Rajesh Ranganath

Flow Map Learning via Nongradient Vector Flow

Mark Goldstein, Anshuk Uppal, Raghav Singhal, Aahlad Puli et autres

Diffusion and flow-based models benefit from simple regression losses, but inference incurs significant overhead because sampling requires integration. Consistency models address this by directly learning the flow maps along the ODE trajectory, opening a design space between one-step and many-step approaches. However, …

Flow Map Learning via Nongradient Vector Flow

Mark Goldstein, Anshuk Uppal, Raghav Singhal, Aahlad Puli et autres

Diffusion and flow-based models benefit from simple regression losses, but inference incurs significant overhead because sampling requires integration. Consistency models address this by directly learning the flow maps along the ODE trajectory, opening a design space between one-step and many-step approaches. However, …

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Imaging without Images: Using Artificial Intelligence for Direct Discovery of Spatial Signatures in the Absence of Image Reconstruction

Sumit Chopra, Raghav Singhal, Angela Tong, Rajesh Ranganath et autres

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Causal Machine Learning Is Not a Panacea: A Roadmap for Observational Causal Inference in Health

Donna Tjandra, Trenton Chang, S Parbhoo, Rajesh Ranganath et autres

Objective: The growing availability of large-scale observational clinical datasets and challenges in conducting randomized controlled trials have spurred enthusiasm in using causal machine learning (ML) for causal inference in observational data. We present a roadmap for applying causal ML to observational data. …

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IP04-25 KTGROW-ML: A NOVEL, MACHINE-LEARNING-BASED, MODEL TO PREDICT THE FUTURE GROWTH OF KIDNEY TUMORS

Simon W. White, Hersh Chandarana, Rajesh Ranganath, William C. Huang et autres

Development and Deployment of a Machine Learning Model to Triage the Use of Prostate MRI ( ProMT ‐ ML ) in Patients With Suspected Prostate Cancer

Jesse Persily, Hersh Chandarana, Angela Tong, Rajesh Ranganath et autres

BACKGROUND: Access to prostate MRI remains limited due to resource constraints and the need for expert interpretation. PURPOSE: To develop machine learning (ML) models that enable risk-based triage for prostate MRI (ProMT-ML) in the evaluation of prostate cancer. STUDY TYPE: Retrospective and …

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Development, External Validation, and Deployment of RFAN-ML: A Machine Learning Model to Estimate Renal Function After Nephrectomy

Jesse Persily, Steven L. Chang, Chen Chen, Yassamin Neshatvar et autres

PURPOSE: Partial nephrectomy has been advocated as the preferred surgical approach for small kidney tumors over total nephrectomy. However, partial nephrectomy is associated with increased perioperative risk. Estimating renal function after nephrectomy can facilitate personalized patient counseling, guide surgical approach, and identify …

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KL-Regularized Reinforcement Learning is Designed to Mode Collapse

Anthony GX-Chen, Jatin Prakash, Jeff Guo, Rob Fergus et autres

It is commonly believed that optimizing the reverse KL divergence results in "mode seeking", while optimizing forward KL results in "mass covering", with the latter being preferred if the goal is to sample from multiple diverse modes. We show -- mathematically and …

Rethinking Reasoning with MDLMs: Early Exits, Post-hoc Reasoning, and Beyond

Zachary Horvitz, Raghav Singhal, Hao Zou, Carles Domingo-Enrich et autres

The reasoning paradigm, where language models reason before answering, has enabled breakthroughs on tasks such as mathematical problem-solving. While current tooling for reasoning is built around next-token prediction trained models, recent works introduce an alternative choice: masked diffusion language models (MDLMs). MDLMs …

Three Forms of Stochastic Injection for Improved Distribution-to-Distribution Generative Modeling

S B Su, Yuhui Zhang, Rajesh Ranganath, Serena Yeung-Levy

Modeling transformations between arbitrary data distributions is a fundamental scientific challenge, arising in applications like drug discovery and evolutionary simulation. While flow matching offers a natural framework for this task, its use has thus far primarily focused on the noise-to-data setting, while …

4299 External reproduction of a causal estimation of the individual and cohort benefit of sequential vs concurrent chemoradiotherapy in SIII NSCLC patients

Charlie Cunniffe, Wouter van Amsterdam, Rajesh Ranganath, Fiona Blackhall et autres

MP06-02 DEVELOPMENT OF A MACHINE LEARNING MODEL TO TRIAGE THE USE OF PROSTATE MRI (PROMT-ML)

Jesse Persily, Hersh Chandarana, Angela Tong, Yassamin Neshatvar et autres