collaborators

5 papers

cs.LG2026

Scalable and Interpretable Representation Alignment with Ordinal Similarity

Diogo Soares, Pankhil Gawade, Andrea Dittadi +1

Evaluating representation similarity is fundamental to representation learning. However, existing metrics suffer from significant limitations: they lack interpretability due to shi…

cs.LG2026

Synergistic Benefits of Joint Molecule Generation and Property Prediction

Adam Izdebski, Jan Olszewski, Pankhil Gawade +5

Modeling the joint distribution of data samples and their properties allows to construct a single model for both data generation and property prediction, with synergistic benefits…

cs.LG2026

Sample Efficient Generative Molecular Optimization with Joint Self-Improvement

Serra Korkmaz, Adam Izdebski, Jonathan Pirnay +5

Generative molecular optimization aims to design molecules with properties surpassing those of existing compounds. However, such candidates are rare and expensive to evaluate, yiel…

cs.LG2025

Freeze, Diffuse, Decode: Geometry-Aware Adaptation of Pretrained Transformer Embeddings for Antimicrobial Peptide Design

Pankhil Gawade, Adam Izdebski, Myriam Lizotte +4

Pretrained transformers provide rich, general-purpose embeddings, which are transferred to downstream tasks. However, current transfer strategies: fine-tuning and probing, either d…

cs.LG2025

seqme: a Python library for evaluating biological sequence design

Rasmus Møller-Larsen, Adam Izdebski, Jan Olszewski +4

Recent advances in computational methods for designing biological sequences have sparked the development of metrics to evaluate these methods performance in terms of the fidelity o…