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ORCIDTommaso Biancalani
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2020 – today
- 2024
[j2]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:
Building, benchmarking, and exploring perturbative maps of transcriptional and morphological data. PLoS Comput. Biol. 20(10): 1012463 (2024)- [c7]Nathaniel Diamant, Ehsan Hajiramezanali, Tommaso Biancalani, Gabriele Scalia:
Conformalized Deep Splines for Optimal and Efficient Prediction Sets. AISTATS 2024: 1657-1665 - [c6]Masatoshi Uehara, Yulai Zhao, Kevin Black, Ehsan Hajiramezanali, Gabriele Scalia, Nathaniel Lee Diamant, Alex M. Tseng, Sergey Levine, Tommaso Biancalani:
Feedback Efficient Online Fine-Tuning of Diffusion Models. ICML 2024 - [c5]Avantika Lal, David Garfield, Tommaso Biancalani, Gökcen Eraslan:
regLM: Designing Realistic Regulatory DNA with Autoregressive Language Models. RECOMB 2024: 332-335 - [i21]Masatoshi Uehara, Yulai Zhao, Kevin Black, Ehsan Hajiramezanali, Gabriele Scalia, Nathaniel Lee Diamant, Alex M. Tseng, Tommaso Biancalani, Sergey Levine:
Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control. CoRR abs/2402.15194 (2024) - [i20]Masatoshi Uehara, Yulai Zhao, Kevin Black, Ehsan Hajiramezanali, Gabriele Scalia, Nathaniel Lee Diamant, Alex M. Tseng, Sergey Levine, Tommaso Biancalani:
Feedback Efficient Online Fine-Tuning of Diffusion Models. CoRR abs/2402.16359 (2024) - [i19]Masatoshi Uehara, Yulai Zhao, Ehsan Hajiramezanali, Gabriele Scalia, Gökcen Eraslan, Avantika Lal, Sergey Levine, Tommaso Biancalani:
Bridging Model-Based Optimization and Generative Modeling via Conservative Fine-Tuning of Diffusion Models. CoRR abs/2405.19673 (2024) - [i18]Yulai Zhao, Masatoshi Uehara, Gabriele Scalia, Tommaso Biancalani, Sergey Levine, Ehsan Hajiramezanali:
Adding Conditional Control to Diffusion Models with Reinforcement Learning. CoRR abs/2406.12120 (2024) - [i17]Masatoshi Uehara, Yulai Zhao, Tommaso Biancalani, Sergey Levine:
Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review. CoRR abs/2407.13734 (2024) - [i16]Stephen Zhewen Lu, Ziqing Lu, Ehsan Hajiramezanali, Tommaso Biancalani, Yoshua Bengio, Gabriele Scalia, Michal Koziarski:
Cell Morphology-Guided Small Molecule Generation with GFlowNets. CoRR abs/2408.05196 (2024) - [i15]Xiner Li, Yulai Zhao, Chenyu Wang, Gabriele Scalia, Gökcen Eraslan, Surag Nair, Tommaso Biancalani, Aviv Regev, Sergey Levine, Masatoshi Uehara:
Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding. CoRR abs/2408.08252 (2024) - [i14]Alex M. Tseng, Gökcen Eraslan, Tommaso Biancalani, Gabriele Scalia:
A mechanistically interpretable neural network for regulatory genomics. CoRR abs/2410.06211 (2024) - [i13]Chenyu Wang, Masatoshi Uehara, Yichun He, Amy Wang, Tommaso Biancalani, Avantika Lal, Tommi S. Jaakkola, Sergey Levine, Hanchen Wang, Aviv Regev:
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design. CoRR abs/2410.13643 (2024) - 2023
- [c4]Muralikrishnna G. Sethuraman, Romain Lopez, Rahul Mohan, Faramarz Fekri, Tommaso Biancalani, Jan-Christian Hütter:
NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning. AISTATS 2023: 6371-6387 - [c3]Nathaniel Lee Diamant, Alex M. Tseng, Kangway V. Chuang, Tommaso Biancalani, Gabriele Scalia:
Improving Graph Generation by Restricting Graph Bandwidth. ICML 2023: 7939-7959 - [c2]Max W. Shen, Emmanuel Bengio, Ehsan Hajiramezanali, Andreas Loukas, Kyunghyun Cho, Tommaso Biancalani:
Towards Understanding and Improving GFlowNet Training. ICML 2023: 30956-30975 - [c1]You Wu, Omid Bazgir, Yongju Lee, Tommaso Biancalani, James Lu, Ehsan Hajiramezanali:
Multitask-Guided Self-Supervised Tabular Learning for Patient-Specific Survival Prediction. MLCB 2023: 10-22 - [i12]Muralikrishnna G. Sethuraman, Romain Lopez, Rahul Mohan, Faramarz Fekri, Tommaso Biancalani, Jan-Christian Hütter:
NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning. CoRR abs/2301.01849 (2023) - [i11]Nathaniel Diamant, Alex M. Tseng, Kangway V. Chuang, Tommaso Biancalani, Gabriele Scalia:
Improving Graph Generation by Restricting Graph Bandwidth. CoRR abs/2301.10857 (2023) - [i10]Alex M. Tseng, Nathaniel Diamant, Tommaso Biancalani, Gabriele Scalia:
