research
∙
06/12/2023
Provably Efficient Bayesian Optimization with Unbiased Gaussian Process Hyperparameter Estimation
Gaussian process (GP) based Bayesian optimization (BO) is a powerful met...
share
research
∙
03/03/2023
Zero-shot Object Counting
Class-agnostic object counting aims to count object instances of an arbi...
research
∙
07/20/2022
Sampling type method combined with deep learning for inverse scattering with one incident wave
We consider the inverse problem of determining the geometry of penetrabl...
research
∙
07/19/2022
Bayesian Generational Population-Based Training
Reinforcement learning (RL) offers the potential for training generally ...
research
∙
06/13/2022
Confident Sinkhorn Allocation for Pseudo-Labeling
Semi-supervised learning is a critical tool in reducing machine learning...
research
∙
03/04/2022
Distributionally Robust Bayesian Optimization with φ-divergences
The study of robustness has received much attention due to its inevitabi...
research
∙
02/22/2022
Retrieval Augmented Classification for Long-Tail Visual Recognition
We introduce Retrieval Augmented Classification (RAC), a generic approac...
research
∙
01/11/2022
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems
The combination of Reinforcement Learning (RL) with deep learning has le...
research
∙
10/22/2021
Gaussian Process Sampling and Optimization with Approximate Upper and Lower Bounds
Many functions have approximately-known upper and/or lower bounds, poten...
research
∙
09/11/2021
Bayesian Topic Regression for Causal Inference
Causal inference using observational text data is becoming increasingly ...
research
∙
06/30/2021
Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL
Despite a series of recent successes in reinforcement learning (RL), man...
research
∙
02/14/2021
Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces
High-dimensional black-box optimisation remains an important yet notorio...
research
∙
10/29/2020
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective
Achieving the full promise of the Thermodynamic Variational Objective (T...
research
∙
06/14/2020
Controlling Quantum Device Measurement using Deep Reinforcement Learning
Qubits based on semiconductor quantum dot devices are promising building...
research
∙
06/13/2020
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search
Neural architecture search (NAS) automates the design of deep neural net...
research
∙
02/26/2020
Incorporating Expert Prior Knowledge into Experimental Design via Posterior Sampling
Scientific experiments are usually expensive due to complex experimental...
research
∙
02/06/2020
One-Shot Bayes Opt with Probabilistic Population Based Training
Selecting optimal hyperparameters is a key challenge in machine learning...
research
∙
12/04/2019
Indian Buffet Neural Networks for Continual Learning
We place an Indian Buffet Process (IBP) prior over the neural structure ...
research
∙
09/20/2019
Bayesian Optimization for Iterative Learning
The success of deep (reinforcement) learning systems crucially depends o...
research
∙
07/22/2019
Accelerating Experimental Design by Incorporating Experimenter Hunches
Experimental design is a process of obtaining a product with target prop...
research
∙
06/20/2019
Bayesian Optimisation over Multiple Continuous and Categorical Inputs
Efficient optimisation of black-box problems that comprise both continuo...
research
∙
05/07/2019
Knowing The What But Not The Where in Bayesian Optimization
Bayesian optimization has demonstrated impressive success in finding the...
research
∙
11/05/2018
Practical Batch Bayesian Optimization for Less Expensive Functions
Bayesian optimization (BO) and its batch extensions are successful for o...
research
∙
02/15/2018
High Dimensional Bayesian Optimization Using Dropout
Scaling Bayesian optimization to high dimensions is challenging task as ...
research
∙
12/04/2017
A+D-Net: Shadow Detection with Adversarial Shadow Attenuation
Single image shadow detection is a very challenging problem because of t...
research
∙
04/03/2017
Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images
Histopathology images are crucial to the study of complex diseases such ...
research
∙
03/31/2017
Geodesic Distance Histogram Feature for Video Segmentation
This paper proposes a geodesic-distance-based feature that encodes globa...
research
∙
04/22/2016
Approximation Vector Machines for Large-scale Online Learning
One of the most challenging problems in kernel online learning is to bou...
research
∙
01/09/2014
