The human body at cellular resolution: the NIH Human Biomolecular Atlas Program. - Michael P. Snyder; Shin Lin; Amanda Posgai; Mark Atkinson; Aviv Regev; Jennifer Rood; Orit Rozenblatt-Rosen; Leslie Gaffney; Anna Hupalowska; Rahul Satija; Nils Gehlenborg; Jay Shendure; et al. (Nat., 2019)

Conditional Diffusion with Less Explicit Guidance via Model Predictive Control. - Max W. Shen; Ehsan Hajiramezanali; Gabriele Scalia; Alex M. Tseng; Nathaniel Diamant; Tommaso Biancalani; Andreas Loukas (CoRR, 2022)

A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning. - Austin Atsango; Nathaniel L. Diamant; Ziqing Lu; Tommaso Biancalani; Gabriele Scalia; Kangway V. Chuang (CoRR, 2022)

Hierarchically branched diffusion models for efficient and interpretable multi-class conditional generation. - Alex M. Tseng; Tommaso Biancalani; Max W. Shen; Gabriele Scalia (CoRR, 2022)

NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning. - Muralikrishnna G. Sethuraman; Romain Lopez; Rahul Mohan; Faramarz Fekri; Tommaso Biancalani; Jan-Christian Hütter (AISTATS, 2023)

Improving Graph Generation by Restricting Graph Bandwidth. - Nathaniel Lee Diamant; Alex M. Tseng; Kangway V. Chuang; Tommaso Biancalani; Gabriele Scalia (ICML, 2023)

Towards Understanding and Improving GFlowNet Training. - Max W. Shen; Emmanuel Bengio; Ehsan Hajiramezanali; Andreas Loukas; Kyunghyun Cho; Tommaso Biancalani (ICML, 2023)

Multitask-Guided Self-Supervised Tabular Learning for Patient-Specific Survival Prediction. - You Wu; Omid Bazgir; Yongju Lee; Tommaso Biancalani; James Lu; Ehsan Hajiramezanali (MLCB, 2023)

GraphGUIDE: interpretable and controllable conditional graph generation with discrete Bernoulli diffusion. - Alex M. Tseng; Nathaniel Diamant; Tommaso Biancalani; Gabriele Scalia (CoRR, 2023)

CREMP: Conformer-Rotamer Ensembles of Macrocyclic Peptides for Machine Learning. - Colin A. Grambow; Hayley Weir; Christian N. Cunningham; Tommaso Biancalani; Kangway V. Chuang (CoRR, 2023)

RINGER: Rapid Conformer Generation for Macrocycles with Sequence-Conditioned Internal Coordinate Diffusion. - Colin A. Grambow; Hayley Weir; Nathaniel L. Diamant; Alex M. Tseng; Tommaso Biancalani; Gabriele Scalia; Kangway V. Chuang (CoRR, 2023)

Complex Preferences for Different Convergent Priors in Discrete Graph Diffusion. - Alex M. Tseng; Nathaniel Diamant; Tommaso Biancalani; Gabriele Scalia (CoRR, 2023)

Unsupervised Segmentation of Colonoscopy Images. - Heming Yao; Jérôme Lüscher; Benjamín Gutiérrez-Becker; Josep Arús-Pous; Tommaso Biancalani; Amelie Bigorgne; David Richmond (CoRR, 2023)

Conformalized Deep Splines for Optimal and Efficient Prediction Sets. - Nathaniel Diamant; Ehsan Hajiramezanali; Tommaso Biancalani; Gabriele Scalia (AISTATS, 2024)

Feedback Efficient Online Fine-Tuning of Diffusion Models. - Masatoshi Uehara; Yulai Zhao 002; Kevin Black; Ehsan Hajiramezanali; Gabriele Scalia; Nathaniel Lee Diamant; Alex M. Tseng; Sergey Levine; Tommaso Biancalani (ICML, 2024)

GFlowNet Assisted Biological Sequence Editing. - Pouya M. Ghari; Alex M. Tseng; Gökcen Eraslan; Romain Lopez; Tommaso Biancalani; Gabriele Scalia; Ehsan Hajiramezanali (NeurIPS, 2024)

Bridging Model-Based Optimization and Generative Modeling via Conservative Fine-Tuning of Diffusion Models. - Masatoshi Uehara; Yulai Zhao 002; Ehsan Hajiramezanali; Gabriele Scalia; Gökcen Eraslan; Avantika Lal; Sergey Levine; Tommaso Biancalani (NeurIPS, 2024)

regLM: Designing Realistic Regulatory DNA with Autoregressive Language Models. - Avantika Lal; David Garfield; Tommaso Biancalani; Gökcen Eraslan (RECOMB, 2024)

Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control. - Masatoshi Uehara; Yulai Zhao 002; Kevin Black; Ehsan Hajiramezanali; Gabriele Scalia; Nathaniel Lee Diamant; Alex M. Tseng; Tommaso Biancalani; Sergey Levine (CoRR, 2024)

