49 citations · 408 across the 42 of their papers we have counts for
27 papers · 1 filter
Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks
Yiwei Lu, Gautam Kamath, Yaoliang Yu
Indiscriminate data poisoning attacks aim to decrease a model's test accuracy by injecting a small amount of corrupted training data. Despite significant interest, existing attacks…
DP-VAE: Differentially Private Pre-trained Variational Autoencoders
Dihong Jiang, Guojun Zhang, Mahdi Karami +3
Modern machine learning systems achieve great success when trained on large datasets. However, these datasets usually contain sensitive information (e.g. medical records, face imag…
Towards Explanation for Unsupervised Graph-Level Representation Learning
Qinghua Zheng, Jihong Wang, Minnan Luo +4
Due to the superior performance of Graph Neural Networks (GNNs) in various domains, there is an increasing interest in the GNN explanation problem "\emph{which fraction of the inpu…
Mitigating Data Heterogeneity in Federated Learning with Data Augmentation
Artur Back de Luca, Guojun Zhang, Xi Chen +1
Federated Learning (FL) is a prominent framework that enables training a centralized model while securing user privacy by fusing local, decentralized models. In this setting, one m…
Indiscriminate Data Poisoning Attacks on Neural Networks
Yiwei Lu, Gautam Kamath, Yaoliang Yu
Data poisoning attacks, in which a malicious adversary aims to influence a model by injecting "poisoned" data into the training process, have attracted significant recent attention…
Proportional Fairness in Federated Learning
Guojun Zhang, Saber Malekmohammadi, Xi Chen +1
With the increasingly broad deployment of federated learning (FL) systems in the real world, it is critical but challenging to ensure fairness in FL, i.e. reasonably satisfactory p…
Demystifying and Generalizing BinaryConnect
Tim Dockhorn, Yaoliang Yu, Eyyüb Sari +2
BinaryConnect (BC) and its many variations have become the de facto standard for neural network quantization. However, our understanding of the inner workings of BC is still quite…
An Operator Splitting View of Federated Learning
Saber Malekmohammadi, Kiarash Shaloudegi, Zeou Hu +1
Over the past few years, the federated learning () community has witnessed a proliferation of new algorithms. However, our understating of the theory of…
Quantifying and Improving Transferability in Domain Generalization
Guojun Zhang, Han Zhao, Yaoliang Yu +1
Out-of-distribution generalization is one of the key challenges when transferring a model from the lab to the real world. Existing efforts mostly focus on building invariant featur…
OLALA: Object-Level Active Learning for Efficient Document Layout Annotation
Zejiang Shen, Jian Zhao, Melissa Dell +2
Document images often have intricate layout structures, with numerous content regions (e.g. texts, figures, tables) densely arranged on each page. This makes the manual annotation…
Stronger and Faster Wasserstein Adversarial Attacks
Kaiwen Wu, Allen Houze Wang, Yaoliang Yu
Deep models, while being extremely flexible and accurate, are surprisingly vulnerable to "small, imperceptible" perturbations known as adversarial attacks. While the majority of ex…
Newton-type Methods for Minimax Optimization
Guojun Zhang, Kaiwen Wu, Pascal Poupart +1
Differential games, in particular two-player sequential zero-sum games (a.k.a. minimax optimization), have been an important modeling tool in applied science and received renewed i…
Federated Learning Meets Multi-objective Optimization
Zeou Hu, Kiarash Shaloudegi, Guojun Zhang +1
Federated learning has emerged as a promising, massively distributed way to train a joint deep model over large amounts of edge devices while keeping private user data strictly on…
Network Comparison with Interpretable Contrastive Network Representation Learning
Takanori Fujiwara, Jian Zhao, Francine Chen +2
Identifying unique characteristics in a network through comparison with another network is an essential network analysis task. For example, with networks of protein interactions ob…
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space
Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu +1
Invariance (defined in a general sense) has been one of the most effective priors for representation learning. Direct factorization of parametric models is feasible only for a smal…
Optimality and Stability in Non-Convex Smooth Games
Guojun Zhang, Pascal Poupart, Yaoliang Yu
Convergence to a saddle point for convex-concave functions has been studied for decades, while recent years has seen a surge of interest in non-convex (zero-sum) smooth games, moti…
Convergence of Gradient Methods on Bilinear Zero-Sum Games
Guojun Zhang, Yaoliang Yu
Min-max formulations have attracted great attention in the ML community due to the rise of deep generative models and adversarial methods, while understanding the dynamics of gradi…
Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin
Kaiwen Wu, Yaoliang Yu
Deep models, while being extremely versatile and accurate, are vulnerable to adversarial attacks: slight perturbations that are imperceptible to humans can completely flip the pred…
Sum-of-Squares Polynomial Flow
Priyank Jaini, Kira A. Selby, Yaoliang Yu
Triangular map is a recent construct in probability theory that allows one to transform any source probability density function to any target density function. Based on triangular…
Distributional Reinforcement Learning for Efficient Exploration
Borislav Mavrin, Shangtong Zhang, Hengshuai Yao +3
In distributional reinforcement learning (RL), the estimated distribution of value function models both the parametric and intrinsic uncertainties. We propose a novel and efficient…
Provably noise-robust, regularised -means clustering
Shrinu Kushagra, Yaoliang Yu, Shai Ben-David
We consider the problem of clustering in the presence of noise. That is, when on top of cluster structure, the data also contains a subset of \emph{unstructured} points. Our goal i…
Convex-constrained Sparse Additive Modeling and Its Extensions
Junming Yin, Yaoliang Yu
Sparse additive modeling is a class of effective methods for performing high-dimensional nonparametric regression. In this work we show how shape constraints such as convexity/conc…
Dropout with Expectation-linear Regularization
Xuezhe Ma, Yingkai Gao, Zhiting Hu +3
Dropout, a simple and effective way to train deep neural networks, has led to a number of impressive empirical successes and spawned many recent theoretical investigations. However…
Distributed Machine Learning via Sufficient Factor Broadcasting
Pengtao Xie, Jin Kyu Kim, Yi Zhou +4
Matrix-parametrized models, including multiclass logistic regression and sparse coding, are used in machine learning (ML) applications ranging from computer vision to computational…