Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference Book Abstract: It draws a diverse group of attendees--physicists, neuroscientists, mathematicians, statisticians, and computer scientists--interested in theoretical and applied aspects of modeling, simulating, and building neural-like or intelligent systems. The annual Neural Information Processing (NIPS) conference is the flagship meeting on neural computation. The conference is interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and diverse applications. Bibliographic details on Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. h�bbd``b`� �� �+�`Q �y �p �� Proceedings of the 2002 Neural Information Processing Systems Conference. %%EOF The Thirtieth Annual Conference on Neural Information Processing Systems (NIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstration Papers presented at the 2003 Neural Information Processing Conference by leading physicists, neuroscientists, mathematicians, statisticians, and computer scientists. ABSTRACT. Most Frequent Affiliations. Request PDF | On Jan 1, 2005, H.P. The conference is interdisciplinary, with contributions inalgorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing,reinforcement learning and control, implementations, and diverse applications. Compra Advances in Neural Information Processing Systems 15: Proceedings of the 2002 Conference (MIT Press) (2003-09-26). The annual Neural Information Processing (NIPS) meeting is the flagship conference on neural computation. Wei Chen, Tie-Yan Liu, and Zhi-Ming Ma, Two-Layer Generalization Analysis for Ranking Using Rademacher Average, Advances in Neural Information Processing Systems 23 (NeurIPS), Pages 370-378, 2010. Advances in Neural Information Processing Systems 31 (NeurIPS 2018) Advances in Neural Information Processing Systems 30 (NIPS 2017) Advances in Neural Information Processing Systems 29 (NIPS 2016) Advances in Neural Information Processing Systems 28 (NIPS 2015) Advances in Neural Information Processing Systems 27 (NIPS 2014) The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation and machine learning. 2017. Sign up for an account to create a profile with publication list, tag and review your related work, and share bibliographies with your co-authors. Theoretical advancement is expected to drive greater system performance improvement, ... and instigate ML researchers to contribute to advances in speaker recognition. The conference is interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and diverse applications. Online control of the false discovery rate with decaying memory, Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes, Imagination-Augmented Agents for Deep Reinforcement Learning, Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations, Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning, Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra, Asynchronous Parallel Coordinate Minimization for MAP Inference, Multiscale Quantization for Fast Similarity Search, Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space, Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods, Training Quantized Nets: A Deeper Understanding, Permutation-based Causal Inference Algorithms with Interventions, Time-dependent spatially varying graphical models, with application to brain fMRI data analysis, Gradient Methods for Submodular Maximization, Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization, The Importance of Communities for Learning to Influence, Multiplicative Weights Update with Constant Step-Size in Congestion Games: Convergence, Limit Cycles and Chaos, Learning Neural Representations of Human Cognition across Many fMRI Studies, A KL-LUCB algorithm for Large-Scale Crowdsourcing, Collaborative Deep Learning in Fixed Topology Networks, Learning Disentangled Representations with Semi-Supervised Deep Generative Models, Self-Supervised Intrinsic Image Decomposition, Exploring Generalization in Deep Learning, A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control, Fader Networks: Manipulating Images by Sliding Attributes, Estimating Mutual Information for Discrete-Continuous Mixtures, Parameter-Free Online Learning via Model Selection, Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction, Unbounded cache model for online language modeling with open vocabulary, Predictive State Recurrent Neural Networks, Early stopping for kernel boosting algorithms: A general analysis with localized complexities, SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability, Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein's Lemma, A Learning Error Analysis for Structured Prediction with Approximate Inference, Efficient Second-Order Online Kernel Learning with Adaptive Embedding, Implicit Regularization in Matrix Factorization, Optimal Shrinkage of Singular Values Under Random Data Contamination, Countering Feedback Delays in Multi-Agent Learning, Asynchronous Coordinate Descent under More Realistic