Search Results for author:
Found 15 papers, 6 papers with code
Principled Preferential Bayesian Optimization
We study the problem of preferential Bayesian optimization (BO), where we aim to optimize a black-box function with only preference feedback over a pair of candidate solutions.
Multi-Agent Bayesian Optimization with Coupled Black-Box and Affine Constraints
Additionally, the algorithm guarantees an $\mathcal{O}(N\sqrt{T})$ bound on the cumulative violation for the known affine constraints, where $N$ is the number of agents.
Data-driven adaptive building thermal controller tuning with constraints: A primal-dual contextual Bayesian optimization approach
We study the problem of tuning the parameters of a room temperature controller to minimize its energy consumption, subject to the constraint that the daily cumulative thermal discomfort of the occupants is below a given threshold.
Bayesian Optimization of Expensive Nested Grey-Box Functions
We consider the problem of optimizing a grey-box objective function, i. e., nested function composed of both black-box and white-box functions.
Pre-trained Language Model with Prompts for Temporal Knowledge Graph Completion
We train our model with a masking strategy to convert TKGC task into a masked token prediction task, which can leverage the semantic information in pre-trained language models.
Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraints
This paper studies the problem of online performance optimization of constrained closed-loop control systems, where both the objective and the constraints are unknown black-box functions affected by exogenous time-varying contextual disturbances.
Violation-Aware Contextual Bayesian Optimization for Controller Performance Optimization with Unmodeled Constraints
We study the problem of performance optimization of closed-loop control systems with unmodeled dynamics.
CONFIG: Constrained Efficient Global Optimization for Closed-Loop Control System Optimization with Unmodeled Constraints
In this paper, the CONFIG algorithm, a simple and provably efficient constrained global optimization algorithm, is applied to optimize the closed-loop control performance of an unknown system with unmodeled constraints.
SMiLE: Schema-augmented Multi-level Contrastive Learning for Knowledge Graph Link Prediction
Specifically, we first exploit network schema as the prior constraint to sample negatives and pre-train our model by employing a multi-level contrastive learning method to yield both prior schema and contextual information.
Optimizing Two-Truck Platooning with Deadlines
The FPTAS can achieve a fuel consumption within a ratio of $(1+\epsilon)$ to the optimal (for any $\epsilon>0$) with a time complexity polynomial in the size of the transportation network and $1/\epsilon$.
Lower Bounds on the Worst-Case Complexity of Efficient Global Optimization
In this paper, we study the worst-case complexity of the efficient global optimization problem and, in contrast to existing kernel-specific results, we derive a unified lower bound for the complexity of efficient global optimization in terms of the metric entropy of a ball in its corresponding reproducing kernel Hilbert space~(RKHS).
PointSCNet: Point Cloud Structure and Correlation Learning Based on Space Filling Curve-Guided Sampling
This paper proposes a point cloud feature extraction network named PointSCNet, to capture the geometrical structure information and local region correlation information of a point cloud.
Ranked #40 on
3D Point Cloud Classification
on ModelNet40
VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints
We study the problem of performance optimization of closed-loop control systems with unmodeled dynamics.
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.
Recurrent Neural Networks are Universal Filters
Another type of popular neural network, deep (feed-forward) neural network has also been successfully applied in different engineering disciplines, whose approximation capability has been well characterized by universal approxi- mation theorem (Hornik et al. (1989), Park & Sandberg (1991), Lu et al. (2017)).
