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03/07/2023
Exploring the Limits of Indiscriminate Data Poisoning Attacks
Indiscriminate data poisoning attacks aim to decrease a model's test acc...
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08/05/2022
DP^2-VAE: Differentially Private Pre-trained Variational Autoencoders
Modern machine learning systems achieve great success when trained on la...
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07/31/2022
Building an Efficiency Pipeline: Commutativity and Cumulativeness of Efficiency Operators for Transformers
There exists a wide variety of efficiency methods for natural language p...
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06/20/2022
Mitigating Data Heterogeneity in Federated Learning with Data Augmentation
Federated Learning (FL) is a prominent framework that enables training a...
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05/20/2022
Towards Explanation for Unsupervised Graph-Level Representation Learning
Due to the superior performance of Graph Neural Networks (GNNs) in vario...
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04/19/2022
Indiscriminate Data Poisoning Attacks on Neural Networks
Data poisoning attacks, in which a malicious adversary aims to influence...
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02/03/2022
Equality Is Not Equity: Proportional Fairness in Federated Learning
Ensuring fairness of machine learning (ML) algorithms is becoming an inc...
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10/25/2021
Demystifying and Generalizing BinaryConnect
BinaryConnect (BC) and its many variations have become the de facto stan...
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08/12/2021
An Operator Splitting View of Federated Learning
Over the past few years, the federated learning () community has witness...
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07/07/2021
S^3: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks
Shift neural networks reduce computation complexity by removing expensiv...
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06/07/2021
Quantifying and Improving Transferability in Domain Generalization
Out-of-distribution generalization is one of the key challenges when tra...
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10/23/2020
Posterior Differential Regularization with f-divergence for Improving Model Robustness
We address the problem of enhancing model robustness through regularizat...
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10/05/2020
OLALA: Object-Level Active Learning Based Layout Annotation
In layout object detection problems, the ground-truth datasets are const...
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08/06/2020
Stronger and Faster Wasserstein Adversarial Attacks
Deep models, while being extremely flexible and accurate, are surprising...
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06/25/2020
Newton-type Methods for Minimax Optimization
Differential games, in particular two-player sequential games (a.k.a. mi...
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06/20/2020
FedMGDA+: Federated Learning meets Multi-objective Optimization
Federated learning has emerged as a promising, massively distributed way...
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06/16/2020
Density Deconvolution with Normalizing Flows
Density deconvolution is the task of estimating a probability density fu...
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05/25/2020
Interpretable Contrastive Learning for Networks
Contrastive learning (CL) is an emerging analysis approach that aims to ...
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04/28/2020
Showing Your Work Doesn't Always Work
In natural language processing, a recently popular line of work explores...
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04/27/2020
DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference
Large-scale pre-trained language models such as BERT have brought signif...
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04/25/2020
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space
Invariance (defined in a general sense) has been one of the most effecti...
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03/08/2020
Complete Hierarchy of Relaxation for Constrained Signomial Positivity
In this article, we prove that the Sums-of-AM/GM Exponential (SAGE) rela...
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02/27/2020
Optimality and Stability in Non-Convex-Non-Concave Min-Max Optimization
Convergence to a saddle point for convex-concave functions has been stud...
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01/28/2020
Unsupervised Multilingual Alignment using Wasserstein Barycenter
We study unsupervised multilingual alignment, the problem of finding wor...
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11/15/2019
Exploiting Token and Path-based Representations of Code for Identifying Security-Relevant Commits
Public vulnerability databases such as CVE and NVD account for only 60 s...
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08/15/2019
Convergence Behaviour of Some Gradient-Based Methods on Bilinear Zero-Sum Games
Min-max formulations have attracted great attention in the ML community ...
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08/15/2019
Convergence Behaviour of Some Gradient-Based Methods on Bilinear Games
Min-max optimization has attracted much attention in the machine learnin...
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07/26/2019
Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin
Deep models, while being extremely versatile and accurate, are vulnerabl...
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07/10/2019
Tails of Triangular Flows
Triangular maps are a construct in probability theory that allows the tr...
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05/13/2019
Distributional Reinforcement Learning for Efficient Exploration
In distributional reinforcement learning (RL), the estimated distributio...
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05/07/2019
Sum-of-Squares Polynomial Flow
Triangular map is a recent construct in probability theory that allows o...
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05/01/2017
Convex-constrained Sparse Additive Modeling and Its Extensions
Sparse additive modeling is a class of effective methods for performing ...
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09/26/2016
Dropout with Expectation-linear Regularization
Dropout, a simple and effective way to train deep neural networks, has l...
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01/31/2016
Additive Approximations in High Dimensional Nonparametric Regression via the SALSA
High dimensional nonparametric regression is an inherently difficult pro...
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10/17/2014
Generalized Conditional Gradient for Sparse Estimation
Structured sparsity is an important modeling tool that expands the appli...
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12/30/2013
Petuum: A New Platform for Distributed Machine Learning on Big Data
What is a systematic way to efficiently apply a wide spectrum of advance...
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06/27/2012