1 citations · 1 across the 10 of their papers we have counts for
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Time-Anchored Diffusion Language Models: Latent-Space Caching for Fast Generation
Joel Anto Paul, Litu Rout, Aditya Akella +1
Recent work on anchored diffusion language models improves denoising by shaping an intermediate latent space with supervised important-token targets. In this work, we introduce tim…
Diffusion-Based Posterior Sampling: A Feynman-Kac Analysis of Bias and Stability
Matias G. Delgadino, Sebastien Motsch, Advait Parulekar +2
Diffusion-based posterior samplers draw from measurement- or reward-conditioned posteriors using pretrained diffusion priors. Though widely used for inverse problems, their output…
Temper-Then-Tilt: Principled Unlearning for Generative Models through Tempering and Classifier Guidance
Jacob L. Block, Mehryar Mohri, Aryan Mokhtari +1
We study machine unlearning in large generative models by framing the task as density ratio estimation to a target distribution rather than supervised fine-tuning. While classifier…
Entropy Aware Reward Guidance for Diffusion Language Model Alignment
Atula Tejaswi, Litu Rout, Constantine Caramanis +2
Reward guidance, also known as posterior sampling, is a popular method for test-time adaptation and post-training in continuous diffusion models. In this paper, we study reward gui…
Test-Time Anchoring for Discrete Diffusion Posterior Sampling
Litu Rout, Andreas Lugmayr, Yasamin Jafarian +4
While continuous diffusion models have achieved remarkable success, discrete diffusion offers a unified framework for jointly modeling text and images. Beyond unification, discrete…
Efficient Approximate Posterior Sampling with Annealed Langevin Monte Carlo
Advait Parulekar, Litu Rout, Karthikeyan Shanmugam +1
We study the problem of posterior sampling in the context of score based generative models. We have a trained score network for a prior , a measurement model , and ar…
Machine Unlearning under Overparameterization
Jacob L. Block, Aryan Mokhtari, Sanjay Shakkottai
Machine unlearning algorithms aim to remove the influence of specific training samples, ideally recovering the model that would have resulted from training on the remaining data al…
Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations
Litu Rout, Yujia Chen, Nataniel Ruiz +3
Generative models transform random noise into images; their inversion aims to transform images back to structured noise for recovery and editing. This paper addresses two key tasks…
Provable Meta-Learning with Low-Rank Adaptations
Jacob L. Block, Sundararajan Srinivasan, Liam Collins +2
The power of foundation models (FMs) lies in their capacity to learn highly expressive representations that can be adapted to a broad spectrum of tasks. However, these pretrained m…
Constrained Posterior Sampling: Time Series Generation with Hard Constraints
Sai Shankar Narasimhan, Shubhankar Agarwal, Litu Rout +2
Generating realistic time series samples is crucial for stress-testing models and protecting user privacy by using synthetic data. In engineering and safety-critical applications,…
RB-Modulation: Training-Free Personalization of Diffusion Models using Stochastic Optimal Control
Litu Rout, Yujia Chen, Nataniel Ruiz +4
We propose Reference-Based Modulation (RB-Modulation), a new plug-and-play solution for training-free personalization of diffusion models. Existing training-free approaches exhibit…
In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness
Liam Collins, Advait Parulekar, Aryan Mokhtari +2
A striking property of transformers is their ability to perform in-context learning (ICL), a machine learning framework in which the learner is presented with a novel context durin…
Beyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion
Litu Rout, Yujia Chen, Abhishek Kumar +3
Sampling from the posterior distribution poses a major computational challenge in solving inverse problems using latent diffusion models. Common methods rely on Tweedie's first-ord…