Adding Conditional Control to Diffusion Models with Reinforcement Learning. - Yulai Zhao 002; Masatoshi Uehara; Gabriele Scalia; Tommaso Biancalani; Sergey Levine; Ehsan Hajiramezanali (CoRR, 2024)

Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review. - Masatoshi Uehara; Yulai Zhao 002; Tommaso Biancalani; Sergey Levine (CoRR, 2024)

Cell Morphology-Guided Small Molecule Generation with GFlowNets. - Stephen Zhewen Lu; Ziqing Lu; Ehsan Hajiramezanali; Tommaso Biancalani; Yoshua Bengio; Gabriele Scalia; Michal Koziarski (CoRR, 2024)

Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding. - Xiner Li; Yulai Zhao 002; Chenyu Wang 003; Gabriele Scalia; Gökcen Eraslan; Surag Nair; Tommaso Biancalani; Aviv Regev; Sergey Levine; Masatoshi Uehara (CoRR, 2024)

A mechanistically interpretable neural network for regulatory genomics. - Alex M. Tseng; Gökcen Eraslan; Tommaso Biancalani; Gabriele Scalia (CoRR, 2024)

MolCap-Arena: A Comprehensive Captioning Benchmark on Language-Enhanced Molecular Property Prediction. - Carl Edwards; Ziqing Lu; Ehsan Hajiramezanali; Tommaso Biancalani; Heng Ji; Gabriele Scalia (CoRR, 2024)

Efficient Fine-Tuning of Single-Cell Foundation Models Enables Zero-Shot Molecular Perturbation Prediction. - Sepideh Maleki; Jan-Christian Huetter; Kangway V. Chuang; Gabriele Scalia; Tommaso Biancalani (CoRR, 2024)

Building, benchmarking, and exploring perturbative maps of transcriptional and morphological data. - Safiye Celik; Jan-Christian Hütter; Sandra Melo Carlos; Nathan H. Lazar; Rahul Mohan; Conor Tillinghast; Tommaso Biancalani; Marta M. Fay; Berton Earnshaw; Imran S. Haque (PLoS Comput. Biol., 2024)

Score-based Explainability for Graph Representations. - Ehsan Hajiramezanali; Sepideh Maleki; Max W. Shen; Kangway V. Chuang; Tommaso Biancalani; Gabriele Scalia (Trans. Mach. Learn. Res., 2024)

Hierarchically branched diffusion models leverage dataset structure for class-conditional generation. - Alex M. Tseng; Max W. Shen; Tommaso Biancalani; Gabriele Scalia (Trans. Mach. Learn. Res., 2024)

Adding Conditional Control to Diffusion Models with Reinforcement Learning. - Yulai Zhao 002; Masatoshi Uehara; Gabriele Scalia; Sun-Yuan Kung; Tommaso Biancalani; Sergey Levine; Ehsan Hajiramezanali (ICLR, 2025)

Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design. - Chenyu Wang 003; Masatoshi Uehara; Yichun He; Amy Wang; Avantika Lal; Tommi S. Jaakkola; Sergey Levine; Aviv Regev; Hanchen Wang 002; Tommaso Biancalani (ICLR, 2025)

Contextualizing biological perturbation experiments through language. - Menghua Wu; Russell Littman; Jacob Levine; Lin Qiu; Tommaso Biancalani; David Richmond; Jan-Christian Huetter (ICLR, 2025)

Inference-Time Alignment in Diffusion Models with Reward-Guided Generation: Tutorial and Review. - Masatoshi Uehara; Yulai Zhao 002; Chenyu Wang 003; Xiner Li; Aviv Regev; Sergey Levine; Tommaso Biancalani (CoRR, 2025)

Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design. - Masatoshi Uehara; Xingyu Su; Yulai Zhao 002; Xiner Li; Aviv Regev; Shuiwang Ji; Sergey Levine; Tommaso Biancalani (CoRR, 2025)

Dynamic Search for Inference-Time Alignment in Diffusion Models. - Xiner Li; Masatoshi Uehara; Xingyu Su; Gabriele Scalia; Tommaso Biancalani; Aviv Regev; Sergey Levine; Shuiwang Ji (CoRR, 2025)

Joint Embedding vs Reconstruction: Provable Benefits of Latent Space Prediction for Self Supervised Learning. - Hugues Van Assel; Mark Ibrahim; Tommaso Biancalani; Aviv Regev; Randall Balestriero (CoRR, 2025)

Iterative Distillation for Reward-Guided Fine-Tuning of Diffusion Models in Biomolecular Design. - Xingyu Su; Xiner Li; Masatoshi Uehara; Sunwoo Kim; Yulai Zhao 002; Gabriele Scalia; Ehsan Hajiramezanali; Tommaso Biancalani; Degui Zhi; Shuiwang Ji (CoRR, 2025)