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