GraphGUIDE: interpretable and controllable conditional graph generation with discrete Bernoulli diffusion. CoRR abs/2302.03790 (2023) - [i9]Max W. Shen, Emmanuel Bengio, Ehsan Hajiramezanali, Andreas Loukas, Kyunghyun Cho, Tommaso Biancalani:
Towards Understanding and Improving GFlowNet Training. CoRR abs/2305.07170 (2023) - [i8]Colin A. Grambow, Hayley Weir, Christian N. Cunningham, Tommaso Biancalani, Kangway V. Chuang:
CREMP: Conformer-Rotamer Ensembles of Macrocyclic Peptides for Machine Learning. CoRR abs/2305.08057 (2023) - [i7]Colin A. Grambow, Hayley Weir, Nathaniel L. Diamant, Alex M. Tseng, Tommaso Biancalani, Gabriele Scalia, Kangway V. Chuang:
RINGER: Rapid Conformer Generation for Macrocycles with Sequence-Conditioned Internal Coordinate Diffusion. CoRR abs/2305.19800 (2023) - [i6]Alex M. Tseng, Nathaniel Diamant, Tommaso Biancalani, Gabriele Scalia:
Complex Preferences for Different Convergent Priors in Discrete Graph Diffusion. CoRR abs/2306.02957 (2023) - [i5]Nathaniel Diamant, Ehsan Hajiramezanali, Tommaso Biancalani, Gabriele Scalia:
Conformalized Deep Splines for Optimal and Efficient Prediction Sets. CoRR abs/2311.00774 (2023) - [i4]Heming Yao, Jérôme Lüscher, Benjamín Gutiérrez-Becker, Josep Arús-Pous, Tommaso Biancalani, Amelie Bigorgne, David Richmond:
Unsupervised Segmentation of Colonoscopy Images. CoRR abs/2312.12599 (2023) - 2022
- [i3]Max W. Shen, Ehsan Hajiramezanali, Gabriele Scalia, Alex M. Tseng, Nathaniel Diamant, Tommaso Biancalani, Andreas Loukas:
Conditional Diffusion with Less Explicit Guidance via Model Predictive Control. CoRR abs/2210.12192 (2022) - [i2]Austin Atsango, Nathaniel L. Diamant, Ziqing Lu, Tommaso Biancalani, Gabriele Scalia, Kangway V. Chuang:
A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning. CoRR abs/2211.02130 (2022) - [i1]Alex M. Tseng, Tommaso Biancalani, Max M. Shen, Gabriele Scalia:
Hierarchically branched diffusion models for efficient and interpretable multi-class conditional generation. CoRR abs/2212.10777 (2022)
2010 – 2019
- 2019
- [j1]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, Julia Laskin, Pehr Harbury, Nicholas A. Nystrom, Jonathan C. Silverstein, Ziv Bar-Joseph, Kun Zhang, Katy Börner, Yiing Lin, Richard Conroy, Dena Procaccini, Ananda L. Roy, Ajay Pillai, Marishka Brown, Zorina S. Galis, Long Cai, Cole Trapnell, Dana Jackson, Garry P. Nolan, William James Greenleaf, Sylvia K. Plevritis, Sara Ahadi, Stephanie A. Nevins, Hayan Lee, Christian Martijn Schuerch, Sarah Black, Vishal Gautham Venkataraaman, Ed Esplin, Aaron Horning, Amir Bahmani, Xin Sun, Sanjay Jain, James S. Hagood, Gloria Pryhuber, Peter V. Kharchenko, Bernd Bodenmiller, Todd Brusko, Michael Clare-Salzler, Harry Nick, Kevin Otto, Clive Wasserfall, Marda Jorgensen, Maigan Brusko, Sergio Maffioletti, Richard M. Caprioli, Jeffrey M. Spraggins, Danielle Gutierrez, Nathan Heath Patterson, Elizabeth K. Neumann, Raymond Harris, Mark P. de Caestecker, Agnes B. Fogo, Raf Van de Plas, Ken Lau, Guo-Cheng Yuan, Qian Zhu, Ruben Dries, Peng Yin, Sinem K. Saka, Jocelyn Y. Kishi, Yu Wang, Isabel Goldaracena, Dong Hye Ye, Kristin E. Burnum-Johnson, Paul D. Piehowski, Charles Ansong, Ying Zhu, Tushar Desai, Jay Mulye, Peter Chou, Monica Nagendran, Sarah A. Teichmann, Benedict Paten, Robert F. Murphy, Jian Ma, Vladimir Yu. Kiselev, Carl Kingsford, Allyson Ricarte, Maria Keays, Sushma Anand Akoju, Matthew Ruffalo, Margaret Vella, Chuck McCallum, Leonard E. Cross, Samuel H. Friedman, Randy W. Heiland, Bruce William Herr II, Paul Macklin, Ellen M. Quardokus, Lisel Record, James P. Sluka, Griffin M. Weber, Philip D. Blood, Alexander Ropelewski, William Shirey, Robin M. Scibek, Paula M. Mabee, W. Christopher Lenhardt, Kimberly Robasky, Stavros Michailidis, John C. Marioni, Andrew Butler, Tim Stuart, Eyal Fisher, Shila Ghazanfar, Gökcen Eraslan, Tommaso Biancalani, Eeshit D. Vaishnav, Pothur Srinivas, Aaron Pawlyk, Salvatore Sechi, Elizabeth L. Wilder, James Anderson:
The human body at cellular resolution: the NIH Human Biomolecular Atlas Program. Nat. 574(7777): 187-192 (2019)
Coauthor Index
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