Joseph Viviano

Multi-Objective Amortized CDRH3 Sequence Sampling Diversifies Promising Antibody Candidates

Almer Van Der Sloot

Quentin Fournier

Robert M. Vernon

Christopher James Langmead

Antibody therapeutics have transformed treatment across diverse therapeutic areas and have become central to modern medicine. While recent m… (see more)achine learning approaches aim to accelerate antibody discovery through structure-based design and target-affinity optimization, successful candidates must also satisfy broad developability constraints, including structural compatibility, low immunogenicity, and favorable biophysical properties, while remaining sufficiently diverse to provide sufficient coverage of design space to mitigate later-stage development failures. We introduce a preference-conditioned multi-objective generative flow network (GFN) fine-tuning pipeline for fixed-framework CDRH3 loop redesign that fine-tunes a biological sequence model against many inexpensive proxies covering backbone compatibility, sequence likelihood, immunogenicity risk, novelty, and developability liabilities. It combines up to fourteen predictive rewards in a single policy trained to sample CDRH3 loops across 623 SAbDab antibody structures, leveraging a reward reduction strategy to preserve Pareto dominance under this learned map. We also introduce a novel autoregressive span-in-filling variant of AMPLIFY-350M that serves both as a scaffold-conditioned CDRH3 generation prior and as a direct conditional likelihood scorer. We evaluated reward scores, diversity, and design quality on loops sampled on 45 held-out antibody complexes to assess structural consistency, confidence, aggregation propensity, stability, and antigen binding. Compared with the inverse-folding methods ProteinMPNN and ESM-IF, our approach produces roughly 10x more distinct acceptable candidates per design at 50% sequence identity, while holding 10% of the 14-objective Pareto front pooled across methods, trading per-sample quality for greater exploration of novel possible structures.

2026-10-11

arXiv (preprint)

Local Inconsistency Resolution: The Interplay between Attention and Control in Probabilistic Models

Oliver Ethan Richardson

We present a generic algorithm for learning and approximate inference with an intuitive epistemic interpretation: iteratively focus on a sub… (see more)set of the model and resolve inconsistencies using the parameters under control. This framework, which we call Local Inconsistency Resolution (LIR) is built upon Probabilistic Dependency Graphs (PDGs), which provide a flexible representational foundation capable of capturing inconsistent beliefs. We show how LIR unifies and generalizes a wide variety of important algorithms in the literature, including the Expectation-Maximization (EM) algorithm, belief propagation, adversarial training, GANs, and GFlowNets. Each of these methods can be recovered as a specific instance of LIR by choosing a procedure to direct focus (attention and control). We implement this algorithm for discrete PDGs and study its properties on synthetically generated PDGs, comparing its behavior to the global optimization semantics of the full PDG.

2026-02-02

International Conference on Artificial Intelligence and Statistics (spotlight)

A scalable gene network model of regulatory dynamics in single cells

Paul Bertin

Alejandro Tejada-Lapuerta

Weixu Wang

Stefan Bauer

Fabian J. Theis

2025-03-24

ArXiv (preprint)

Action Abstractions for Amortized Sampling

Lena Nehale Ezzine

Nikolay Malkin

As trajectories sampled by policies used by reinforcement learning (RL) and generative flow networks (GFlowNets) grow longer, credit assignm… (see more)ent and exploration become more challenging, and the long planning horizon hinders mode discovery and generalization. The challenge is particularly pronounced in entropy-seeking RL methods, such as generative flow networks, where the agent must learn to sample from a structured distribution and discover multiple high-reward states, each of which take many steps to reach. To tackle this challenge, we propose an approach to incorporate the discovery of action abstractions, or high-level actions, into the policy optimization process. Our approach involves iteratively extracting action subsequences commonly used across many high-reward trajectories and `chunking' them into a single action that is added to the action space. In empirical evaluation on synthetic and real-world environments, our approach demonstrates improved sample efficiency performance in discovering diverse high-reward objects, especially on harder exploration problems. We also observe that the abstracted high-order actions are interpretable, capturing the latent structure of the reward landscape of the action space. This work provides a cognitively motivated approach to action abstraction in RL and is the first demonstration of hierarchical planning in amortized sequential sampling.