Assumptions, Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls, Hierarchical Clustering Beyond the Worst-Case, Invariance and Stability of Deep Convolutional Representations, The Expressive Power of Neural Networks: A View from the Width, Spectrally-normalized margin bounds for neural networks, Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes, Population Matching Discrepancy and Applications in Deep Learning, Scalable Planning with Tensorflow for Hybrid Nonlinear Domains, Learned in Translation: Contextualized Word Vectors, Scalable Log Determinants for Gaussian Process Kernel Learning, Poincaré Embeddings for Learning Hierarchical Representations, Learning Combinatorial Optimization Algorithms over Graphs, Learning with Bandit Feedback in Potential Games, Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments, Communication-Efficient Distributed Learning of Discrete Distributions, Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles, When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness, Matrix Norm Estimation from a Few Entries, Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons, Causal Effect Inference with Deep Latent-Variable Models, Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity, Gradient Episodic Memory for Continual Learning, Effective Parallelisation for Machine Learning, Semisupervised Clustering, AND-Queries and Locally Encodable Source Coding, Clustering Stable Instances of Euclidean k-means, Good Semi-supervised Learning That Requires a Bad GAN, On Blackbox Backpropagation and Jacobian Sensing, Protein Interface Prediction using Graph Convolutional Networks, Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities, Towards Generalization and Simplicity in Continuous Control, Random Projection Filter Bank for Time Series Data, On Frank-Wolfe and Equilibrium Computation, Modulating early visual processing by language, Learning Mixture of Gaussians with Streaming Data, Practical Hash Functions for Similarity Estimation and Dimensionality Reduction, GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium, The Scaling Limit of High-Dimensional Online Independent Component Analysis, The power of absolute discounting: all-dimensional distribution estimation, Spectral Mixture Kernels for Multi-Output Gaussian Processes, Learning Linear Dynamical Systems via Spectral Filtering, Z-Forcing: Training Stochastic Recurrent Networks, Learning Hierarchical Information Flow with Recurrent Neural Modules, Neural Variational Inference and Learning in Undirected Graphical Models, The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process, Structured Bayesian Pruning via Log-Normal Multiplicative Noise, Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin, Acceleration and Averaging in Stochastic Descent Dynamics, Kernel functions based on triplet comparisons, An Error Detection and Correction Framework for Connectomics, Style Transfer from Non-Parallel Text by Cross-Alignment, Stochastic Submodular Maximization: The Case of Coverage Functions, Affinity Clustering: Hierarchical Clustering at Scale, Unsupervised Transformation Learning via Convex Relaxations, A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening, Linear Time Computation of Moments in Sum-Product Networks, A Meta-Learning Perspective on Cold-Start Recommendations for Items, Predicting Scene Parsing and Motion Dynamics in the Future, Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference, Efficient Approximation Algorithms for Strings Kernel Based Sequence Classification, Kernel Feature Selection via Conditional Covariance Minimization, Convergence of Gradient EM on Multi-component Mixture of Gaussians, Real Time Image Saliency for Black Box Classifiers, Houdini: Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples, Efficient and Flexible Inference for Stochastic Systems, When Cyclic Coordinate Descent Outperforms Randomized Coordinate Descent, Experimental Design for Learning Causal Graphs with Latent Variables, Stochastic Mirror Descent in Variationally Coherent Optimization Problems, On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models, A General Framework for Robust Interactive Learning, Multi-view Matrix Factorization for Linear Dynamical System Estimation. The complete twelve-volume proceedings of the Neural Information Processing Systems conferences from 1988 to 1999 on CD-ROM. Bartlett, Peter, Pereira, Fernando, Burges, Christopher, Bottou, Leon, & Weinberger, Kilian (Eds.) Is Input Sparsity Time Possible for Kernel Low-Rank Approximation? In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs. ISBN:9781510860964 Pages:7,102 (10 Vols) Format:Softcover TOC:View Table of Contents Publ:Neural Information Processing Systems Foundation, Inc. ( NIPS ) POD Publ:Curran Associates, Inc. ( Jun 2018 ) h�b```�Df�-������ Graf and others published Advances in Neural Information Processing Systems | Find, read and cite all the research you need on ResearchGate Wei Chen, Tie-Yan Liu, Yanyan Lan, and Zhi-Ming Ma, Ranking Measures and Loss functions in Learning to Rank, Advances in Neural Information Processing Systems 22 ( NeurIPS ), … The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. endstream endobj startxref Submission Deadline Tuesday 26 Jun 2018 Proceedings indexed by : Conference Dates Dec 3, 2018 - Dec 6, 2018 Conference Address Palais … It draws a diverse group of attendees—physicists, neuroscientists, mathematicians, statisticians, and computer scientists—interested in theoretical and applied aspects of modeling, simulating, and building neural-like or intelligent systems. 