no code implementations • 4 Apr 2024 • Jerret Ross, Brian Belgodere, Samuel C. Hoffman, Vijil Chenthamarakshan, Youssef Mroueh, Payel Das
Impressively, we find GP-MoLFormer is able to generate a significant fraction of novel, valid, and unique SMILES even when the number of generated molecules is in the 10 billion range and the reference set is over a billion.
no code implementations • 17 Feb 2023 • Manish Nagireddy, Moninder Singh, Samuel C. Hoffman, Evaline Ju, Karthikeyan Natesan Ramamurthy, Kush R. Varshney
In this paper, focusing specifically on compositions of functions arising from the different pillars, we aim to reduce this gap, develop new insights for trustworthy ML, and answer questions such as the following.
no code implementations • 11 Oct 2022 • Michael Feffer, Martin Hirzel, Samuel C. Hoffman, Kiran Kate, Parikshit Ram, Avraham Shinnar
Bias mitigators can improve algorithmic fairness in machine learning models, but their effect on fairness is often not stable across data splits.
no code implementations • 14 Jul 2022 • Samuel C. Hoffman, Kahini Wadhawan, Payel Das, Prasanna Sattigeri, Karthikeyan Shanmugam
In this work, we provide a simple algorithm that relies on perturbation experiments on latent codes of a pre-trained generative autoencoder to uncover a causal graph that is implied by the generative model.
1 code implementation • 8 Jul 2022 • Matteo Manica, Jannis Born, Joris Cadow, Dimitrios Christofidellis, Ashish Dave, Dean Clarke, Yves Gaetan Nana Teukam, Giorgio Giannone, Samuel C. Hoffman, Matthew Buchan, Vijil Chenthamarakshan, Timothy Donovan, Hsiang Han Hsu, Federico Zipoli, Oliver Schilter, Akihiro Kishimoto, Lisa Hamada, Inkit Padhi, Karl Wehden, Lauren McHugh, Alexy Khrabrov, Payel Das, Seiji Takeda, John R. Smith
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery.
no code implementations • 19 Apr 2022 • Vijil Chenthamarakshan, Samuel C. Hoffman, C. David Owen, Petra Lukacik, Claire Strain-Damerell, Daren Fearon, Tika R. Malla, Anthony Tumber, Christopher J. Schofield, Helen M. E. Duyvesteyn, Wanwisa Dejnirattisai, Loic Carrique, Thomas S. Walter, Gavin R. Screaton, Tetiana Matviiuk, Aleksandra Mojsilovic, Jason Crain, Martin A. Walsh, David I. Stuart, Payel Das
To perform target-aware design of novel inhibitor molecules, a protein sequence-conditioned sampling on the generative foundation model is performed.
no code implementations • 1 Feb 2022 • Michael Feffer, Martin Hirzel, Samuel C. Hoffman, Kiran Kate, Parikshit Ram, Avraham Shinnar
A popular approach to train more stable models is ensemble learning.
no code implementations • 2 Dec 2021 • Samuel C. Hoffman, Vijil Chenthamarakshan, Dmitry Yu. Zubarev, Daniel P. Sanders, Payel Das
Photo-acid generators (PAGs) are compounds that release acids ($H^+$ ions) when exposed to light.
no code implementations • 24 Sep 2021 • Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovic, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang
As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations.
no code implementations • NeurIPS 2020 • Vijil Chenthamarakshan, Payel Das, Samuel C. Hoffman, Hendrik Strobelt, Inkit Padhi, Kar Wai Lim, Benjamin Hoover, Matteo Manica, Jannis Born, Teodoro Laino, Aleksandra Mojsilovic
CogMol also includes insilico screening for assessing toxicity of parent molecules and their metabolites with a multi-task toxicity classifier, synthetic feasibility with a chemical retrosynthesis predictor, and target structure binding with docking simulations.
no code implementations • 25 Sep 2019 • Thanh V Nguyen, Youssef Mroueh, Samuel C. Hoffman, Payel Das, Pierre Dognin, Giuseppe Romano, Chinmay Hegde
We consider the problem of generating configurations that satisfy physical constraints for optimal material nano-pattern design, where multiple (and often conflicting) properties need to be simultaneously satisfied.
2 code implementations • 6 Sep 2019 • Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang
Equally important, we provide a taxonomy to help entities requiring explanations to navigate the space of explanation methods, not only those in the toolkit but also in the broader literature on explainability.
13 code implementations • 3 Oct 2018 • Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, Seema Nagar, Karthikeyan Natesan Ramamurthy, John Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, Yunfeng Zhang
Such architectural design and abstractions enable researchers and developers to extend the toolkit with their new algorithms and improvements, and to use it for performance benchmarking.
no code implementations • 24 May 2018 • Prasanna Sattigeri, Samuel C. Hoffman, Vijil Chenthamarakshan, Kush R. Varshney
In this paper, we introduce the Fairness GAN, an approach for generating a dataset that is plausibly similar to a given multimedia dataset, but is more fair with respect to protected attributes in allocative decision making.