
OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation
Realworld tasks often exhibit a compositional structure that contains a...
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Explore More and Improve Regret in Linear Quadratic Regulators
Stabilizing the unknown dynamics of a control system and minimizing regr...
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Unsupervised Controllable Generation with SelfTraining
Recent generative adversarial networks (GANs) are able to generate impre...
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Neural Networks with Recurrent Generative Feedback
Neural networks are vulnerable to input perturbations such as additive n...
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Automated SynthetictoReal Generalization
Models trained on synthetic images often face degraded generalization to...
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Deep Bayesian Quadrature Policy Optimization
We study the problem of obtaining accurate policy gradient estimates. Th...
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Learning compositional functions via multiplicative weight updates
Compositionality is a basic structural feature of both biological and ar...
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Competitive Policy Optimization
A core challenge in policy optimization in competitive Markov decision p...
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Competitive Mirror Descent
Constrained competitive optimization involves multiple agents trying to ...
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Multipole Graph Neural Operator for Parametric Partial Differential Equations
One of the main challenges in using deep learningbased methods for simu...
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ChanceConstrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems
Learningbased control algorithms require collection of abundant supervi...
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MeshfreeFlowNet: A PhysicsConstrained Deep Continuous SpaceTime SuperResolution Framework
We propose MeshfreeFlowNet, a novel deep learningbased superresolution...
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Spectral Learning on Matrices and Tensors
Spectral methods have been the mainstay in several domains such as machi...
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Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems
We study the problem of adaptive control in partially observable linear ...
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Regret Bound of Adaptive Control in Linear Quadratic Gaussian (LQG) Systems
We study the problem of adaptive control in partially observable linear ...
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Neural Operator: Graph Kernel Network for Partial Differential Equations
The classical development of neural networks has been primarily for mapp...
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SemiSupervised StyleGAN for Disentanglement Learning
Disentanglement learning is crucial for obtaining disentangled represent...
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Regret Minimization in Partially Observable Linear Quadratic Control
We study the problem of regret minimization in partially observable line...
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InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers
Continuous Normalizing Flows (CNFs) have emerged as promising deep gener...
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Angular Visual Hardness
Although convolutional neural networks (CNNs) are inspired by the mechan...
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Triply Robust OffPolicy Evaluation
We propose a robust regression approach to offpolicy evaluation (OPE) f...
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Finding Social Media Trolls: Dynamic Keyword Selection Methods for RapidlyEvolving Online Debates
Online harassment is a significant social problem. Prevention of online ...
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Memory Augmented Recursive Neural Networks
Recursive neural networks have shown an impressive performance for model...
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Implicit competitive regularization in GANs
Generative adversarial networks (GANs) are capable of producing high qua...
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Multi Sense Embeddings from Topic Models
Distributed word embeddings have yielded stateoftheart performance in...
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OutofDistribution Detection Using Neural Rendering Generative Models
Outofdistribution (OoD) detection is a natural downstream task for dee...
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Directivity Modes of Earthquake Populations with Unsupervised Learning
We present a novel approach for resolving modes of rupture directivity i...
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Learning Causal State Representations of Partially Observable Environments
Intelligent agents can cope with sensoryrich environments by learning t...
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Robust Regression for Safe Exploration in Control
We study the problem of safe learning and exploration in sequential cont...
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Competitive Gradient Descent
We introduce a new algorithm for the numerical computation of Nash equil...
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Regularized Learning for Domain Adaptation under Label Shifts
We propose Regularized Learning under Label shifts (RLLS), a principled ...
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Stochastically RankRegularized Tensor Regression Networks
Overparametrization of deep neural networks has recently been shown to ...
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Multidimensional Tensor Sketch
Sketching refers to a class of randomized dimensionality reduction metho...
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Stochastic Linear Bandits with Hidden Low Rank Structure
Highdimensional representations often have a lower dimensional underlyi...
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Neural Lander: Stable Drone Landing Control using Learned Dynamics
Precise trajectory control near ground is difficult for multirotor dron...
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Neural Rendering Model: Joint Generation and Prediction for SemiSupervised Learning
Unsupervised and semisupervised learning are important problems that ar...
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Open Vocabulary Learning on Source Code with a GraphStructured Cache
Machine learning models that take computer program source code as input ...
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Trust Region Policy Optimization of POMDPs
We propose Generalized Trust Region Policy Optimization (GTRPO), a Reinf...
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signSGD with Majority Vote is Communication Efficient And Byzantine Fault Tolerant
Training neural networks on large datasets can be accelerated by distrib...
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SampleEfficient Deep RL with Generative Adversarial Tree Search
We propose Generative Adversarial Tree Search (GATS), a sampleefficient...
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Probabilistic FastText for MultiSense Word Embeddings
We introduce Probabilistic FastText, a new model for word embeddings tha...
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Born Again Neural Networks
Knowledge distillation (KD) consists of transferring knowledge from one ...
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Question Type Guided Attention in Visual Question Answering
Visual Question Answering (VQA) requires integration of feature maps wit...
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Stochastic Activation Pruning for Robust Adversarial Defense
Neural networks are known to be vulnerable to adversarial examples. Care...
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Active Learning with Partial Feedback
In the largescale multiclass setting, assigning labels often consists o...
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signSGD: compressed optimisation for nonconvex problems
Training large neural networks requires distributing learning across mul...
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Efficient Exploration through Bayesian Deep QNetworks
We propose Bayesian Deep QNetwork (BDQN), a practical Thompson sampling...
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Combining Symbolic and Function Evaluation Expressions In Neural Programs
Neural programming involves training neural networks to learn programs f...
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Learning From Noisy Singlylabeled Data
Supervised learning depends on annotated examples, which are taken to be...
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StrassenNets: Deep learning with a multiplication budget
A large fraction of the arithmetic operations required to evaluate deep ...
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Anima Anandkumar
verfied profile
Bren Professor at Caltech and Principal Scientist at NVIDIA