0 Most Cited Authors. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? The NIPS*2001 Conference Proceedings, Advances in Neural Information Processing Systems 14, edited by Thomas G. Dietterich, Sue Becker, and Zoubin Ghahramani, will be available to Conference attendees in three different formats. First, the complete Proceedings will … (ISBN: 9780262561457) from Amazon's Book Store. The annual conference on Neural Information Processing Systems (NIPS) is the flagshipconference on neural computation. |�r���ʌ-B��`ͮ5�N�9��Mfe˽�Ո�u|���ز��Z�=]���h We present a formulation of CNNs in the context of spectral graph theory, which provides the … Subject areas are listed below in brief, and in full here. Advances in Neural Information Processing Systems 31 / [a cura di] S. Bengio, H.M. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, R. Garnett. Proceedings of a meeting held 12-14 December 2011, Granada, Spain. ��_te���w���,&RL����s$m%P��O-0i"8��*�M��2�v�hzg1�*����,>��``�l�g���)yrA��-�v���O�T� K�3�f��=x�)e`iQ��2��E+X�,��2. �Yi;F�&.�0n��Lt��p�ɂنo/�ɁFF>FW�� The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. �'P��~���B�V;00�3���a���@�a*� Do Deep Neural Networks Suffer from Crowding? Compra [(Advances in Neural Information Processing Systems: v. 11 : Proceedings of the 1998 Conference)] [Edited by Michael S. Kearns ] published on (July, … �%� Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference. It draws preeminent academic researchers from around the world and is widely considered to be a showcase conference for new developments in network algorithms and architectures. The conference draws a diverse group of attendees—physicists, neuroscientists, mathematicians, statisticians, and computer scientists—and the presentations are interdisciplinary, with contributions in … NIPS'14: Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2 Generative adversarial nets. 196 0 obj <>stream Gradient descent GAN optimization is locally stable, Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks, Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model. The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. Neural Information Processing Systems (NIPS) 2008. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 12 Proceedings of the 1999 Conference edited by Sara A. Solla, Todd K. Leen and Klaus-Robert Müller A Bradford Book The MIT Press Cambridge, Massachusetts London, England Series:Advances in Neural Information Processing Systems 30 Editor:Von Luxburg, U. et al. Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, Roman Garnett, editors, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, USA. It draws a diverse group of attendees—physicists, neuroscientists, mathematicians, statisticians, and computer scientists. Vishwanathan 31st Annual Conference on Neural Information Processing Systems Read Advances in Neural Information Processing Systems: v. 8: Proceedings of the Advances in Neural Information Processing Systems 30 Long Beach, California, USA 4-9 December 2017 Editors: Ulrike von Luxburg Samy Bengio Rob Fergus Roman Garnett Isabelle Guyon Hanna Wallach S.V.N. Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, USA, Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning, Concentration of Multilinear Functions of the Ising Model with Applications to Network Data, Attentional Pooling for Action Recognition, Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization, Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis, Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs, Scalable Generalized Linear Bandits: Online Computation and Hashing, Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models, Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent, Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning, Learning to See Physics via Visual De-animation, Label Efficient Learning of Transferable Representations acrosss Domains and Tasks, Decoding with Value Networks for Neural Machine Translation, Parametric Simplex Method for Sparse Learning, Uprooting and Rerooting Higher-Order Graphical Models, The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings, From Parity to Preference-based Notions of Fairness in Classification, Inferring Generative Model Structure with Static Analysis, Structured Embedding Models for Grouped Data, A Linear-Time Kernel Goodness-of-Fit Test, Cortical microcircuits as gated-recurrent neural networks, k-Support and Ordered Weighted Sparsity for Overlapping