5 citations · 10 across the 11 of their papers we have counts for
11 papers
A study of EHVI vs fixed scalarization for molecule design
Anabel Yong, Austin Tripp, Layla Hosseini-Gerami +1
Multi-objective Bayesian optimization (MOBO) provides a principled framework for navigating trade-offs in molecular design. However, its empirical advantages over scalarized altern…
AbRank: A Benchmark Dataset and Metric-Learning Framework for Antibody-Antigen Affinity Ranking
Chunan Liu, Aurelien Pelissier, Yanjun Shao +4
Accurate prediction of antibody-antigen (Ab-Ag) binding affinity is essential for therapeutic design and vaccine development, yet the performance of current models is limited by no…
Time-varying Factor Augmented Vector Autoregression with Grouped Sparse Autoencoder
Yiyong Luo, Brooks Paige, Jim Griffin
Recent economic events, including the global financial crisis and COVID-19 pandemic, have exposed limitations in linear Factor Augmented Vector Autoregressive (FAVAR) models for fo…
Effects of Dropout on Performance in Long-range Graph Learning Tasks
Jasraj Singh, Keyue Jiang, Brooks Paige +1
Message Passing Neural Networks (MPNNs) are a class of Graph Neural Networks (GNNs) that propagate information across the graph via local neighborhoods. The scheme gives rise to tw…
Right Now, Wrong Then: Non-Stationary Direct Preference Optimization under Preference Drift
Seongho Son, William Bankes, Sayak Ray Chowdhury +2
Current Large Language Model (LLM) preference optimization algorithms do not account for temporal preference drift, which can lead to severe misalignment. To address this limitatio…
AsEP: Benchmarking Deep Learning Methods for Antibody-specific Epitope Prediction
Chunan Liu, Lilian Denzler, Yihong Chen +2
Epitope identification is vital for antibody design yet challenging due to the inherent variability in antibodies. While many deep learning methods have been developed for general…
Analyzing the Generalization and Reliability of Steering Vectors
Daniel Tan, David Chanin, Aengus Lynch +4
Steering vectors (SVs) have been proposed as an effective approach to adjust language model behaviour at inference time by intervening on intermediate model activations. They have…
Generative Active Learning for the Search of Small-molecule Protein Binders
Maksym Korablyov, Cheng-Hao Liu, Moksh Jain +31
Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exhibit a property of interest remains a si…
Can a Confident Prior Replace a Cold Posterior?
Martin Marek, Brooks Paige, Pavel Izmailov
Benchmark datasets used for image classification tend to have very low levels of label noise. When Bayesian neural networks are trained on these datasets, they often underfit, misr…
Diffusive Gibbs Sampling
Wenlin Chen, Mingtian Zhang, Brooks Paige +2
The inadequate mixing of conventional Markov Chain Monte Carlo (MCMC) methods for multi-modal distributions presents a significant challenge in practical applications such as Bayes…
Gaussian Processes on Cellular Complexes
Mathieu Alain, So Takao, Brooks Paige +1
In recent years, there has been considerable interest in developing machine learning models on graphs to account for topological inductive biases. In particular, recent attention h…