Advances in Neural Information Processing Systems

Publication Venue For
 Directed Spectral Measures Improve Latent Network Models Of Neural Populations.. 34:74217435. 2021
 A dictionary approach to domaininvariant learning in deep networks. 2020December. 2020
 A universal approximation theorem of deep neural networks for expressing probability distributions. 2020December. 2020
 Beyond lazy training for overparameterized tensor decomposition. 2020December. 2020
 Explaining landscape connectivity of lowcost solutions for multilayer nets. 32. 2019
 Improving textual network learning with variational homophilic embeddings. 32. 2019
 Kernelbased approaches for sequence modeling: Connections to neural methods. 32. 2019
 Spiderboost and momentum: Faster stochastic variance reduction algorithms. 32. 2019
 Learning bounds for greedy approximation with explicit feature maps from multiple kernels. 2018December:46904701. 2018
 Adversarial symmetric variational autoencoder. 2017December:43314340. 2017
 Deconvolutional paragraph representation learning. 2017December:41704180. 2017
 On Optimal Generalizability in Parametric Learning. 30. 2017
 On optimal generalizability in parametric learning. 2017December:34563466. 2017
 Demixing odors  Fast inference in olfaction 2013
 Dynamic clustering via asymptotics of the dependent Dirichlet process mixture 2013
 Efficient supervised sparse analysis and synthesis operators 2013
 Integrated nonfactorized variational inference 2013
 Locally adaptive bayesian multivariate time series 2013
 Multiscale dictionary learning for estimating conditional distributions 2013
 Robust multimodal graph matching: Sparse coding meets graph matching 2013
 Augmentandconquer negative binomial processes. 4:25462554. 2012
 Complex inference in neural circuits with probabilistic population codes and topic models. 4:30593067. 2012
 Finding exemplars from pairwise dissimilarities via simultaneous sparse recovery. 1:1927. 2012
 Joint modeling of a matrix with associated text via latent binary features. 2:15561564. 2012
 Modelling reciprocating relationships with Hawkes processes. 4:26002608. 2012
 Multiresolution Gaussian processes. 1:737745. 2012
 Provable ICA with unknown Gaussian noise, with implications for Gaussian mixtures and autoencoders. 3:23752383. 2012
 Repulsive mixtures. 3:18891897. 2012
 Topology constraints in graphical models. 1:791799. 2012
 Generalized Beta Mixtures of Gaussians. 24:523531. 2011
 A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation.. 2009:486494. 2009
 Stratification learning: Detecting mixed density and dimensionality in high dimensional point clouds. 553560. 2007
 Radial basis function network for multitask learning. 795802. 2005
 A Regression Approach to LearningAugmented Online Algorithms 2021
 Bubblewrap: Online tiling and realtime flow prediction on neural manifolds 2021
 CAMGAN: Continual Adaptation Modules for Generative Adversarial Networks 2021
 FLWBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective 2021
 Learning Markov State Abstractions for Deep Reinforcement Learning 2021
 On the Representation of Solutions to Elliptic PDEs in Barron Spaces 2021
 Robust Allocations with Diversity Constraints 2021
 Robust Counterfactual Explanations on Graph Neural Networks 2021
 Supercharging Imbalanced Data Learning With Energybased Contrastive Representation Transfer 2021
 Understanding Deflation Process in Overparametrized Tensor Decomposition 2021
 A finitetime analysis of two timescale actorcritic methods 2020
 Adaptive probing policies for shortest path routing 2020
 AutoSync: Learning to synchronize for dataparallel distributed deep learning 2020
 Benchmarking deep inverse models over time, and the neuraladjoint method 2020
 Calibrating CNNs for lifelong learning 2020
 GAN memory with no forgetting 2020
 Instancebased generalization in reinforcement learning 2020
 Online neural connectivity estimation with noisy group testing 2020
 Perturbing across the feature hierarchy to improve standard and strict blackbox attack transferability 2020
 Reconsidering generative objectives for counterfactual reasoning 2020
 Capacity bounded differential privacy 2019
 Certified adversarial robustness with additive noise 2019
 Defending neural backdoors via generative distribution modeling 2019
 Gradient information for representation and modeling 2019
 On fenchel minimax learning 2019
