via Latent Diffusion Models
Inverse problems arise in a multitude of applications, where the goal is...
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07/25/2023
mL-BFGS: A Momentum-based L-BFGS for Distributed Large-Scale Neural Network Optimization
Quasi-Newton methods still face significant challenges in training large...
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07/24/2023
Learning Provably Robust Estimators for Inverse Problems via Jittering
Deep neural networks provide excellent performance for inverse problems ...
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07/02/2023
Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory
Deep learning models are prone to forgetting information learned in the ...
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06/06/2023
On the Role of Attention in Prompt-tuning
Prompt-tuning is an emerging strategy to adapt large language models (LL...
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03/25/2023
DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-Consistency
Diffusion models have established new state of the art in a multitude of...
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03/24/2023
Implicit Balancing and Regularization: Generalization and Convergence Guarantees for Overparameterized Asymmetric Matrix Sensing
Recently, there has been significant progress in understanding the conve...
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03/07/2023
CUDA: Convolution-based Unlearnable Datasets
Large-scale training of modern deep learning models heavily relies on pu...
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12/10/2022
SMILE: Scaling Mixture-of-Experts with Efficient Bi-level Routing
The mixture of Expert (MoE) parallelism is a recent advancement that sca...
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09/18/2022
The Geometry of Self-supervised Learning Models and its Impact on Transfer Learning
Self-supervised learning (SSL) has emerged as a desirable paradigm in co...
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06/30/2022
Neural Networks can Learn Representations with Gradient Descent
Significant theoretical work has established that in specific regimes, n...
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05/13/2022
Toward a Geometrical Understanding of Self-supervised Contrastive Learning
Self-supervised learning (SSL) is currently one of the premier technique...
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03/31/2022
Learning from many trajectories
We initiate a study of supervised learning from many independent sequenc...
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03/15/2022
HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction
In accelerated MRI reconstruction, the anatomy of a patient is recovered...
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11/22/2021
FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks
Federated Learning (FL) is a distributed learning paradigm that can lear...
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10/06/2021
SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision
Federated Learning (FL) is transforming the ML training ecosystem from a...
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09/23/2021
Outlier-Robust Sparse Estimation via Non-Convex Optimization
We explore the connection between outlier-robust high-dimensional statis...
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06/28/2021
Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction
Recently there has been significant theoretical progress on understandin...
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06/28/2021
Data augmentation for deep learning based accelerated MRI reconstruction with limited data
Deep neural networks have emerged as very successful tools for image res...
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04/29/2021
Generalization Guarantees for Neural Architecture Search with Train-Validation Split
Neural Architecture Search (NAS) is a popular method for automatically d...
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04/12/2021
Understanding Overparameterization in Generative Adversarial Networks
A broad class of unsupervised deep learning methods such as Generative A...
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02/05/2021
PipeTransformer: Automated Elastic Pipelining for Distributed Training of Transformers
The size of Transformer models is growing at an unprecedented pace. It h...
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11/16/2020
Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View
Contemporary machine learning applications often involve classification ...
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10/21/2020
Precise Statistical Analysis of Classification Accuracies for Adversarial Training
Despite the wide empirical success of modern machine learning algorithms...
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06/16/2020
Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks
Transfer learning has emerged as a powerful technique for improving the ...
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05/26/2020
Approximation Schemes for ReLU Regression
We consider the fundamental problem of ReLU regression, where the goal i...
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05/07/2020
Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximation
Un-trained convolutional neural networks have emerged as highly successf...
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05/04/2020
High-Dimensional Robust Mean Estimation via Gradient Descent
We study the problem of high-dimensional robust mean estimation in the p...
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02/26/2020
3D Phase Retrieval at Nano-Scale via Accelerated Wirtinger Flow
Imaging 3D nano-structures at very high resolution is crucial in a varie...
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02/24/2020
Precise Tradeoffs in Adversarial Training for Linear Regression
Despite breakthrough performance, modern learning models are known to be...
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12/26/2019
Convergence and sample complexity of gradient methods for the model-free linear quadratic regulator problem
Model-free reinforcement learning attempts to find an optimal control ac...
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10/31/2019
Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators
Convolutional Neural Networks (CNNs) have emerged as highly successful t...
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06/12/2019
Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian
Modern neural network architectures often generalize well despite contai...
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03/27/2019
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks
Modern neural networks are typically trained in an over-parameterized re...
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02/12/2019
Towards moderate overparameterization: global convergence guarantees for training shallow neural networks
Many modern neural network architectures are trained in an overparameter...
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01/19/2019
Fitting ReLUs via SGD and Quantized SGD
In this paper we focus on the problem of finding the optimal weights of ...
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12/25/2018
Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path?
Many modern learning tasks involve fitting nonlinear models to data whic...
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05/24/2018
Polynomially Coded Regression: Optimal Straggler Mitigation via Data Encoding
We consider the problem of training a least-squares regression model on ...
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05/16/2018
End-to-end Learning of a Convolutional Neural Network via Deep Tensor Decomposition
In this paper we study the problem of learning the weights of a deep con...
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03/31/2018
Fundamental Resource Trade-offs for Encoded Distributed Optimization
Dealing with the shear size and complexity of today's massive data sets ...
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10/27/2017
Near-Optimal Straggler Mitigation for Distributed Gradient Methods
Modern learning algorithms use gradient descent updates to train inferen...
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07/16/2017
Theoretical insights into the optimization landscape of over-parameterized shallow neural networks
In this paper we study the problem of learning a shallow artificial neur...
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05/10/2017
Learning ReLUs via Gradient Descent
In this paper we study the problem of learning Rectified Linear Units (R...
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02/20/2017
Structured signal recovery from quadratic measurements: Breaking sample complexity barriers via nonconvex optimization
This paper concerns the problem of recovering an unknown but structured ...
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10/23/2016
Fast and Reliable Parameter Estimation from Nonlinear Observations
In this paper we study the problem of recovering a structured but unknow...
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11/10/2015
Experimental robustness of Fourier Ptychography phase retrieval algorithms
Fourier ptychography is a new computational microscopy technique that pr...
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06/11/2015
Isometric sketching of any set via the Restricted Isometry Property
In this paper we show that for the purposes of dimensionality reduction ...
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12/23/2014
Approximate Subspace-Sparse Recovery with Corrupted Data via Constrained ℓ_1-Minimization
High-dimensional data often lie in low-dimensional subspaces correspondi...
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01/11/2013
Robust subspace clustering
Subspace clustering refers to the task of finding a multi-subspace repre...
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11/05/2012