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Found 13 papers, 4 papers with code
A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps
In order to deploy machine learning in a real-world self-driving laboratory where data acquisition is costly and there are multiple competing design criteria, systems need to be able to intelligently sample while balancing performance trade-offs and constraints.
Numerical evidence against advantage with quantum fidelity kernels on classical data
We show that the general-purpose hyperparameter tuning techniques proposed to improve the generalization of quantum kernels lead to the kernel becoming well-approximated by a classical kernel, removing the possibility of quantum advantage.
Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
For the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets (from SDSS and DECaLS).
Bandwidth Enables Generalization in Quantum Kernel Models
Quantum computers are known to provide speedups over classical state-of-the-art machine learning methods in some specialized settings.
DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification
On the other hand, we show that training with domain adaptation improves model robustness and mitigates the effects of these perturbations, improving the classification accuracy by 23% on data with higher observational noise.
Importance of Kernel Bandwidth in Quantum Machine Learning
Quantum kernel methods are considered a promising avenue for applying quantum computers to machine learning problems.
Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic Optimization
We consider unconstrained stochastic optimization problems with no available gradient information.
Randomized Algorithms for Scientific Computing (RASC)
Randomized algorithms have propelled advances in artificial intelligence and represent a foundational research area in advancing AI for Science.
Scalable Statistical Inference of Photometric Redshift via Data Subsampling
Handling big data has largely been a major bottleneck in traditional statistical models.
Exploiting Symmetry Reduces the Cost of Training QAOA
We show how by considering only the terms that are not connected by symmetry, we can significantly reduce the cost of evaluating the QAOA energy.
Quantum Physics
Adaptive Sampling Quasi-Newton Methods for Derivative-Free Stochastic Optimization
We consider stochastic zero-order optimization problems, which arise in settings from simulation optimization to reinforcement learning.
Sequential Learning of Active Subspaces
In such cases, often a surrogate model is employed, on which finite differencing is performed.
Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian
An augmented Lagrangian (AL) can convert a constrained optimization problem into a sequence of simpler (e. g., unconstrained) problems, which are then usually solved with local solvers.