Groups: Hardness and Algorithms, A simple model of recognition and recall memory, On Structured Prediction Theory with Calibrated Convex Surrogate Losses, Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model, MaskRNN: Instance Level Video Object Segmentation, Gated Recurrent Convolution Neural Network for OCR, Towards Accurate Binary Convolutional Neural Network, Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks, Phase Transitions in the Pooled Data Problem, Universal Style Transfer via Feature Transforms, On the Model Shrinkage Effect of Gamma Process Edge Partition Models, Inference in Graphical Models via Semidefinite Programming Hierarchies, Preventing Gradient Explosions in Gated Recurrent Units, On the Power of Truncated SVD for General High-rank Matrix Estimation Problems, f-GANs in an Information Geometric Nutshell, Toward Multimodal Image-to-Image Translation, Mixture-Rank Matrix Approximation for Collaborative Filtering, Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms, Learning multiple visual domains with residual adapters, Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions, Learning Spherical Convolution for Fast Features from 360° Imagery, MarrNet: 3D Shape Reconstruction via 2.5D Sketches, Multimodal Learning and Reasoning for Visual Question Answering, Adversarial Surrogate Losses for Ordinal Regression, Hypothesis Transfer Learning via Transformation Functions, Controllable Invariance through Adversarial Feature Learning, Convergence Analysis of Two-layer Neural Networks with ReLU Activation, Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization, Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks, Efficient Online Linear Optimization with Approximation Algorithms, Geometric Descent Method for Convex Composite Minimization, Diffusion Approximations for Online Principal Component Estimation and Global Convergence, Avoiding Discrimination through Causal Reasoning, Nonparametric Online Regression while Learning the Metric, Recycling Privileged Learning and Distribution Matching for Fairness, Safe and Nested Subgame Solving for Imperfect-Information Games, Unsupervised Image-to-Image Translation Networks, Coded Distributed Computing for Inverse Problems, A Screening Rule for l1-Regularized Ising Model Estimation, Improved Dynamic Regret for Non-degenerate Functions, Learning Efficient Object Detection Models with Knowledge Distillation, Deep Mean-Shift Priors for Image Restoration, Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees, Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes, Lower bounds on the robustness to adversarial perturbations, Minimizing a Submodular Function from Samples, Introspective Classification with Convolutional Nets, Unsupervised learning of object frames by dense equivariant image labelling, Compression-aware Training of Deep Networks, Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces, PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs, Detrended Partial Cross Correlation for Brain Connectivity Analysis, Contrastive Learning for Image Captioning, Safe Model-based Reinforcement Learning with Stability Guarantees, Matching on Balanced Nonlinear Representations for Treatment Effects Estimation, GP CaKe: Effective brain connectivity with causal kernels, Decoupling "when to update" from "how to update", Learning to Pivot with Adversarial Networks, SchNet: A continuous-filter convolutional neural network for modeling quantum interactions, Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples, Differentiable Learning of Submodular Functions, Inductive Representation Learning on Large Graphs, Subset Selection and Summarization in Sequential Data, Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces, Gradient Descent Can Take Exponential Time to Escape Saddle Points, Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction, Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding, Integration Methods and Optimization Algorithms, Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition, Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations, Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data, Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications, Predictive-State Decoders: Encoding the Future into Recurrent Networks, Optimistic posterior sampling for reinforcement learning: worst-case regret bounds, Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results, Matching neural paths: transfer from recognition to correspondence search, Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data, Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets, Learning to Inpaint for Image Compression, Adaptive Bayesian Sampling with Monte Carlo EM, ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization, Flexible statistical inference for mechanistic models of neural dynamics, Learning Unknown Markov Decision Processes: A Thompson Sampling Approach, Testing and Learning on Distributions with Symmetric Noise