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09/07/2023
Fast FixMatch: Faster Semi-Supervised Learning with Curriculum Batch Size
Advances in Semi-Supervised Learning (SSL) have almost entirely closed t...
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09/06/2023
Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat
We carefully evaluate a number of algorithms for learning in a federated...
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06/19/2023
Adaptive Federated Learning with Auto-Tuned Clients
Federated learning (FL) is a distributed machine learning framework wher...
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06/14/2023
Fed-ZERO: Efficient Zero-shot Personalization with Federated Mixture of Experts
One of the goals in Federated Learning (FL) is to create personalized mo...
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06/13/2023
Accelerated Convergence of Nesterov's Momentum for Deep Neural Networks under Partial Strong Convexity
Current state-of-the-art analyses on the convergence of gradient descent...
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05/26/2023
Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time
Large language models(LLMs) have sparked a new wave of exciting AI appli...
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11/09/2022
Extragradient with Positive Momentum is Optimal for Games with Cross-Shaped Jacobian Spectrum
The extragradient method has recently gained increasing attention, due t...
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11/09/2022
Cold Start Streaming Learning for Deep Networks
The ability to dynamically adapt neural networks to newly-available data...
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10/29/2022
Strong Lottery Ticket Hypothesis with ε–perturbation
The strong Lottery Ticket Hypothesis (LTH) claims the existence of a sub...
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10/28/2022
LOFT: Finding Lottery Tickets through Filter-wise Training
Recent work on the Lottery Ticket Hypothesis (LTH) shows that there exis...
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10/28/2022
Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout
Asynchronous learning protocols have regained attention lately, especial...
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05/08/2022
DPMS: An ADD-Based Symbolic Approach for Generalized MaxSAT Solving
Boolean MaxSAT, as well as generalized formulations such as Min-MaxSAT a...
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03/22/2022
Local Stochastic Factored Gradient Descent for Distributed Quantum State Tomography
We propose a distributed Quantum State Tomography (QST) protocol, named ...
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03/20/2022
PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication
Graph Convolutional Networks (GCNs) is the state-of-the-art method for l...
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12/07/2021
i-SpaSP: Structured Neural Pruning via Sparse Signal Recovery
We propose a novel, structured pruning algorithm for neural networks – t...
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12/05/2021
On the Convergence of Shallow Neural Network Training with Randomly Masked Neurons
Given a dense shallow neural network, we focus on iteratively creating, ...
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11/11/2021
Convergence and Stability of the Stochastic Proximal Point Algorithm with Momentum
Stochastic gradient descent with momentum (SGDM) is the dominant algorit...
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10/23/2021
Federated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Tasks
Federated learning enables many local devices to train a deep learning m...
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07/31/2021
Provably Efficient Lottery Ticket Discovery
The lottery ticket hypothesis (LTH) claims that randomly-initialized, de...
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07/09/2021
REX: Revisiting Budgeted Training with an Improved Schedule
Deep learning practitioners often operate on a computational and monetar...
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07/02/2021
ResIST: Layer-Wise Decomposition of ResNets for Distributed Training
We propose , a novel distributed training protocol for Residual Networks...
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07/02/2021
Mitigating deep double descent by concatenating inputs
The double descent curve is one of the most intriguing properties of dee...
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06/16/2021
Momentum-inspired Low-Rank Coordinate Descent for Diagonally Constrained SDPs
We present a novel, practical, and provable approach for solving diagona...
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04/14/2021
Fast quantum state reconstruction via accelerated non-convex programming
We propose a new quantum state reconstruction method that combines ideas...
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02/20/2021
GIST: Distributed Training for Large-Scale Graph Convolutional Networks
The graph convolutional network (GCN) is a go-to solution for machine le...
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12/17/2020
Rank-One Measurements of Low-Rank PSD Matrices Have Small Feasible Sets
We study the role of the constraint set in determining the solution to l...
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12/14/2020
On Continuous Local BDD-Based Search for Hybrid SAT Solving
We explore the potential of continuous local search (CLS) in SAT solving...
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11/28/2020
On Generalization of Adaptive Methods for Over-parameterized Linear Regression
Over-parameterization and adaptive methods have played a crucial role in...
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11/25/2020
ImCLR: Implicit Contrastive Learning for Image Classification
Contrastive learning is an effective method for learning visual represen...
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07/01/2020
Bayesian Coresets: An Optimization Perspective
Bayesian coresets have emerged as a promising approach for implementing ...
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12/02/2019
FourierSAT: A Fourier Expansion-Based Algebraic Framework for Solving Hybrid Boolean Constraints
The Boolean SATisfiability problem (SAT) is of central importance in com...
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11/15/2019
Optimal Mini-Batch Size Selection for Fast Gradient Descent
This paper presents a methodology for selecting the mini-batch size that...
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11/12/2019
Negative sampling in semi-supervised learning
We introduce Negative Sampling in Semi-Supervised Learning (NS3L), a sim...
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10/29/2019
Learning Sparse Distributions using Iterative Hard Thresholding
Iterative hard thresholding (IHT) is a projected gradient descent algori...
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10/11/2019
Decaying momentum helps neural network training
Momentum is a simple and popular technique in deep learning for gradient...
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10/04/2019
Distributed Learning of Deep Neural Networks using Independent Subnet Training
Stochastic gradient descent (SGD) is the method of choice for distribute...
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03/29/2019
SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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02/01/2019
Compressing Gradient Optimizers via Count-Sketches
Many popular first-order optimization methods (e.g., Momentum, AdaGrad, ...
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11/16/2018
Minimum norm solutions do not always generalize well for over-parameterized problems
Stochastic gradient descent is the de facto algorithm for training deep ...
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06/06/2018
Implicit regularization and solution uniqueness in over-parameterized matrix sensing
We consider whether algorithmic choices in over-parameterized linear mat...
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06/01/2018
Run Procrustes, Run! On the convergence of accelerated Procrustes Flow
In this work, we present theoretical results on the convergence of non-c...
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05/24/2018
Simple and practical algorithms for ℓ_p-norm low-rank approximation
We propose practical algorithms for entrywise ℓ_p-norm low-rank approxim...
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05/23/2018
Approximate Newton-based statistical inference using only stochastic gradients
We present a novel inference framework for convex empirical risk minimiz...
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12/26/2017
IHT dies hard: Provable accelerated Iterative Hard Thresholding
We study --both in theory and practice-- the use of momentum motions in ...
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11/04/2017
Provable quantum state tomography via non-convex methods
With nowadays steadily growing quantum processors, it is required to dev...
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05/21/2017
Statistical inference using SGD
We present a novel method for frequentist statistical inference in M-est...
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09/12/2016
Non-square matrix sensing without spurious local minima via the Burer-Monteiro approach
We consider the non-square matrix sensing problem, under restricted isom...
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06/04/2016
Provable Burer-Monteiro factorization for a class of norm-constrained matrix problems
We study the projected gradient descent method on low-rank matrix proble...
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05/29/2016
A simple and provable algorithm for sparse diagonal CCA
Given two sets of variables, derived from a common set of samples, spars...
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05/02/2016
