74 citations · 83 across the 8 of their papers we have counts for
9 papers
PairIT: Autoregressive Transformers for Low-Data Molecule Optimization
Hannah Lawrence, Bodhi P. Vani, Ji Won Park +5
Generating novel molecules with desired properties is a central challenge in drug discovery. Although foundational work focused on de novo generation, practical use cases require a…
MGDA-Decoupled: Geometry-Aware Multi-Objective Optimisation for DPO-based LLM Alignment
Andor Vári-Kakas, Ji Won Park, Natasa Tagasovska
Aligning large language models (LLMs) to desirable human values requires balancing multiple, potentially conflicting objectives such as helpfulness, truthfulness, and harmlessness,…
Supervised Contrastive Block Disentanglement
Taro Makino, Ji Won Park, Natasa Tagasovska +11
Real-world datasets often combine data collected under different experimental conditions. This yields larger datasets, but also introduces spurious correlations that make it diffic…
Antibody DomainBed: Out-of-Distribution Generalization in Therapeutic Protein Design
Nataša Tagasovska, Ji Won Park, Matthieu Kirchmeyer +8
Machine learning (ML) has demonstrated significant promise in accelerating drug design. Active ML-guided optimization of therapeutic molecules typically relies on a surrogate model…
MoleCLUEs: Molecular Conformers Maximally In-Distribution for Predictive Models
Michael Maser, Natasa Tagasovska, Jae Hyeon Lee +1
Structure-based molecular ML (SBML) models can be highly sensitive to input geometries and give predictions with large variance. We present an approach to mitigate the challenge of…
BOtied: Multi-objective Bayesian optimization with tied multivariate ranks
Ji Won Park, Nataša Tagasovska, Michael Maser +2
Many scientific and industrial applications require the joint optimization of multiple, potentially competing objectives. Multi-objective Bayesian optimization (MOBO) is a sample-e…
Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis
Yanan Xin, Natasa Tagasovska, Fernando Perez-Cruz +1
Artificial intelligence (AI) is revolutionizing many areas of our lives, leading a new era of technological advancement. Particularly, the transportation sector would benefit from…
Uncertainty Surrogates for Deep Learning
Radhakrishna Achanta, Natasa Tagasovska
In this paper we introduce a novel way of estimating prediction uncertainty in deep networks through the use of uncertainty surrogates. These surrogates are features of the penulti…
Single-Model Uncertainties for Deep Learning
Natasa Tagasovska, David Lopez-Paz
We provide single-model estimates of aleatoric and epistemic uncertainty for deep neural networks. To estimate aleatoric uncertainty, we propose Simultaneous Quantile Regression (S…