171 citations · 937 across the 54 of their papers we have counts for
12 papers · 1 filter
SMART: Self-supervised Multi-task pretrAining with contRol Transformers
Yanchao Sun, Shuang Ma, Ratnesh Madaan +3
Self-supervised pretraining has been extensively studied in language and vision domains, where a unified model can be easily adapted to various downstream tasks by pretraining repr…
ClimaX: A foundation model for weather and climate
Tung Nguyen, Johannes Brandstetter, Ashish Kapoor +2
Most state-of-the-art approaches for weather and climate modeling are based on physics-informed numerical models of the atmosphere. These approaches aim to model the non-linear dyn…
Learning Modular Simulations for Homogeneous Systems
Jayesh K. Gupta, Sai Vemprala, Ashish Kapoor
Complex systems are often decomposed into modular subsystems for engineering tractability. Although various equation based white-box modeling techniques make use of such structure,…
Denoised Smoothing: A Provable Defense for Pretrained Classifiers
Hadi Salman, Mingjie Sun, Greg Yang +2
We present a method for provably defending any pretrained image classifier against adversarial attacks. This method, for instance, allows public vision API providers and u…
Enhancing Stratospheric Weather Analyses and Forecasts by Deploying Sensors from a Weather Balloon
Kiwan Maeng, Iskender Kushan, Brandon Lucia +1
The ability to analyze and forecast stratospheric weather conditions is fundamental to addressing climate change. However, our capacity to collect data in the stratosphere is limit…
Identifying Bias in AI using Simulation
Daniel McDuff, Roger Cheng, Ashish Kapoor
Machine learned models exhibit bias, often because the datasets used to train them are biased. This presents a serious problem for the deployment of such technology, as the resulti…
Risk-Aware Algorithms for Adversarial Contextual Bandits
Wen Sun, Debadeepta Dey, Ashish Kapoor
In this work we consider adversarial contextual bandits with risk constraints. At each round, nature prepares a context, a cost for each arm, and additionally a risk for each arm.…
Bayesian inference via rejection filtering
Nathan Wiebe, Christopher Granade, Ashish Kapoor +1
We provide a method for approximating Bayesian inference using rejection sampling. We not only make the process efficient, but also dramatically reduce the memory required relative…
Quantum Inspired Training for Boltzmann Machines
Nathan Wiebe, Ashish Kapoor, Christopher Granade +1
We present an efficient classical algorithm for training deep Boltzmann machines (DBMs) that uses rejection sampling in concert with variational approximations to estimate the grad…
Riffled Independence for Efficient Inference with Partial Rankings
Jonathan Huang, Ashish Kapoor, Carlos Guestrin
Distributions over rankings are used to model data in a multitude of real world settings such as preference analysis and political elections. Modeling such distributions presents s…
On Discarding, Caching, and Recalling Samples in Active Learning
Ashish Kapoor, Eric J. Horvitz
We address challenges of active learning under scarce informational resources in non-stationary environments. In real-world settings, data labeled and integrated into a predictive…
Efficient Probabilistic Inference with Partial Ranking Queries
Jonathan Huang, Ashish Kapoor, Carlos E. Guestrin
Distributions over rankings are used to model data in various settings such as preference analysis and political elections. The factorial size of the space of rankings, however, ty…