Invariance, A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering, Deanonymization in the Bitcoin P2P Network, Accelerated consensus via Min-Sum Splitting, Generalized Linear Model Regression under Distance-to-set Penalties, Adaptive stimulus selection for optimizing neural population responses, Nonbacktracking Bounds on the Influence in Independent Cascade Models, Online Convex Optimization with Stochastic Constraints, Max-Margin Invariant Features from Transformed Unlabelled Data, Regularized Modal Regression with Applications in Cognitive Impairment Prediction, Translation Synchronization via Truncated Least Squares, A New Alternating Direction Method for Linear Programming, Regret Analysis for Continuous Dueling Bandit, TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning, Learning Affinity via Spatial Propagation Networks, NeuralFDR: Learning Discovery Thresholds from Hypothesis Features, Probabilistic Rule Realization and Selection, Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions, A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis, Learning Multiple Tasks with Multilinear Relationship Networks, Online to Offline Conversions, Universality and Adaptive Minibatch Sizes, Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure, Deep Learning with Topological Signatures, Predicting User Activity Level In Point Processes With Mass Transport Equation, Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues, Positive-Unlabeled Learning with Non-Negative Risk Estimator, Optimal Sample Complexity of M-wise Data for Top-K Ranking, What-If Reasoning using Counterfactual Gaussian Processes, QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding, Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks, Train longer, generalize better: closing the generalization gap in large batch training of neural networks, Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks, Model evidence from nonequilibrium simulations, Minimal Exploration in Structured Stochastic Bandits, Learned D-AMP: Principled Neural Network based Compressive Image Recovery, Deliberation Networks: Sequence Generation Beyond One-Pass Decoding, Adaptive Clustering through Semidefinite Programming, Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning, Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search, Learning Chordal Markov Networks via Branch and Bound, Revenue Optimization with Approximate Bid Predictions, Solving Most Systems of Random Quadratic Equations, Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data, Lookahead Bayesian Optimization with Inequality Constraints, Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts, Revisit Fuzzy Neural Network: Demystifying Batch Normalization and ReLU with Generalized Hamming Network, Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization, Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models, Generating steganographic images via adversarial training, Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration, Consistent Multitask Learning with Nonlinear Output Relations, Alternating minimization for dictionary learning with random initialization, Stabilizing Training of Generative Adversarial Networks through Regularization, Expectation Propagation with Stochastic Kinetic Model in Complex Interaction Systems, Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs, Compatible Reward Inverse Reinforcement Learning, First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization, Hiding Images in Plain Sight: Deep Steganography, Bayesian Dyadic Trees and Histograms for Regression, A graph-theoretic approach to multitasking, Natural Value Approximators: Learning when to Trust Past Estimates, Bandits Dueling on Partially Ordered Sets, Elementary Symmetric Polynomials for Optimal Experimental Design, Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols, Training Deep Networks without Learning Rates Through Coin Betting, Pixels to Graphs by Associative Embedding, Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks, MMD GAN: Towards Deeper Understanding of Moment Matching Network, The Reversible Residual Network: Backpropagation Without Storing Activations, Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe, Expectation Propagation for t-Exponential Family Using q-Algebra, Few-Shot Learning Through an Information Retrieval Lens, Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation, Associative Embedding: End-to-End Learning for Joint Detection and Grouping, Large-Scale Quadratically Constrained Quadratic Program via Low-Discrepancy Sequences, Inhomogeneous Hypergraph Clustering with Applications, Differentiable Learning of Logical Rules for Knowledge Base Reasoning, Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks, Masked Autoregressive Flow for Density Estimation, Non-convex Finite-Sum Optimization Via SCSG Methods, Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting, An inner-loop free solution to inverse problems using deep neural networks, OnACID: Online Analysis of Calcium Imaging Data in Real Time, Fast