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08/27/2023
Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective
Linear scalarization, i.e., combining all loss functions by a weighted s...
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06/15/2023
FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods
This paper introduces the Fair Fairness Benchmark (), a benchmarking fra...
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05/22/2023
In-Context Learning of Large Language Models Explained as Kernel Regression
Large language models (LLMs) have initiated a paradigm shift in transfer...
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04/20/2023
Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs
How can we learn effective node representations on textual graphs? Graph...
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03/10/2023
Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning
Contrastive loss has been increasingly used in learning representations ...
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01/27/2023
Dual-View Selective Instance Segmentation Network for Unstained Live Adherent Cells in Differential Interference Contrast Images
Despite recent advances in data-independent and deep-learning algorithms...
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12/21/2022
Learning List-Level Domain-Invariant Representations for Ranking
Domain adaptation aims to transfer the knowledge acquired by models trai...
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11/03/2022
Fair and Optimal Classification via Transports to Wasserstein-Barycenter
Fairness in automated decision-making systems has gained increasing atte...
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10/22/2022
Greedy Modality Selection via Approximate Submodular Maximization
Multimodal learning considers learning from multi-modality data, aiming ...
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09/27/2022
Dense-TNT: Efficient Vehicle Type Classification Neural Network Using Satellite Imagery
Accurate vehicle type classification serves a significant role in the in...
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09/14/2022
Interpolation-based immersed finite element and isogeometric analysis
We introduce a new paradigm for immersed finite element and isogeometric...
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09/01/2022
Exploring Gradient-based Multi-directional Controls in GANs
Generative Adversarial Networks (GANs) have been widely applied in model...
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08/31/2022
Application of Data Encryption in Chinese Named Entity Recognition
Recently, with the continuous development of deep learning, the performa...
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07/21/2022
FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data
Federated learning (FL) provides an effective paradigm to train machine ...
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05/23/2022
Conditional Supervised Contrastive Learning for Fair Text Classification
Contrastive representation learning has gained much attention due to its...
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04/25/2022
Algorithms and Theory for Supervised Gradual Domain Adaptation
The phenomenon of data distribution evolving over time has been observed...
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04/18/2022
Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond
The vast majority of existing algorithms for unsupervised domain adaptat...
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03/09/2022
Model-Agnostic Multitask Fine-tuning for Few-shot Vision-Language Transfer Learning
Despite achieving state-of-the-art zero-shot performance, existing visio...
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02/11/2022
Conditional Contrastive Learning with Kernel
Conditional contrastive learning frameworks consider the conditional sam...
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01/30/2022
Provable Domain Generalization via Invariant-Feature Subspace Recovery
Domain generalization asks for models trained on a set of training envir...
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11/19/2021
Towards Return Parity in Markov Decision Processes
Algorithmic decisions made by machine learning models in high-stakes dom...
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06/16/2021
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation
Multi-task learning (MTL) aims to improve the generalization of several ...
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06/16/2021
Costs and Benefits of Wasserstein Fair Regression
Real-world applications of machine learning tools in high-stakes domains...
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06/11/2021
Online Continual Adaptation with Active Self-Training
Models trained with offline data often suffer from continual distributio...
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06/11/2021
Invariant Information Bottleneck for Domain Generalization
The main challenge for domain generalization (DG) is to overcome the pot...
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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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06/05/2021
Conditional Contrastive Learning: Removing Undesirable Information in Self-Supervised Representations
Self-supervised learning is a form of unsupervised learning that leverag...
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03/21/2021
Self-supervised Representation Learning with Relative Predictive Coding
This paper introduces Relative Predictive Coding (RPC), a new contrastiv...
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02/24/2021
Understanding and Mitigating Accuracy Disparity in Regression
With the widespread deployment of large-scale prediction systems in high...
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01/29/2021
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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12/19/2020
Fundamental Limits and Tradeoffs in Invariant Representation Learning
Many machine learning applications involve learning representations that...
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10/19/2020
Model-based Policy Optimization with Unsupervised Model Adaptation
Model-based reinforcement learning methods learn a dynamics model with r...
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10/09/2020
Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation
The success of supervised learning hinges on the assumption that the tra...
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09/28/2020
Graph Adversarial Networks: Protecting Information against Adversarial Attacks
We study the problem of protecting information when learning with graph ...
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09/01/2020
A Review of Single-Source Deep Unsupervised Visual Domain Adaptation
Large-scale labeled training datasets have enabled deep neural networks ...
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08/11/2020
On Learning Language-Invariant Representations for Universal Machine Translation
The goal of universal machine translation is to learn to translate betwe...
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06/09/2020
Neural Methods for Point-wise Dependency Estimation
Since its inception, the neural estimation of mutual information (MI) ha...
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03/10/2020
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift
Adversarial learning has demonstrated good performance in the unsupervis...
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11/21/2019
Continual Learning with Adaptive Weights (CLAW)
Approaches to continual learning aim to successfully learn a set of rela...
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10/16/2019
Conditional Learning of Fair Representations
We propose a novel algorithm for learning fair representations that can ...
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07/14/2019
Learning Neural Networks with Adaptive Regularization
Feed-forward neural networks can be understood as a combination of an in...
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06/19/2019
Inherent Tradeoffs in Learning Fair Representation
With the prevalence of machine learning in high-stakes applications, esp...
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06/19/2019
Adversarial Task-Specific Privacy Preservation under Attribute Attack
With the prevalence of machine learning services, crowdsourced data cont...
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01/27/2019
On Learning Invariant Representation for Domain Adaptation
Due to the ability of deep neural nets to learn rich representations, re...
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06/16/2018
On Strategyproof Conference Peer Review
We consider peer review in a conference setting where there is typically...
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05/02/2018
Convolutional-Recurrent Neural Networks for Speech Enhancement
We propose an end-to-end model based on convolutional and recurrent neur...
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01/19/2018
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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06/20/2017
Frank-Wolfe Optimization for Symmetric-NMF under Simplicial Constraint
We propose a Frank-Wolfe (FW) solver to optimize the symmetric nonnegati...
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05/26/2017
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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05/25/2017