 Optimal sparse decision trees 2019
 Ouroboros: On accelerating training of transformerbased language models 2019
 Reward constrained interactive recommendation with natural language feedback 2019
 Scalable gromovwasserstein learning for graph partitioning and matching 2019
 Stochastic gradient hamiltonian monte carlo methods with recursive variance reduction 2019
 The step decay schedule: A near optimal, geometrically decaying learning rate procedure for least squares 2019
 This looks like that: Deep learning for interpretable image recognition 2019
 Adversarial text generation via featuremover's distance 2018
 Beyond logconcavity: Provable guarantees for sampling multimodal distributions using simulated tempering langevin Monte Carlo 2018
 Diffusion maps for textual network embedding 2018
 Distilled Wasserstein learning for word embedding and topic modeling 2018
 Generalized inverse optimization through online learning 2018
 Global convergence of Langevin dynamics based algorithms for nonconvex optimization 2018
 Informationbased adaptive stimulus selection to optimize communication efficiency in braincomputer interfaces 2018
 On the local minima of the empirical risk 2018
 Stochastic nested variance reduction for nonconvex optimization 2018
 Thirdorder smoothness helps: Faster stochastic optimization algorithms for finding local minima 2018
 A probabilistic framework for nonlinearities in stochastic neural networks 2017
 A screening rule for ℓ1regularized ising model estimation 2017
 ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching 2017
 ALICE: Towards understanding adversarial learning for joint distribution matching 2017
 Active exploration for learning symbolic representations 2017
 Adversarial Symmetric Variational Autoencoder 2017
 An Innerloop Free Solution to Inverse Problems using Deep Neural Networks 2017
 Crossspectral factor analysis 2017
 On the optimization landscape of tensor decompositions 2017
 Robust and efficient transfer learning with hidden parameter Markov decision processes 2017
 Scalable model selection for belief networks 2017
 Speeding up latent variable Gaussian graphical model estimation via nonconvex optimization 2017
 Targeting EEG/LFP synchrony with neural nets 2017
 TernGrad: Ternary gradients to reduce communication in distributed deep learning 2017
 Triangle generative adversarial networks 2017
 VAE Learning via Stein Variational Gradient Descent 2017
 VAE learning via Stein variational gradient descent 2017
 DECOrrelated feature space partitioning for distributed sparse regression 2016
 Flexible models for microclustering with application to entity resolution 2016
 Learning structured sparsity in deep neural networks 2016
 Linear feature encoding for reinforcement learning 2016
 Matrix completion has no spurious local minimum 2016
 Semiparametric differential graph models 2016
 Stochastic gradient MCMC with stale gradients 2016
 Towards unifying hamiltonian Monte Carlo and Slice sampling 2016
 Variational autoencoder for deep learning of images, labels and captions 2016
 Deep poisson factor modeling 2015
 Discriminative robust transformation learning 2015
 Fast secondorder stochastic backpropagation for variational inference 2015
 Largescale Bayesian multilabel learning via topicbased label embeddings 2015
 On the consistency theory of high dimensional variable screening 2015
 On the convergence of stochastic gradient MCMC algorithms with highorder integrators 2015
 Parallelizing MCMC with random partition trees 2015
 Policy evaluation using the Ωreturn 2015
 Probabilistic curve learning: Coulomb repulsion and the electrostatic Gaussian process 2015
 Bayesian nonlinear support vector machines and discriminative factor modeling 2014
 Compressive sensing of signals from a GMM with sparse precision matrices 2014
 Dynamic rank factor model for text streams 2014
 Median selection subset aggregation for parallel inference 2014
 The Bayesian case model: A generative approach for casebased reasoning and prototype classification 2014
 Bayesian Estimation of Latentlygrouped Parameters in Undirected Graphical Models. 2013
 A graphical transformation for belief propagation: Maximum Weight Matchings and oddsized cycles 2013
 An integer optimization approach to associative classification 2012
 Multiplicative forests for continuoustime processes 2012
 On semisupervised classification 2005
 On the dynamics of boosting 2004