66 citations · 300 across the 29 of their papers we have counts for
26 papers · 1 filter
FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods
Xiaotian Han, Jianfeng Chi, Yu Chen +4
This paper introduces the Fair Fairness Benchmark (\textsf{FFB}), a benchmarking framework for in-processing group fairness methods. Ensuring fairness in machine learning is import…
Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs
Costas Mavromatis, Vassilis N. Ioannidis, Shen Wang +6
How can we learn effective node representations on textual graphs? Graph Neural Networks (GNNs) that use Language Models (LMs) to encode textual information of graphs achieve state…
Greedy Modality Selection via Approximate Submodular Maximization
Runxiang Cheng, Gargi Balasubramaniam, Yifei He +2
Multimodal learning considers learning from multi-modality data, aiming to fuse heterogeneous sources of information. However, it is not always feasible to leverage all available m…
Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond
Haoxiang Wang, Bo Li, Han Zhao
The vast majority of existing algorithms for unsupervised domain adaptation (UDA) focus on adapting from a labeled source domain to an unlabeled target domain directly in a one-off…
Conditional Contrastive Learning with Kernel
Yao-Hung Hubert Tsai, Tianqin Li, Martin Q. Ma +4
Conditional contrastive learning frameworks consider the conditional sampling procedure that constructs positive or negative data pairs conditioned on specific variables. Fair cont…
Provable Domain Generalization via Invariant-Feature Subspace Recovery
Haoxiang Wang, Haozhe Si, Bo Li +1
Domain generalization asks for models trained over a set of training environments to perform well in unseen test environments. Recently, a series of algorithms such as Invariant Ri…
FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptation
Yan Shen, Jian Du, Han Zhao +3
Federated adversary domain adaptation is a unique distributed minimax training task due to the prevalence of label imbalance among clients, with each client only seeing a subset of…
Towards Return Parity in Markov Decision Processes
Jianfeng Chi, Jian Shen, Xinyi Dai +3
Algorithmic decisions made by machine learning models in high-stakes domains may have lasting impacts over time. However, naive applications of standard fairness criterion in stati…
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation
Haoxiang Wang, Han Zhao, Bo Li
Multi-task learning (MTL) aims to improve the generalization of several related tasks by learning them jointly. As a comparison, in addition to the joint training scheme, modern me…
Costs and Benefits of Fair Regression
Han Zhao
Real-world applications of machine learning tools in high-stakes domains are often regulated to be fair, in the sense that the predicted target should satisfy some quantitative not…
Invariant Information Bottleneck for Domain Generalization
Bo Li, Yifei Shen, Yezhen Wang +6
Invariant risk minimization (IRM) has recently emerged as a promising alternative for domain generalization. Nevertheless, the loss function is difficult to optimize for nonlinear…
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…
Conditional Contrastive Learning for Improving Fairness in Self-Supervised Learning
Martin Q. Ma, Yao-Hung Hubert Tsai, Paul Pu Liang +4
Contrastive self-supervised learning (SSL) learns an embedding space that maps similar data pairs closer and dissimilar data pairs farther apart. Despite its success, one issue has…
Self-supervised Representation Learning with Relative Predictive Coding
Yao-Hung Hubert Tsai, Martin Q. Ma, Muqiao Yang +3
This paper introduces Relative Predictive Coding (RPC), a new contrastive representation learning objective that maintains a good balance among training stability, minibatch size s…
Understanding and Mitigating Accuracy Disparity in Regression
Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon +1
With the widespread deployment of large-scale prediction systems in high-stakes domains, e.g., face recognition, criminal justice, etc., disparity in prediction accuracy between di…
Fundamental Limits and Tradeoffs in Invariant Representation Learning
Han Zhao, Chen Dan, Bryon Aragam +3
A wide range of machine learning applications such as privacy-preserving learning, algorithmic fairness, and domain adaptation/generalization among others, involve learning invaria…
Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation
Bo Li, Yezhen Wang, Shanghang Zhang +4
The success of supervised learning hinges on the assumption that the training and test data come from the same underlying distribution, which is often not valid in practice due to…
Information Obfuscation of Graph Neural Networks
Peiyuan Liao, Han Zhao, Keyulu Xu +4
While the advent of Graph Neural Networks (GNNs) has greatly improved node and graph representation learning in many applications, the neighborhood aggregation scheme exposes addit…
Neural Methods for Point-wise Dependency Estimation
Yao-Hung Hubert Tsai, Han Zhao, Makoto Yamada +2
Since its inception, the neural estimation of mutual information (MI) has demonstrated the empirical success of modeling expected dependency between high-dimensional random variabl…
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift
Remi Tachet, Han Zhao, Yu-Xiang Wang +1
Adversarial learning has demonstrated good performance in the unsupervised domain adaptation setting, by learning domain-invariant representations. However, recent work has shown l…
Conditional Learning of Fair Representations
Han Zhao, Amanda Coston, Tameem Adel +1
We propose a novel algorithm for learning fair representations that can simultaneously mitigate two notions of disparity among different demographic subgroups in the classification…
Learning Neural Networks with Adaptive Regularization
Han Zhao, Yao-Hung Hubert Tsai, Ruslan Salakhutdinov +1
Feed-forward neural networks can be understood as a combination of an intermediate representation and a linear hypothesis. While most previous works aim to diversify the representa…
Inherent Tradeoffs in Learning Fair Representations
Han Zhao, Geoffrey J. Gordon
Real-world applications of machine learning tools in high-stakes domains are often regulated to be fair, in the sense that the predicted target should satisfy some quantitative not…
Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation
Han Zhao, Jianfeng Chi, Yuan Tian +1
Crowdsourced data used in machine learning services might carry sensitive information about attributes that users do not want to share. Various methods have been proposed to minimi…