289 citations · 1.2k across the 72 of their papers we have counts for
11 papers · 1 filter
Training neural operators to preserve invariant measures of chaotic attractors
Ruoxi Jiang, Peter Y. Lu, Elena Orlova +1
Chaotic systems make long-horizon forecasts difficult because small perturbations in initial conditions cause trajectories to diverge at an exponential rate. In this setting, neura…
Deep Stochastic Mechanics
Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang +2
This paper introduces a novel deep-learning-based approach for numerical simulation of a time-evolving Schrödinger equation inspired by stochastic mechanics and generative diffusio…
ReLU Neural Networks with Linear Layers are Biased Towards Single- and Multi-Index Models
Suzanna Parkinson, Greg Ongie, Rebecca Willett
Neural networks often operate in the overparameterized regime, in which there are far more parameters than training samples, allowing the training data to be fit perfectly. That is…
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantification
Ruoxi Jiang, Rebecca Willett
This paper explores learning emulators for parameter estimation with uncertainty estimation of high-dimensional dynamical systems. We assume access to a computationally complex sim…
NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction
Yi Ding, Avinash Rao, Hyebin Song +2
Datacenters execute large computational jobs, which are composed of smaller tasks. A job completes when all its tasks finish, so stragglers -- rare, yet extremely slow tasks -- are…
The Role of Linear Layers in Nonlinear Interpolating Networks
Greg Ongie, Rebecca Willett
This paper explores the implicit bias of overparameterized neural networks of depth greater than two layers. Our framework considers a family of networks of varying depth that all…
Adaptive Differentially Private Empirical Risk Minimization
Xiaoxia Wu, Lingxiao Wang, Irina Cristali +2
We propose an adaptive (stochastic) gradient perturbation method for differentially private empirical risk minimization. At each iteration, the random noise added to the gradient i…
A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case
Greg Ongie, Rebecca Willett, Daniel Soudry +1
A key element of understanding the efficacy of overparameterized neural networks is characterizing how they represent functions as the number of weights in the network approaches i…
Bilinear Bandits with Low-rank Structure
Kwang-Sung Jun, Rebecca Willett, Stephen Wright +1
We introduce the bilinear bandit problem with low-rank structure in which an action takes the form of a pair of arms from two different entity types, and the reward is a bilinear f…
Relax but stay in control: from value to algorithms for online Markov decision processes
Peng Guan, Maxim Raginsky, Rebecca Willett
Online learning algorithms are designed to perform in non-stationary environments, but generally there is no notion of a dynamic state to model constraints on current and future ac…
Sequential anomaly detection in the presence of noise and limited feedback
Maxim Raginsky, Rebecca Willett, Corinne Horn +2
This paper describes a methodology for detecting anomalies from sequentially observed and potentially noisy data. The proposed approach consists of two main elements: (1) {\em filt…