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Found 24 papers, 11 papers with code
Incentives in Private Collaborative Machine Learning
Finally, the mediator rewards each party with different posterior samples of the model parameters.
Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches
The second approach introduces a post-processing method EsbRS which greatly improves the robustness certificate based on building model ensembles.
Time-to-Pattern: Information-Theoretic Unsupervised Learning for Scalable Time Series Summarization
Data summarization is the process of generating interpretable and representative subsets from a dataset.
Personalized Federated Domain Adaptation for Item-to-Item Recommendation
To mitigate this data bottleneck, we postulate that recommendation patterns learned from existing mature market segments (with private data) could be adapted to build effective warm-start models for emerging ones.
Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs
Second, we need to learn a consensus graph that captures the high-order interactions between local feature spaces and how to combine them to achieve a better prediction.
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms
This work studies the threats of adversarial attack on multivariate probabilistic forecasting models and viable defense mechanisms.
Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback
We develop and investigate a personalizable deep metric model that captures both the internal contents of items and how they were interacted with by users.
Federated Inference through Aligning Local Representations and Learning a Consensus Graph
On the top is a federation of the local data representations, performing global inference that incorporates all distributed parts collectively.
Federated Estimation of Causal Effects from Observational Data
Many modern applications collect data that comes in federated spirit, with data kept locally and undisclosed.
Adaptive Multi-Source Causal Inference
The proposed method can infer causal effects in the target population without prior knowledge of data discrepancy between the additional data sources and the target.
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes
We introduce a new scalable approximation for Gaussian processes with provable guarantees which hold simultaneously over its entire parameter space.
CHEER: Rich Model Helps Poor Model via Knowledge Infusion
There is a growing interest in applying deep learning (DL) to healthcare, driven by the availability of data with multiple feature channels in rich-data environments (e. g., intensive care units).
CASTER: Predicting Drug Interactions with Chemical Substructure Representation
Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality.
Statistical Model Aggregation via Parameter Matching
We consider the problem of aggregating models learned from sequestered, possibly heterogeneous datasets.
Bayesian Nonparametric Federated Learning of Neural Networks
In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited.
RDPD: Rich Data Helps Poor Data via Imitation
In many situations, we need to build and deploy separate models in related environments with different data qualities.
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems
Distributed machine learning (ML) is a modern computation paradigm that divides its workload into independent tasks that can be simultaneously achieved by multiple machines (i. e., agents) for better scalability.
Decentralized High-Dimensional Bayesian Optimization with Factor Graphs
This paper presents a novel decentralized high-dimensional Bayesian optimization (DEC-HBO) algorithm that, in contrast to existing HBO algorithms, can exploit the interdependent effects of various input components on the output of the unknown objective function f for boosting the BO performance and still preserve scalability in the number of input dimensions without requiring prior knowledge or the existence of a low (effective) dimension of the input space.
Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression
This paper presents a novel variational inference framework for deriving a family of Bayesian sparse Gaussian process regression (SGPR) models whose approximations are variationally optimal with respect to the full-rank GPR model enriched with various corresponding correlation structures of the observation noises.
Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems
The practicality of existing works addressing this challenge is limited to only small-scale synchronous decision-making scenarios or a single agent planning its best response against a single adversary with fixed, procedurally characterized strategies.
A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression
While much research effort has been dedicated to scaling up sparse Gaussian process (GP) models based on inducing variables for big data, little attention is afforded to the other less explored class of low-rank GP approximations that exploit the sparse spectral representation of a GP kernel.
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.
Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agents
A key challenge in non-cooperative multi-agent systems is that of developing efficient planning algorithms for intelligent agents to interact and perform effectively among boundedly rational, self-interested agents (e. g., humans).
A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior
Recent advances in Bayesian reinforcement learning (BRL) have shown that Bayes-optimality is theoretically achievable by modeling the environment's latent dynamics using Flat-Dirichlet-Multinomial (FDM) prior.
Multi-agent Reinforcement Learning
reinforcement-learning
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