2 citations · 2 across the 5 of their papers we have counts for
7 papers
Soft Strategy Selection for Batch-Mode Active Learning
Rushil Gupta, Romain Lopez
Real-world deployment of active learning typically forces practitioners to choose an acquisition strategy before any data is labeled. This is a daunting task: strategy performance…
AdaWeather: Adaptively Mixing Probabilistic Weather Forecasts with Logarithmic Regret
Saptarishi Dhanuka, Sarvesh Iyer, Manmeet Singh +5
Recent advances in machine learning have produced probabilistic weather forecasting models comparable to state-of-the-art numerical weather predictors. But no model consistently do…
Generating DDPM-based Samples from Tilted Distributions
Himadri Mandal, Dhruman Gupta, Rushil Gupta +5
Given independent samples from a -dimensional probability distribution, our aim is to generate diffusion-based samples from a distribution obtained by tilting the original,…
LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet?
Rushil Gupta, Jason Hartford, Bang Liu
Large language models (LLMs) have recently been proposed as general-purpose agents for experimental design, with claims that they can perform in-context experimental design. We eva…
Tight Lower Bounds and Improved Convergence in Performative Prediction
Pedram Khorsandi, Rushil Gupta, Mehrnaz Mofakhami +2
Performative prediction is a framework accounting for the shift in the data distribution induced by the prediction of a model deployed in the real world. Ensuring rapid convergence…
Pairing Analogy-Augmented Generation with Procedural Memory for Procedural Q&A
K Roth, Rushil Gupta, Simon Halle +1
Large language models struggle to synthesize disparate pieces of information into a coherent plan when approaching a complex procedural task. In this work, we introduce a novel for…
Learning Disentangled Representation in Object-Centric Models for Visual Dynamics Prediction via Transformers
Sanket Gandhi, Atul, Samanyu Mahajan +4
Recent work has shown that object-centric representations can greatly help improve the accuracy of learning dynamics while also bringing interpretability. In this work, we take thi…