
Selfsupervised Representation Learning with Relative Predictive Coding
This paper introduces Relative Predictive Coding (RPC), a new contrastiv...
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Understanding and Mitigating Accuracy Disparity in Regression
With the widespread deployment of largescale prediction systems in high...
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Acoustic Structure Inverse Design and Optimization Using Deep Learning
From ancient to modern times, acoustic structures have been used to cont...
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Fundamental Limits and Tradeoffs in Invariant Representation Learning
Many machine learning applications involve learning representations that...
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Modelbased Policy Optimization with Unsupervised Model Adaptation
Modelbased reinforcement learning methods learn a dynamics model with r...
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Learning Invariant Representations and Risks for Semisupervised Domain Adaptation
The success of supervised learning hinges on the assumption that the tra...
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Graph Adversarial Networks: Protecting Information against Adversarial Attacks
We study the problem of protecting information when learning with graph ...
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A Review of SingleSource Deep Unsupervised Visual Domain Adaptation
Largescale labeled training datasets have enabled deep neural networks ...
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On Learning LanguageInvariant Representations for Universal Machine Translation
The goal of universal machine translation is to learn to translate betwe...
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Neural Methods for Pointwise Dependency Estimation
Since its inception, the neural estimation of mutual information (MI) ha...
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Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift
Adversarial learning has demonstrated good performance in the unsupervis...
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Continual Learning with Adaptive Weights (CLAW)
Approaches to continual learning aim to successfully learn a set of rela...
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Conditional Learning of Fair Representations
We propose a novel algorithm for learning fair representations that can ...
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Learning Neural Networks with Adaptive Regularization
Feedforward neural networks can be understood as a combination of an in...
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Inherent Tradeoffs in Learning Fair Representation
With the prevalence of machine learning in highstakes applications, esp...
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Adversarial TaskSpecific Privacy Preservation under Attribute Attack
With the prevalence of machine learning services, crowdsourced data cont...
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On Learning Invariant Representation for Domain Adaptation
Due to the ability of deep neural nets to learn rich representations, re...
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On Strategyproof Conference Peer Review
We consider peer review in a conference setting where there is typically...
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ConvolutionalRecurrent Neural Networks for Speech Enhancement
We propose an endtoend model based on convolutional and recurrent neur...
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Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations
Strict partial order is a mathematical structure commonly seen in relati...
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FrankWolfe Optimization for SymmetricNMF under Simplicial Constraint
We propose a FrankWolfe (FW) solver to optimize the symmetric nonnegati...
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Multiple Source Domain Adaptation with Adversarial Training of Neural Networks
While domain adaptation has been actively researched in recent years, mo...
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Principled Hybrids of Generative and Discriminative Domain Adaptation
We propose a probabilistic framework for domain adaptation that blends b...
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Linear Time Computation of Moments in SumProduct Networks
Bayesian online algorithms for SumProduct Networks (SPNs) need to updat...
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Efficient Multitask Feature and Relationship Learning
In this paper we propose a multiconvex framework for multitask learnin...
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A Unified Approach for Learning the Parameters of SumProduct Networks
We present a unified approach for learning the parameters of SumProduct...
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SelfAdaptive Hierarchical Sentence Model
The ability to accurately model a sentence at varying stages (e.g., word...
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On the Relationship between SumProduct Networks and Bayesian Networks
In this paper, we establish some theoretical connections between SumPro...
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Han Zhao
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