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Chuan Sheng Foo

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8 papers

1 author row

NeurIPS Conference 2025 Conference Paper

Incentivizing Time-Aware Fairness in Data Sharing

Jiangwei Chen
Kieu Thao Nguyen Pham
Rachael Sim
Arun Verma
Zhaoxuan Wu
Chuan Sheng Foo
Bryan Kian Hsiang Low

In collaborative data sharing and machine learning, multiple parties aggregate their data resources to train a machine learning model with better model performance. However, as the parties incur data collection costs, they are only willing to do so when guaranteed incentives, such as fairness and individual rationality. Existing frameworks assume that all parties join the collaboration simultaneously, which does not hold in many real-world scenarios. Due to the long processing time for data cleaning, difficulty in overcoming legal barriers, or unawareness, the parties may join the collaboration at different times. In this work, we propose the following perspective: As a party who joins earlier incurs higher risk and encourages the contribution from other wait-and-see parties, that party should receive a reward of higher value for sharing data earlier. To this end, we propose a fair and time-aware data sharing framework, including novel time-aware incentives. We develop new methods for deciding reward values to satisfy these incentives. We further illustrate how to generate model rewards that realize the reward values and empirically demonstrate the properties of our methods on synthetic and real-world datasets.

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AAAI Conference 2024 Conference Paper

Fine Structure-Aware Sampling: A New Sampling Training Scheme for Pixel-Aligned Implicit Models in Single-View Human Reconstruction

Kennard Yanting Chan
Fayao Liu
Guosheng Lin
Chuan Sheng Foo
Weisi Lin

Pixel-aligned implicit models, such as PIFu, PIFuHD, and ICON, are used for single-view clothed human reconstruction. These models need to be trained using a sampling training scheme. Existing sampling training schemes either fail to capture thin surfaces (e.g. ears, fingers) or cause noisy artefacts in reconstructed meshes. To address these problems, we introduce Fine Structured-Aware Sampling (FSS), a new sampling training scheme to train pixel-aligned implicit models for single-view human reconstruction. FSS resolves the aforementioned problems by proactively adapting to the thickness and complexity of surfaces. In addition, unlike existing sampling training schemes, FSS shows how normals of sample points can be capitalized in the training process to improve results. Lastly, to further improve the training process, FSS proposes a mesh thickness loss signal for pixel-aligned implicit models. It becomes computationally feasible to introduce this loss once a slight reworking of the pixel-aligned implicit function framework is carried out. Our results show that our methods significantly outperform SOTA methods qualitatively and quantitatively. Our code is publicly available at https://github.com/kcyt/FSS.

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NeurIPS Conference 2023 Conference Paper

Bayesian Optimization with Cost-varying Variable Subsets

Sebastian Tay
Chuan Sheng Foo
Daisuke Urano
Richalynn Leong
Bryan Kian Hsiang Low

We introduce the problem of Bayesian optimization with cost-varying variable subsets (BOCVS) where in each iteration, the learner chooses a subset of query variables and specifies their values while the rest are randomly sampled. Each chosen subset has an associated cost. This presents the learner with the novel challenge of balancing between choosing more informative subsets for more directed learning versus leaving some variables to be randomly sampled to reduce incurred costs. This paper presents a novel Gaussian process upper confidence bound-based algorithm for solving the BOCVS problem that is provably no-regret. We analyze how the availability of cheaper control sets helps in exploration and reduces overall regret. We empirically show that our proposed algorithm can find significantly better solutions than comparable baselines with the same budget.

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NeurIPS Conference 2023 Conference Paper

Model Shapley: Equitable Model Valuation with Black-box Access

Xinyi Xu
Thanh Lam
Chuan Sheng Foo
Bryan Kian Hsiang Low

Valuation methods of data and machine learning (ML) models are essential to the establishment of AI marketplaces. Importantly, certain practical considerations (e. g. , operational constraints, legal restrictions) favor the use of model valuation over data valuation. Also, existing marketplaces that involve trading of pre-trained ML models call for an equitable model valuation method to price them. In particular, we investigate the black-box access setting which allows querying a model (to observe predictions) without disclosing model-specific information (e. g. , architecture and parameters). By exploiting a Dirichlet abstraction of a model’s predictions, we propose a novel and equitable model valuation method called model Shapley. We also leverage a Lipschitz continuity of model Shapley to design a learning approach for predicting the model Shapley values (MSVs) of many vendors’ models (e. g. , 150) in a large-scale marketplace. We perform extensive empirical validation on the effectiveness of model Shapley using various real-world datasets and heterogeneous model types.

