102 citations · 317 across the 18 of their papers we have counts for
18 papers
Towards Conversational Medical AI with Eyes, Ears and a Voice
Meet Shah, Jason Gusdorf, Anil Palepu +50
The practice of medicine relies not only upon skillful dialogue but also on the nuanced exchange and interpretation of rich auditory and visual cues between doctors and patients. B…
A Unification of Discrete, Gaussian, and Simplicial Diffusion
Nuria Alina Chandra, Yucen Lily Li, Alan N. Amin +5
To model discrete sequences such as DNA, proteins, and language using diffusion, practitioners must choose between three major methods: diffusion in discrete space, Gaussian diffus…
Is Sequence Information All You Need for Bayesian Optimization of Antibodies?
Sebastian W. Ober, Calvin McCarter, Aniruddh Raghu +4
Bayesian optimization is a natural candidate for the engineering of antibody therapeutic properties, which is often iterative and expensive. However, finding the optimal choice of…
Improved Therapeutic Antibody Reformatting through Multimodal Machine Learning
Jiayi Xin, Aniruddh Raghu, Nick Bhattacharya +3
Modern therapeutic antibody design often involves composing multi-part assemblages of individual functional domains, each of which may be derived from a different source or enginee…
Guided Sequence-Structure Generative Modeling for Iterative Antibody Optimization
Aniruddh Raghu, Sebastian Ober, Maxwell Kazman +1
Therapeutic antibody candidates often require extensive engineering to improve key functional and developability properties before clinical development. This can be achieved throug…
Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences
Alan Nawzad Amin, Nate Gruver, Yilun Kuang +6
To build effective therapeutics, biologists iteratively mutate antibody sequences to improve binding and stability. Proposed mutations can be informed by previous measurements or b…
Generative Humanization for Therapeutic Antibodies
Cade Gordon, Aniruddh Raghu, Peyton Greenside +1
Antibody therapies have been employed to address some of today's most challenging diseases, but must meet many criteria during drug development before reaching a patient. Humanizat…
Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series
Aniruddh Raghu, Payal Chandak, Ridwan Alam +2
Self-supervised learning (SSL) for clinical time series data has received significant attention in recent literature, since these data are highly rich and provide important informa…
Data Augmentation for Electrocardiograms
Aniruddh Raghu, Divya Shanmugam, Eugene Pomerantsev +2
Neural network models have demonstrated impressive performance in predicting pathologies and outcomes from the 12-lead electrocardiogram (ECG). However, these models often need to…
Meta-Learning to Improve Pre-Training
Aniruddh Raghu, Jonathan Lorraine, Simon Kornblith +2
Pre-training (PT) followed by fine-tuning (FT) is an effective method for training neural networks, and has led to significant performance improvements in many domains. PT can inco…
Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction
Aniruddh Raghu, John Guttag, Katherine Young +3
The impact of machine learning models on healthcare will depend on the degree of trust that healthcare professionals place in the predictions made by these models. In this paper, w…
Teaching with Commentaries
Aniruddh Raghu, Maithra Raghu, Simon Kornblith +2
Effective training of deep neural networks can be challenging, and there remain many open questions on how to best learn these models. Recently developed methods to improve neural…
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML
Aniruddh Raghu, Maithra Raghu, Samy Bengio +1
An important research direction in machine learning has centered around developing meta-learning algorithms to tackle few-shot learning. An especially successful algorithm has been…
Model-Based Reinforcement Learning for Sepsis Treatment
Aniruddh Raghu, Matthieu Komorowski, Sumeetpal Singh
Sepsis is a dangerous condition that is a leading cause of patient mortality. Treating sepsis is highly challenging, because individual patients respond very differently to medical…
Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters
Aniruddh Raghu, Omer Gottesman, Yao Liu +4
In this work, we consider the problem of estimating a behaviour policy for use in Off-Policy Policy Evaluation (OPE) when the true behaviour policy is unknown. Via a series of empi…
Representation Balancing MDPs for Off-Policy Policy Evaluation
Yao Liu, Omer Gottesman, Aniruddh Raghu +4
We study the problem of off-policy policy evaluation (OPPE) in RL. In contrast to prior work, we consider how to estimate both the individual policy value and average policy value…
Deep Reinforcement Learning for Sepsis Treatment
Aniruddh Raghu, Matthieu Komorowski, Imran Ahmed +3
Sepsis is a leading cause of mortality in intensive care units and costs hospitals billions annually. Treating a septic patient is highly challenging, because individual patients r…
Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach
Aniruddh Raghu, Matthieu Komorowski, Leo Anthony Celi +2
Sepsis is a leading cause of mortality in intensive care units (ICUs) and costs hospitals billions annually. Treating a septic patient is highly challenging, because individual pat…