1.1k citations · 7.8k across the 180 of their papers we have counts for
15 papers · 1 filter
InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers
Tan M. Nguyen, Animesh Garg, Richard G. Baraniuk +1
Continuous Normalizing Flows (CNFs) have emerged as promising deep generative models for a wide range of tasks thanks to their invertibility and exact likelihood estimation. Howeve…
Angular Visual Hardness
Beidi Chen, Weiyang Liu, Zhiding Yu +4
Recent convolutional neural networks (CNNs) have led to impressive performance but often suffer from poor calibration. They tend to be overconfident, with the model confidence not…
Triply Robust Off-Policy Evaluation
Anqi Liu, Hao Liu, Anima Anandkumar +1
We propose a robust regression approach to off-policy evaluation (OPE) for contextual bandits. We frame OPE as a covariate-shift problem and leverage modern robust regression tools…
Finding Social Media Trolls: Dynamic Keyword Selection Methods for Rapidly-Evolving Online Debates
Anqi Liu, Maya Srikanth, Nicholas Adams-Cohen +2
Online harassment is a significant social problem. Prevention of online harassment requires rapid detection of harassing, offensive, and negative social media posts. In this paper,…
Turbulence forecasting via Neural ODE
Gavin D. Portwood, Peetak P. Mitra, Mateus Dias Ribeiro +9
Fluid turbulence is characterized by strong coupling across a broad range of scales. Furthermore, besides the usual local cascades, such coupling may extend to interactions that ar…
Implicit competitive regularization in GANs
Florian Schäfer, Hongkai Zheng, Anima Anandkumar
To improve the stability of GAN training we need to understand why they can produce realistic samples. Presently, this is attributed to properties of the divergence obtained under…
Multi Sense Embeddings from Topic Models
Shobhit Jain, Sravan Babu Bodapati, Ramesh Nallapati +1
Distributed word embeddings have yielded state-of-the-art performance in many NLP tasks, mainly due to their success in capturing useful semantic information. These representations…
Out-of-Distribution Detection Using Neural Rendering Generative Models
Yujia Huang, Sihui Dai, Tan Nguyen +2
Out-of-distribution (OoD) detection is a natural downstream task for deep generative models, due to their ability to learn the input probability distribution. There are mainly two…
Directivity Modes of Earthquake Populations with Unsupervised Learning
Zachary E. Ross, Daniel T. Trugman, Kamyar Azizzadenesheli +1
We present a novel approach for resolving modes of rupture directivity in large populations of earthquakes. A seismic spectral decomposition technique is used to first produce rela…
Learning Causal State Representations of Partially Observable Environments
Amy Zhang, Zachary C. Lipton, Luis Pineda +5
Intelligent agents can cope with sensory-rich environments by learning task-agnostic state abstractions. In this paper, we propose an algorithm to approximate causal states, which…
Robust Regression for Safe Exploration in Control
Anqi Liu, Guanya Shi, Soon-Jo Chung +2
We study the problem of safe learning and exploration in sequential control problems. The goal is to safely collect data samples from operating in an environment, in order to learn…
Competitive Gradient Descent
Florian Schäfer, Anima Anandkumar
We introduce a new algorithm for the numerical computation of Nash equilibria of competitive two-player games. Our method is a natural generalization of gradient descent to the two…
Regularized Learning for Domain Adaptation under Label Shifts
Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang +1
We propose Regularized Learning under Label shifts (RLLS), a principled and a practical domain-adaptation algorithm to correct for shifts in the label distribution between a source…
Tensor Dropout for Robust Learning
Arinbjörn Kolbeinsson, Jean Kossaifi, Yannis Panagakis +4
CNNs achieve remarkable performance by leveraging deep, over-parametrized architectures, trained on large datasets. However, they have limited generalization ability to data outsid…
Stochastic Linear Bandits with Hidden Low Rank Structure
Sahin Lale, Kamyar Azizzadenesheli, Anima Anandkumar +1
High-dimensional representations often have a lower dimensional underlying structure. This is particularly the case in many decision making settings. For example, when the represen…