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Found 22 papers, 11 papers with code
Model Fusion through Bayesian Optimization in Language Model Fine-Tuning
Building on this observation, we introduce a novel model fusion technique that optimizes both the desired metric and loss through multi-objective Bayesian optimization.
Exploiting Preferences in Loss Functions for Sequential Recommendation via Weak Transitivity
A choice of optimization objective is immensely pivotal in the design of a recommender system as it affects the general modeling process of a user's intent from previous interactions.
Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimization
First, we propose a novel confidence set that is `semi-adaptive' to the unknown sub-Gaussian parameter $\sigma_*^2$ in the sense that the (normalized) confidence width scales with $\sqrt{d\sigma_*^2 + \sigma_0^2}$ where $d$ is the dimension and $\sigma_0^2$ is the specified sub-Gaussian parameter (known) that can be much larger than $\sigma_*^2$.
Beyond Regrets: Geometric Metrics for Bayesian Optimization
These metrics only rely on function evaluations, so that they do not consider geometric relationships between query points and global solutions, or query points themselves.
Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations
To design and understand these nanophotonic structures, electrodynamic simulations are essential.
Generalized Neural Sorting Networks with Error-Free Differentiable Swap Functions
Sorting is a fundamental operation of all computer systems, having been a long-standing significant research topic.
Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning
Beyond the probabilistic regression-based methods, density ratio estimation-based Bayesian optimization has been suggested in order to estimate a density ratio of the groups relatively close and relatively far to a global optimum.
Budget-Aware Sequential Brick Assembly with Efficient Constraint Satisfaction
To solve this problem, we propose a new method to predict the scores of the next brick position by employing a U-shaped sparse 3D convolutional neural network.
Learning to Assemble Geometric Shapes
Assembling parts into an object is a combinatorial problem that arises in a variety of contexts in the real world and involves numerous applications in science and engineering.
On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization
Sequential model-based optimization sequentially selects a candidate point by constructing a surrogate model with the history of evaluations, to solve a black-box optimization problem.
On Evaluation Metrics for Graph Generative Models
While we focus on applying these metrics to GGM evaluation, in practice this enables the ability to easily compute the dissimilarity between any two sets of graphs regardless of domain.
Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning
Discovering a solution in a combinatorial space is prevalent in many real-world problems but it is also challenging due to diverse complex constraints and the vast number of possible combinations.
Combinatorial Bayesian Optimization with Random Mapping Functions to Convex Polytopes
The main idea is to use a random mapping which embeds the combinatorial space into a convex polytope in a continuous space, on which all essential process is performed to determine a solution to the black-box optimization in the combinatorial space.
Fragment Relation Networks for Geometric Shape Assembly
Our model processes the candidate fragments in a permutation-equivariant manner and can generalize to cases with an arbitrary number of fragments and even with a different target object.
Bootstrapping Neural Processes
While this "data-driven" way of learning stochastic processes has proven to handle various types of data, NPs still rely on an assumption that uncertainty in stochastic processes is modeled by a single latent variable, which potentially limits the flexibility.
Combinatorial 3D Shape Generation via Sequential Assembly
To alleviate this consequence induced by a huge number of feasible combinations, we propose a combinatorial 3D shape generation framework.
Bayesian Optimization with Approximate Set Kernels
We propose a practical Bayesian optimization method over sets, to minimize a black-box function that takes a set as a single input.
Practical Bayesian Optimization with Threshold-Guided Marginal Likelihood Maximization
We propose a practical Bayesian optimization method using Gaussian process regression, of which the marginal likelihood is maximized where the number of model selection steps is guided by a pre-defined threshold.
MxML: Mixture of Meta-Learners for Few-Shot Classification
A meta-model is trained on a distribution of similar tasks such that it learns an algorithm that can quickly adapt to a novel task with only a handful of labeled examples.
On Local Optimizers of Acquisition Functions in Bayesian Optimization
In practice, however, local optimizers of an acquisition function are also used, since searching for the global optimizer is often a non-trivial or time-consuming task.
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instances.
Learning to Warm-Start Bayesian Hyperparameter Optimization
A simple alternative of manual search is random/grid search on a space of hyperparameters, which still undergoes extensive evaluations of validation errors in order to find its best configuration.
