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07/16/2023
Revisiting Implicit Models: Sparsity Trade-offs Capability in Weight-tied Model for Vision Tasks
Implicit models such as Deep Equilibrium Models (DEQs) have garnered sig...
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06/06/2023
On Pitfalls of Test-Time Adaptation
Test-Time Adaptation (TTA) has recently emerged as a promising approach ...
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06/01/2023
Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging
Snapshot compressive imaging emerges as a promising technology for acqui...
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05/18/2023
Prediction with Incomplete Data under Agnostic Mask Distribution Shift
Data with missing values is ubiquitous in many applications. Recent year...
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03/17/2023
No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier
Data heterogeneity is an inherent challenge that hinders the performance...
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02/07/2023
Persuading a Behavioral Agent: Approximately Best Responding and Learning
The classic Bayesian persuasion model assumes a Bayesian and best-respon...
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01/29/2023
FedConceptEM: Robust Federated Learning Under Diverse Distribution Shifts
Federated Learning (FL) is a machine learning paradigm that protects pri...
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01/03/2023
Decentralized Gradient Tracking with Local Steps
Gradient tracking (GT) is an algorithm designed for solving decentralize...
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12/13/2022
How Does Independence Help Generalization? Sample Complexity of ERM on Product Distributions
While many classical notions of learnability (e.g., PAC learnability) ar...
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07/26/2022
The Sample Complexity of Forecast Aggregation
We consider a Bayesian forecast aggregation model where n experts, after...
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05/27/2022
Client Selection in Nonconvex Federated Learning: Improved Convergence Analysis for Optimal Unbiased Sampling Strategy
Federated learning (FL) is a distributed machine learning paradigm that ...
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05/26/2022
FedAug: Reducing the Local Learning Bias Improves Federated Learning on Heterogeneous Data
Federated Learning (FL) is a machine learning paradigm that learns from ...
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05/22/2022
Test-Time Robust Personalization for Federated Learning
Federated Learning (FL) is a machine learning paradigm where many client...
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05/03/2022
Adversarial Training for High-Stakes Reliability
In the future, powerful AI systems may be deployed in high-stakes settin...
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02/15/2022
Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition
In wearable-based human activity recognition (HAR) research, one of the ...
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02/08/2022
An Improved Analysis of Gradient Tracking for Decentralized Machine Learning
We consider decentralized machine learning over a network where the trai...
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12/25/2021
Towards Federated Learning on Time-Evolving Heterogeneous Data
Federated Learning (FL) is an emerging learning paradigm that preserves ...
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12/16/2021
Understanding Memorization from the Perspective of Optimization via Efficient Influence Estimation
Over-parameterized deep neural networks are able to achieve excellent tr...
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11/21/2021
Learning by Active Forgetting for Neural Networks
Remembering and forgetting mechanisms are two sides of the same coin in ...
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10/08/2021
RelaySum for Decentralized Deep Learning on Heterogeneous Data
In decentralized machine learning, workers compute model updates on thei...
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10/08/2021
Nash Convergence of Mean-Based Learning Algorithms in First Price Auctions
Understanding the convergence properties of learning dynamics in repeate...
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08/28/2021
Representation Memorization for Fast Learning New Knowledge without Forgetting
The ability to quickly learn new knowledge (e.g. new classes or data dis...
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08/18/2021
The Multi-Modal Video Reasoning and Analyzing Competition
In this paper, we introduce the Multi-Modal Video Reasoning and Analyzin...
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07/28/2021
From Monopoly to Competition: Optimal Contests Prevail
We study competition among contests in a general model that allows for a...
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07/02/2021
The Optimal Size of an Epistemic Congress
We analyze the optimal size of a congress in a representative democracy....
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04/12/2021
Deep Learning for IoT
Deep learning and other machine learning approaches are deployed to many...
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02/09/2021
Consensus Control for Decentralized Deep Learning
Decentralized training of deep learning models enables on-device learnin...
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02/09/2021
Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data
Decentralized training of deep learning models is a key element for enab...
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11/22/2020
AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting
Traffic forecasting is a fundamental and challenging task in the field o...
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10/12/2020
A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling
The Empirical Revenue Maximization (ERM) is one of the most important pr...
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09/10/2020
XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification
We present XCM, an eXplainable Convolutional neural network for Multivar...
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07/03/2020
Learning Utilities and Equilibria in Non-Truthful Auctions
In non-truthful auctions, agents' utility for a strategy depends on the ...
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06/19/2020
Error Analysis of Symmetric Linear/Bilinear Partially Penalized Immersed Finite Element Methods for Helmholtz Interface Problems
This article presents an error analysis of the symmetric linear/bilinear...
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06/15/2020
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them
We analyze the influence of adversarial training on the loss landscape o...
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06/12/2020
Dynamic Model Pruning with Feedback
Deep neural networks often have millions of parameters. This can hinder ...
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06/12/2020
Ensemble Distillation for Robust Model Fusion in Federated Learning
Federated Learning (FL) is a machine learning setting where many devices...
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06/10/2020
Extrapolation for Large-batch Training in Deep Learning
Deep learning networks are typically trained by Stochastic Gradient Desc...
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04/28/2020
An Enriched Immersed Finite Element Method for Interface Problems with Nonhomogeneous Jump Conditions
This article presents and analyzes a p^th-degree immersed finite element...
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04/26/2020
Masking as an Efficient Alternative to Finetuning for Pretrained Language Models
We present an efficient method of utilizing pretrained language models, ...
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03/07/2020
DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images
Change detection is a basic task of remote sensing image processing. The...
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01/18/2020
Deep Collaborative Embedding for information cascade prediction
Recently, information cascade prediction has attracted increasing intere...
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12/19/2019
Overcoming Long-term Catastrophic Forgetting through Adversarial Neural Pruning and Synaptic Consolidation
Enabling a neural network to sequentially learn multiple tasks is of gre...
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12/02/2019
On Clearing Prices in Matching Markets: A Simple Characterization without Duality
Duality of linear programming is a standard approach to the classical we...
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07/22/2019
Decentralized Deep Learning with Arbitrary Communication Compression
Decentralized training of deep learning models is a key element for enab...
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05/28/2019
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data
For recurrent neural networks trained on time series with target and exo...
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01/14/2019
FoundationDB Record Layer: A Multi-Tenant Structured Datastore
The FoundationDB Record Layer is an open source library that provides a ...
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08/22/2018
Don't Use Large Mini-Batches, Use Local SGD
Mini-batch stochastic gradient methods are the current state of the art ...
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06/17/2018
Multi-variable LSTM neural network for autoregressive exogenous model
In this paper, we propose multi-variable LSTM capable of accurate foreca...
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04/14/2018
An interpretable LSTM neural network for autoregressive exogenous model
In this paper, we propose an interpretable LSTM recurrent neural network...
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04/04/2018