2025-01-21

International Conference on Learning Representations (poster)

Multi-Domain Balanced Sampling Improves Out-of-Generalization of Chest X-ray Pathology Prediction Models

Enoch Amoatey Tetteh

David M. Krueger

Joseph Paul Cohen

Learning models that generalize under different distribution shifts in medical imaging has been a long-standing research challenge. There ha… (see more)ve been several proposals for efﬁcient and robust visual representation learning among vision research practitioners, especially in the sensitive and critical biomedical domain. In this paper, we propose an idea for out-of-distribution generalization of chest X-ray pathologies that uses a simple balanced batch sampling technique. We observed that balanced sampling between the multiple training datasets improves the performance over baseline models trained without balancing. Code for this work is available on Github. 1

2021-12-26

arXiv (preprint)

www.semanticscholar.org

Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models

David Krueger

Learning models that generalize under different distribution shifts in medical imaging has been a long-standing research challenge. There ha… (see more)ve been several proposals for efficient and robust visual representation learning among vision research practitioners, especially in the sensitive and critical biomedical domain. In this paper, we propose an idea for out-of-distribution generalization of chest X-ray pathologies that uses a simple balanced batch sampling technique. We observed that balanced sampling between the multiple training datasets improves the performance over baseline models trained without balancing.

2021-12-26

arXiv (preprint)

Problèmes associés au déploiement des modèles fondés sur l’apprentissage machine en santé

Joseph Paul Cohen

Tianshi Cao

Chin-wei Huang

Michael Fralick

Marzyeh Ghassemi

Muhammad Mamdani

Russell Greiner

2021-11-06

Canadian Medical Association Journal (published)

Problems in the deployment of machine-learned models in health care

Joseph Paul Cohen

Tianshi Cao

Chinwei Huang

Michael Fralick

Marzyeh Ghassemi

M. Mamdani

R. Greiner

2021-08-29

Canadian Medical Association Journal (published)

What's in the Box? A Preliminary Analysis of Undesirable Content in the Common Crawl Corpus

Alexandra (Sasha) Luccioni

Alexandra Luccioni, Joseph Viviano. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th Int… (see more)ernational Joint Conference on Natural Language Processing (Volume 2: Short Papers). 2021.

2021-07-31

Meeting of the Association for Computational Linguistics and International Joint Conference on Natural Language Processing (published)

Saliency Is a Possible Red Herring When Diagnosing Poor Generalization

Becks Simpson

Poor generalization is one symptom of models that learn to predict target variables using spuriously-correlated image features present only … (see more)in the training distribution instead of the true image features that denote a class. It is often thought that this can be diagnosed visually using attribution (aka saliency) maps. We study if this assumption is correct. In some prediction tasks, such as for medical images, one may have some images with masks drawn by a human expert, indicating a region of the image containing relevant information to make the prediction. We study multiple methods that take advantage of such auxiliary labels, by training networks to ignore distracting features which may be found outside of the region of interest. This mask information is only used during training and has an impact on generalization accuracy depending on the severity of the shift between the training and test distributions. Surprisingly, while these methods improve generalization performance in the presence of a covariate shift, there is no strong correspondence between the correction of attribution towards the features a human expert has labelled as important and generalization performance. These results suggest that the root cause of poor generalization may not always be spatially defined, and raise questions about the utility of masks as "attribution priors" as well as saliency maps for explainable predictions.

2021-05-02

International Conference on Learning Representations (Poster)

Saliency Is a Possible Red Herring When Diagnosing Poor Generalization

Becks Simpson

Poor generalization is one symptom of models that learn to predict target variables using spuriously-correlated image features present only … (see more)in the training distribution instead of the true image features that denote a class. It is often thought that this can be diagnosed visually using attribution (aka saliency) maps. We study if this assumption is correct. In some prediction tasks, such as for medical images, one may have some images with masks drawn by a human expert, indicating a region of the image containing relevant information to make the prediction. We study multiple methods that take advantage of such auxiliary labels, by training networks to ignore distracting features which may be found outside of the region of interest. This mask information is only used during training and has an impact on generalization accuracy depending on the severity of the shift between the training and test distributions. Surprisingly, while these methods improve generalization performance in the presence of a covariate shift, there is no strong correspondence between the correction of attribution towards the features a human expert has labelled as important and generalization performance. These results suggest that the root cause of poor generalization may not always be spatially defined, and raise questions about the utility of masks as "attribution priors" as well as saliency maps for explainable predictions.

2021-05-02

International Conference on Learning Representations (Poster)