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Found 13 papers, 12 papers with code
Closed-Form Test Functions for Biophysical Sequence Optimization Algorithms
There is a growing body of work seeking to replicate the success of machine learning (ML) on domains like computer vision (CV) and natural language processing (NLP) to applications involving biophysical data.
Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)
As artificial intelligence (AI) / machine learning (ML) gain widespread adoption, practitioners are increasingly seeking means to quantify and control the risk these systems incur.
Protein Design with Guided Discrete Diffusion
A popular approach to protein design is to combine a generative model with a discriminative model for conditional sampling.
GAUCHE: A Library for Gaussian Processes in Chemistry
By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry.
Bayesian Optimization with Conformal Prediction Sets
Bayesian optimization is a coherent, ubiquitous approach to decision-making under uncertainty, with applications including multi-arm bandits, active learning, and black-box optimization.
PropertyDAG: Multi-objective Bayesian optimization of partially ordered, mixed-variable properties for biological sequence design
Bayesian optimization offers a sample-efficient framework for navigating the exploration-exploitation trade-off in the vast design space of biological sequences.
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
Bayesian optimization (BayesOpt) is a gold standard for query-efficient continuous optimization.
Deconstructing the Inductive Biases of Hamiltonian Neural Networks
Physics-inspired neural networks (NNs), such as Hamiltonian or Lagrangian NNs, dramatically outperform other learned dynamics models by leveraging strong inductive biases.
Conditioning Sparse Variational Gaussian Processes for Online Decision-making
With a principled representation of uncertainty and closed form posterior updates, Gaussian processes (GPs) are a natural choice for online decision making.
Does Knowledge Distillation Really Work?
Knowledge distillation is a popular technique for training a small student network to emulate a larger teacher model, such as an ensemble of networks.
Kernel Interpolation for Scalable Online Gaussian Processes
Gaussian processes (GPs) provide a gold standard for performance in online settings, such as sample-efficient control and black box optimization, where we need to update a posterior distribution as we acquire data in a sequential fashion.
On the model-based stochastic value gradient for continuous reinforcement learning
For over a decade, model-based reinforcement learning has been seen as a way to leverage control-based domain knowledge to improve the sample-efficiency of reinforcement learning agents.
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data
The translation equivariance of convolutional layers enables convolutional neural networks to generalize well on image problems.
