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09/19/2023
Mixture Weight Estimation and Model Prediction in Multi-source Multi-target Domain Adaptation
We consider the problem of learning a model from multiple heterogeneous ...
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02/23/2023
On the Hardness of Robustness Transfer: A Perspective from Rademacher Complexity over Symmetric Difference Hypothesis Space
Recent studies demonstrated that the adversarially robust learning under...
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02/22/2023
Do We Really Need Complicated Model Architectures For Temporal Networks?
Recurrent neural network (RNN) and self-attention mechanism (SAM) are th...
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02/17/2023
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection
As privacy protection receives much attention, unlearning the effect of ...
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10/17/2022
Tight Analysis of Extra-gradient and Optimistic Gradient Methods For Nonconvex Minimax Problems
Despite the established convergence theory of Optimistic Gradient Descen...
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03/17/2022
Learning Distributionally Robust Models at Scale via Composite Optimization
To train machine learning models that are robust to distribution shifts ...
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11/19/2021
Dynamic Graph Representation Learning via Graph Transformer Networks
Dynamic graph representation learning is an important task with widespre...
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11/16/2021
Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks
Despite the recent success of Graph Neural Networks (GNNs), training GNN...
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10/28/2021
On Provable Benefits of Depth in Training Graph Convolutional Networks
Graph Convolutional Networks (GCNs) are known to suffer from performance...
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10/26/2021
Meta-learning with an Adaptive Task Scheduler
To benefit the learning of a new task, meta-learning has been proposed t...
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07/22/2021
Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time
In this paper we prove that Local (S)GD (or FedAvg) can optimize two-lay...
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04/04/2021
Pareto Efficient Fairness in Supervised Learning: From Extraction to Tracing
As algorithmic decision-making systems are becoming more pervasive, it i...
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03/03/2021
On the Importance of Sampling in Learning Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have achieved impressive empirical a...
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02/25/2021
Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency
Local SGD is a promising approach to overcome the communication overhead...
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02/25/2021
Distributionally Robust Federated Averaging
In this paper, we study communication efficient distributed algorithms f...
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02/08/2021
Communication-efficient k-Means for Edge-based Machine Learning
We consider the problem of computing the k-means centers for a large hig...
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10/22/2020
Online Structured Meta-learning
Learning quickly is of great importance for machine intelligence deploye...
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07/02/2020
Federated Learning with Compression: Unified Analysis and Sharp Guarantees
In federated learning, communication cost is often a critical bottleneck...
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06/24/2020
Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks
Sampling methods (e.g., node-wise, layer-wise, or subgraph) has become a...
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03/30/2020
Adaptive Personalized Federated Learning
Investigation of the degree of personalization in federated learning alg...
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11/12/2019
Efficient Fair Principal Component Analysis
The flourishing assessments of fairness measure in machine learning algo...
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10/31/2019
On the Convergence of Local Descent Methods in Federated Learning
In federated distributed learning, the goal is to optimize a global trai...
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10/30/2019
Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization
Communication overhead is one of the key challenges that hinders the sca...
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05/20/2017
Learning Feature Nonlinearities with Non-Convex Regularized Binned Regression
For various applications, the relations between the dependent and indepe...
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10/10/2016
Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data
Sketching techniques have become popular for scaling up machine learning...
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11/04/2015
Train and Test Tightness of LP Relaxations in Structured Prediction
Structured prediction is used in areas such as computer vision and natur...
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02/07/2014
Binary Excess Risk for Smooth Convex Surrogates
In statistical learning theory, convex surrogates of the 0-1 loss are hi...
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01/18/2014
Excess Risk Bounds for Exponentially Concave Losses
The overarching goal of this paper is to derive excess risk bounds for l...
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11/19/2013
Beating the Minimax Rate of Active Learning with Prior Knowledge
Active learning refers to the learning protocol where the learner is all...
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02/01/2013
Sparse Multiple Kernel Learning with Geometric Convergence Rate
In this paper, we study the problem of sparse multiple kernel learning (...
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08/30/2012
