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Found 13 papers, 3 papers with code
Incentives in Private Collaborative Machine Learning
Finally, the mediator rewards each party with different posterior samples of the model parameters.
COPR: Continual Human Preference Learning via Optimal Policy Regularization
Reinforcement Learning from Human Feedback (RLHF) is commonly utilized to improve the alignment of Large Language Models (LLMs) with human preferences.
SecFormer: Towards Fast and Accurate Privacy-Preserving Inference for Large Language Models
However, the application of SMPC in Privacy-Preserving Inference (PPI) for large language models, particularly those based on the Transformer architecture, often leads to considerable slowdowns or declines in performance.
EncryIP: A Practical Encryption-Based Framework for Model Intellectual Property Protection
In the rapidly growing digital economy, protecting intellectual property (IP) associated with digital products has become increasingly important.
Model Provenance via Model DNA
Understanding the life cycle of the machine learning (ML) model is an intriguing area of research (e. g., understanding where the model comes from, how it is trained, and how it is used).
Dependency Structure Search Bayesian Optimization for Decision Making Models
Derivative-free approaches such as Bayesian Optimization mitigate the dependency on the quality of gradient feedback, but are known to scale poorly in the high-dimension setting of complex decision making models.
Practical Privacy-Preserving Gaussian Process Regression via Secret Sharing
In contrast to existing studies that protect the data privacy of GPR via homomorphic encryption, differential privacy, or federated learning, our proposed method is more practical and can be used to preserve the data privacy of both the model inputs and outputs for various data-sharing scenarios (e. g., horizontally/vertically-partitioned data).
PanGu-$α$: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation
To enhance the generalization ability of PanGu-$\alpha$, we collect 1. 1TB high-quality Chinese data from a wide range of domains to pretrain the model.
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Balancing training time vs. performance with Bayesian Early Pruning
Pruning is an approach to alleviate overparameterization of deep neural networks (DNN) by zeroing out or pruning DNN elements with little to no efficacy at a given task.
Collaborative Machine Learning with Incentive-Aware Model Rewards
This paper proposes to value a party's reward based on Shapley value and information gain on model parameters given its data.
Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression
To achieve this, we represent the probabilistic kernel as an additional variational variable in a variational inference (VI) framework for SGPR models where its posterior belief is learned together with that of the other variational variables (i. e., inducing variables and kernel hyperparameters).
Bayesian Optimization with Binary Auxiliary Information
This paper presents novel mixed-type Bayesian optimization (BO) algorithms to accelerate the optimization of a target objective function by exploiting correlated auxiliary information of binary type that can be more cheaply obtained, such as in policy search for reinforcement learning and hyperparameter tuning of machine learning models with early stopping.
Near-Optimal Active Learning of Multi-Output Gaussian Processes
This paper addresses the problem of active learning of a multi-output Gaussian process (MOGP) model representing multiple types of coexisting correlated environmental phenomena.