Black-box Variational Inference through Stochastic Trust-Region Optimization, SGD Learns the Conjugate Kernel Class of the Network, Noise-Tolerant Interactive Learning Using Pairwise Comparisons, Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems, Generative Local Metric Learning for Kernel Regression, Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications, Fitting Low-Rank Tensors in Constant Time, Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation, How regularization affects the critical points in linear networks, Information-theoretic analysis of generalization capability of learning algorithms, Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems, Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System, Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM, EX2: Exploration with Exemplar Models for Deep Reinforcement Learning, Multitask Spectral Learning of Weighted Automata, Multi-way Interacting Regression via Factorization Machines, Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network, Practical Data-Dependent Metric Compression with Provable Guarantees, REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models, Nonlinear random matrix theory for deep learning, Parallel Streaming Wasserstein Barycenters, ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games, Dual Discriminator Generative Adversarial Nets, Decomposition-Invariant Conditional Gradient for General Polytopes with Line Search, VAIN: Attentional Multi-agent Predictive Modeling, An Empirical Bayes Approach to Optimizing Machine Learning Algorithms, Differentially Private Empirical Risk Minimization Revisited: Faster and More General, Variational Inference via \chi Upper Bound Minimization, On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning, #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning, An Empirical Study on The Properties of Random Bases for Kernel Methods, Bridging the Gap Between Value and Policy Based Reinforcement Learning, Premise Selection for Theorem Proving by Deep Graph Embedding, A Bayesian Data Augmentation Approach for Learning Deep Models, Principles of Riemannian Geometry in Neural Networks, Cold-Start Reinforcement Learning with Softmax Policy Gradient, Alternating Estimation for Structured High-Dimensional Multi-Response Models, Estimation of the covariance structure of heavy-tailed distributions, Mean Field Residual Networks: On the Edge of Chaos, Decomposable Submodular Function Minimization: Discrete and Continuous, Deep Recurrent Neural Network-Based Identification of Precursor microRNAs, Robust Estimation of Neural Signals in Calcium Imaging, Beyond Parity: Fairness Objectives for Collaborative Filtering, A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent, Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach, Model-Powered Conditional Independence Test, Deep Voice 2: Multi-Speaker Neural Text-to-Speech, Variance-based Regularization with Convex Objectives, Deep Lattice Networks and Partial Monotonic Functions, Continual Learning with Deep Generative Replay, AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms, Learning Causal Structures Using Regression Invariance, Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback, Near Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem, Reinforcement Learning under Model Mismatch, Hierarchical Attentive Recurrent Tracking, Tomography of the London Underground: a Scalable Model for Origin-Destination Data, Unbiased estimates for linear regression via volume sampling, Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search, Adaptive Accelerated Gradient Converging Method under H\"{o}lderian Error Bound Condition, Stein Variational Gradient Descent as Gradient Flow, Partial Hard Thresholding: Towards A Principled Analysis of Support Recovery, Shallow Updates for Deep Reinforcement Learning, LightGBM: A Highly Efficient Gradient Boosting Decision Tree, Adversarial Ranking for Language Generation, Regret Minimization in MDPs with Options without Prior Knowledge, Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee, Graph Matching via Multiplicative Update Algorithm, Dynamic Importance Sampling for Anytime Bounds of the Partition Function, Generalization Properties of Learning with Random Features, Differentially private Bayesian learning on distributed data, Learning to Compose Domain-Specific Transformations for Data Augmentation, Wasserstein Learning of Deep Generative Point Process Models, Language Modeling with Recurrent Highway Hypernetworks, Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter, Streaming Sparse Gaussian Process Approximations, VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning, A Regularized Framework for Sparse and Structured Neural Attention, Multi-output Polynomial Networks and Factorization Machines, Clustering Billions of Reads for DNA Data Storage, Multi-Objective Non-parametric Sequential Prediction, A Universal Analysis of Large-Scale Regularized Least Squares Solutions, ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events, Process-constrained batch Bayesian optimisation, Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes, Spherical convolutions and their application in molecular modelling, Efficient Optimization for Linear Dynamical Systems with Applications to Clustering and Sparse Coding, On Optimal Generalizability in Parametric Learning, Near Optimal Sketching of Low-Rank Tensor Regression, Tractability in Structured Probability Spaces, Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit, Gaussian process based nonlinear latent structure discovery in multivariate spike train data, Neural system identification for large populations separating "what" and "where", Certified Defenses for Data Poisoning Attacks, Eigen-Distortions of Hierarchical Representations, Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization, Unsupervised Sequence Classification using Sequential Output Statistics, Adaptive Batch Size for Safe Policy Gradients, A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning, PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference, Off-policy evaluation for slate recommendation, A multi-agent reinforcement learning model of common-pool resource appropriation, On the Optimization Landscape of Tensor Decompositions, High-Order Attention Models for Visual Question Answering, Sparse convolutional coding for neuronal assembly detection, Quantifying how much sensory information in a neural code is relevant for behavior, Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks, Reducing Reparameterization Gradient Variance, Visual Reference Resolution using Attention Memory for Visual Dialog, Joint distribution optimal transportation for domain adaptation, Multiresolution Kernel Approximation for Gaussian Process Regression, Collapsed variational Bayes for Markov jump processes, Universal consistency and minimax rates for online Mondrian Forests, Diving into the shallows: a computational perspective on large-scale shallow learning, Influence Maximization with ε-Almost Submodular Threshold Functions, InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations, Variational Laws of Visual Attention for Dynamic Scenes, Recursive Sampling for the Nystrom Method, Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning, Incorporating Side Information by Adaptive Convolution, Conic Scan-and-Cover algorithms for nonparametric topic modeling, FALKON: An Optimal Large Scale Kernel Method, Structured Generative Adversarial Networks, Variational Memory Addressing in Generative Models, On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm, Scalable Levy Process Priors for Spectral Kernel Learning, Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction, On-the-fly Operation Batching in Dynamic Computation Graphs, Nonlinear Acceleration of Stochastic Algorithms, Optimized Pre-Processing for Discrimination Prevention, Independence clustering (without a matrix), Fast amortized inference of neural activity from calcium imaging data with variational autoencoders, Adaptive Active Hypothesis Testing under Limited Information, Streaming Weak Submodularity: Interpreting Neural Networks on the Fly, Successor Features for Transfer in Reinforcement Learning, Prototypical Networks for Few-shot Learning, Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation, Mapping distinct timescales of functional interactions among brain networks, Multi-Armed Bandits with Metric Movement Costs, Learning A Structured Optimal Bipartite Graph for Co-Clustering, The Marginal Value of Adaptive Gradient Methods in Machine Learning, Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification, Deconvolutional Paragraph Representation Learning, Random Permutation Online Isotonic Regression, A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning, Inverse Filtering for Hidden Markov Models, Non-parametric Structured Output Networks, VAE Learning via Stein Variational Gradient Descent, Reconstructing perceived faces from brain activations with deep adversarial neural decoding, Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems, Temporal Coherency based Criteria for Predicting Video Frames using Deep Multi-stage Generative Adversarial Networks, Deep Reinforcement Learning from Human Preferences, On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks, Policy Gradient With Value Function Approximation For Collective Multiagent Planning, Adversarial Symmetric Variational Autoencoder, Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays, A Minimax Optimal Algorithm for Crowdsourcing, Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach, A Decomposition of Forecast Error in Prediction Markets, Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net, Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication, Unsupervised Learning of Disentangled Representations from Video. 12-14 December 2011, Granada, Spain Sparsity Time Possible for Kernel Low-Rank Approximation the. 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