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AAAI Conference 2023 Conference Paper

Probably Approximate Shapley Fairness with Applications in Machine Learning

Zijian Zhou
Xinyi Xu
Rachael Hwee Ling Sim
Chuan Sheng Foo
Bryan Kian Hsiang Low

The Shapley value (SV) is adopted in various scenarios in machine learning (ML), including data valuation, agent valuation, and feature attribution, as it satisfies their fairness requirements. However, as exact SVs are infeasible to compute in practice, SV estimates are approximated instead. This approximation step raises an important question: do the SV estimates preserve the fairness guarantees of exact SVs? We observe that the fairness guarantees of exact SVs are too restrictive for SV estimates. Thus, we generalise Shapley fairness to probably approximate Shapley fairness and propose fidelity score, a metric to measure the variation of SV estimates, that determines how probable the fairness guarantees hold. Our last theoretical contribution is a novel greedy active estimation (GAE) algorithm that will maximise the lowest fidelity score and achieve a better fairness guarantee than the de facto Monte-Carlo estimation. We empirically verify GAE outperforms several existing methods in guaranteeing fairness while remaining competitive in estimation accuracy in various ML scenarios using real-world datasets.

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AAAI Conference 2022 Conference Paper

Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards

Sebastian Shenghong Tay
Xinyi Xu
Chuan Sheng Foo
Bryan Kian Hsiang Low

This paper presents a novel collaborative generative modeling (CGM) framework that incentivizes collaboration among self-interested parties to contribute data to a pool for training a generative model (e. g. , GAN), from which synthetic data are drawn and distributed to the parties as rewards commensurate to their contributions. Distributing synthetic data as rewards (instead of trained models or money) offers taskand model-agnostic benefits for downstream learning tasks and is less likely to violate data privacy regulation. To realize the framework, we firstly propose a data valuation function using maximum mean discrepancy (MMD) that values data based on its quantity and quality in terms of its closeness to the true data distribution and provide theoretical results guiding the kernel choice in our MMD-based data valuation function. Then, we formulate the reward scheme as a linear optimization problem that when solved, guarantees certain incentives such as fairness in the CGM framework. We devise a weighted sampling algorithm for generating synthetic data to be distributed to each party as reward such that the value of its data and the synthetic data combined matches its assigned reward value by the reward scheme. We empirically show using simulated and real-world datasets that the parties’ synthetic data rewards are commensurate to their contributions.

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NeurIPS Conference 2021 Conference Paper

Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning

Xinyi Xu
Lingjuan Lyu
Xingjun Ma
Chenglin Miao
Chuan Sheng Foo
Bryan Kian Hsiang Low

In collaborative machine learning(CML), multiple agents pool their resources(e. g. , data) together for a common learning task. In realistic CML settings where the agents are self-interested and not altruistic, they may be unwilling to share data or model information without adequate rewards. Furthermore, as the data/model information shared by the agents may differ in quality, designing rewards which are fair to them is important so that they would not feel exploited nor discouraged from sharing. In this paper, we adopt federated learning as the CML paradigm, propose a novel cosine gradient Shapley value(CGSV) to fairly evaluate the expected marginal contribution of each agent’s uploaded model parameter update/gradient without needing an auxiliary validation dataset, and based on the CGSV, design a novel training-time gradient reward mechanism with a fairness guarantee by sparsifying the aggregated parameter update/gradient downloaded from the server as reward to each agent such that its resulting quality is commensurate to that of the agent’s uploaded parameter update/gradient. We empirically demonstrate the effectiveness of our fair gradient reward mechanism on multiple benchmark datasets in terms of fairness, predictive performance, and time overhead.

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NeurIPS Conference 2021 Conference Paper

Validation Free and Replication Robust Volume-based Data Valuation

Xinyi Xu
Zhaoxuan Wu
Chuan Sheng Foo
Bryan Kian Hsiang Low

Data valuation arises as a non-trivial challenge in real-world use cases such as collaborative machine learning, federated learning, trusted data sharing, data marketplaces. The value of data is often associated with the learning performance (e. g. , validation accuracy) of a model trained on the data, which introduces a close coupling between data valuation and validation. However, a validation set may notbe available in practice and it can be challenging for the data providers to reach an agreement on the choice of the validation set. Another practical issue is that of data replication: Given the value of some data points, a dishonest data provider may replicate these data points to exploit the valuation for a larger reward/payment. We observe that the diversity of the data points is an inherent property of a dataset that is independent of validation. We formalize diversity via the volume of the data matrix (i. e. , determinant of its left Gram), which allows us to establish a formal connection between the diversity of data and learning performance without requiring validation. Furthermore, we propose a robust volume measure with a theoretical guarantee on the replication robustness by following the intuition that copying the same data points does not increase the diversity of data. We perform extensive experiments to demonstrate its consistency in valuation and practical advantages over existing baselines and show that our method is model- and task-agnostic and can be flexibly adapted to handle various neural networks.

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