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Works72 from public data
- 141
- A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data95
This paper presents a novel unifying framework of anytime sparse Gaussian process regression (SGPR) models that can produce good predictive performance fast and improve their predictive performance over time and empirically evaluates the trade-off between the predictive performance vs. time efficiency of the anytime SGPR models on two real-world million-sized datasets.
- Verifiably safe exploration for end-to-end reinforcement learning67
A first approach toward enforcing formal safety constraints on end-to-end policies with visual inputs is contributed, drawing on recent advances in object detection and automated reasoning for hybrid dynamical systems.
- A distributed variational inference framework for unifying parallel sparse Gaussian process regression models60
A novel distributed variational inference framework that unifies many parallel sparse Gaussian process regression (SGPR) models for scalable hyperparameter learning with big data and empirically evaluates the predictive performance and scalability of the distributedvariational SGPR models unified by this framework on two real-world datasets.
- Decentralized High-Dimensional Bayesian Optimization With Factor Graphs59
A novel decentralized high-dimensional Bayesian optimization algorithm that 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 dimension of the input space is presented.
- Active Learning Is Planning: Nonmyopic ε-Bayes-Optimal Active Learning of Gaussian Processes50
This paper presents a novel nonmyopic e-Bayes-optimal active learning (e-BAL) approach that jointly optimizes the exploration-exploitation trade-off and proposes an anytime algorithm based on eBAL with performance guarantee and empirically demonstrates that it outperforms the state-of-the-art algorithms.
- Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems49
A rigorous theoretical analysis of the approximation loss arising from the proposed representation to achieve efficient online updates and model fusion and shows that COOL-GP is highly effective in model fusion, resilient to information disparity between agents, robust to transmission loss, and can scale to thousands of agents.
- Near-Optimal Active Learning of Multi-Output Gaussian Processes42
This paper exploits a structure common to sparse MOGP models for deriving a novel active learning criterion and exploits a relaxed form of submodularity property of the new criterion for devising a polynomial-time approximation algorithm that guarantees a constant-factor approximation of that achieved by the optimal set of selected observations.
- A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression40
The generalized framework of stochastic variational Bayesian sparse spectrum GP (sVBSSGP) models addresses their shortcomings by adopting a Bayesian treatment of the spectral frequencies to avoid overfitting, and exploiting local data for boosting the predictive performance.
- Nonmyopic \epsilon-Bayes-Optimal Active Learning of Gaussian Processes38
To perform active learning in real time, this paper proposes an anytime algorithm based on e-BAL with performance guarantee and empirically demonstrate that, with limited budget, it outperforms the state-of-the-art algorithms.
- DDL: Deep Dictionary Learning for Predictive Phenotyping35
A deep dictionary learning framework (DDL) for phenotyping is proposed, which utilizes unlabeled data as a complementary source of information to generate a better, more succinct data representation that helps improve DDL’s phenotyping performance over existing methods that only uses labeled data.
- Collective Model Fusion for Multiple Black-Box Experts35
This paper presents the first collective model fusion framework for multiple experts with heterogeneous black-box architectures, addressing the key issues of how black- box experts interact to understand the predictive behaviors of one another and how the shared understandings can be combined to generate high-quality consensus prediction.
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- Offline Model-Based Optimization via Policy-Guided Gradient Search25
This work introduces a new learning-to-search perspective for offline optimization by reformulating it as an offline reinforcement learning problem and explicitly learns the best policy for a given surrogate model created from the offline data.
- Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion25
A new fusion paradigm is developed that represents each expert as a distribution over a spectrum of predictive prototypes, which are isolated from task-specific information encoded within the prototype distribution and can then be reintegrated to generate a new model that solves a new task encoded with a different prototype distribution.
- Recent Advances in Scaling Up Gaussian Process Predictive Models for Large Spatiotemporal Data22
An overview of the recent progress in scaling up GP models for large spatiotemporally correlated data through parallelization on clusters of machines, online learning, and nonmyopic active sensing/learning is presented.
- Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data19
This work transforms federated learning into a distributed set modeling task, aggregating diverse sets of prompts to globally fine-tune the pre-trained model, and introduces a new probabilistic prompt aggregation method that substantially outperforms existing federated model aggregation techniques.
- Scalable Decision-Theoretic Coordination and Control for Real-time Active Multi-Camera Surveillance18
An overview of the novel decision-theoretic multi-agent approach for controlling and coordinating multiple active cameras in surveillance, which focuses on two novel surveillance tasks: maximize the number of targets observed in active cameras with guaranteed image resolution and to improve the fairness in observation of multiple targets.
- AID: Active Distillation Machine to Leverage Pre-Trained Black-Box Models in Private Data Settings13
This paper presents an active distillation method for a local institution to find the best queries within its given budget to distill an on-server black-box model’s predictive knowledge into a local surrogate with transparent parameterization, which addresses several challenges of deploying machine learning in many industrial settings with strong proprietary constraints.
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- Boosting Offline Optimizers with Surrogate Sensitivity12
An optimizable sensitivity measurement is developed for the surrogate model, which inspires a sensitivity-informed regularizer that is applicable to a wide range of offline optimizers and is demonstrated in the extensive experiment benchmark.
- Incentives in Private Collaborative Machine Learning12
This work introduces differential privacy (DP) as an incentive, which enforces a privacy-valuation trade-off, and rewards each party with different posterior samples of the model parameters.
- Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems11
The approach first optimizes a set of stratagems that represent best responses to detect changes in adversary strategies and then integrated into a unified policy that can detect and respond when the adversaries change their strategies.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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