"Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions.Sliced Wasserstein Kernel for Persistence Diagrams.Multiple Clustering Views from Multiple Uncertain Experts.Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference.Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning.Robust Structured Estimation with Single-Index Models.Strong NP-Hardness for Sparse Optimization with Concave Penalty Functions.Learning to Learn without Gradient Descent by Gradient Descent.Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables.Toward Efficient and Accurate Covariance Matrix Estimation on Compressed Data.Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability.Learning to Aggregate Ordinal Labels by Maximizing Separating Width.MEC: Memory-efficient Convolution for Deep Neural Network.Improving Stochastic Policy Gradients in Continuous Control with Deep Reinforcement Learning using the Beta Distribution.Parseval Networks: Improving Robustness to Adversarial Examples.Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC.AdaNet: Adaptive Structural Learning of Artificial Neural Networks.Random Feature Expansions for Deep Gaussian Processes.Soft-DTW: a Differentiable Loss Function for Time-Series.Understanding Synthetic Gradients and Decoupled Neural Interfaces.Language Modeling with Gated Convolutional Networks.An Infinite Hidden Markov Model With Similarity-Biased Transitions.Consistency Analysis for Binary Classification Revisited.iSurvive: An Interpretable, Event-time Prediction Model for mHealth.Image-to-Markup Generation with Coarse-to-Fine Attention.RobustFill: Neural Program Learning under Noisy I/O.Being Robust (in High Dimensions) Can Be Practical.A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI.Stochastic Variance Reduction Methods for Policy Evaluation.Rule-Enhanced Penalized Regression by Column Generation using Rectangular Maximum Agreement.Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders.Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening.Maximum Selection and Ranking under Noisy Comparisons.Fake News Mitigation via Point Process Based Intervention.Regret Minimization in Behaviorally-Constrained Zero-Sum Games.Coresets for Vector Summarization with Applications to Network Graphs.Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks.Input Switched Affine Networks: An RNN Architecture Designed for Interpretability.Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning.Counterfactual Data-Fusion for Online Reinforcement Learners.Forward and Reverse Gradient-Based Hyperparameter Optimization.Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier.Local-to-Global Bayesian Network Structure Learning.Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis.Zonotope Hit-and-run for Efficient Sampling from Projection DPPs.No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis.Convex Phase Retrieval without Lifting via PhaseMax.ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices.Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs.Reinforcement Learning with Deep Energy-Based Policies.DeepBach: a Steerable Model for Bach Chorales Generation.Faster Greedy MAP Inference for Determinantal Point Processes.Data-Efficient Policy Evaluation Through Behavior Policy Search.Joint Dimensionality Reduction and Metric Learning: A Geometric Take.Deep IV: A Flexible Approach for Counterfactual Prediction.The Sample Complexity of Online One-Class Collaborative Filtering.Warped Convolutions: Efficient Invariance to Spatial Transformations.Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space.DARLA: Improving Zero-Shot Transfer in Reinforcement Learning.SPLICE: Fully Tractable Hierarchical Extension of ICA with Pooling.Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo.Prox-PDA: The Proximal Primal-Dual Algorithm for Fast Distributed Nonconvex Optimization and Learning Over Networks.Analysis and Optimization of Graph Decompositions by Lifted Multicuts.Dissipativity Theory for Nesterov's Accelerated Method.Learning Discrete Representations via Information Maximizing Self-Augmented Training.Deep Generative Models for Relational Data with Side Information.Variational Inference for Sparse and Undirected Models.Decoupled Neural Interfaces using Synthetic Gradients.Scalable Generative Models for Multi-label Learning with Missing Labels.Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control.Bayesian Optimization with Tree-structured Dependencies.Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation.From Patches to Images: A Nonparametric Generative Model.Density Level Set Estimation on Manifolds with DBSCAN.Uniform Convergence Rates for Kernel Density Estimation.Contextual Decision Processes with low Bellman rank are PAC-Learnable.Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs.An Adaptive Test of Independence with Analytic Kernel Embeddings.StingyCD: Safely Avoiding Wasteful Updates in Coordinate Descent.Differentially Private Chi-squared Test by Unit Circle Mechanism.Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP.Recursive Partitioning for Personalization using Observational Data.Multi-fidelity Bayesian Optimisation with Continuous Approximations.Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics.Meritocratic Fairness for Cross-Population Selection.On Approximation Guarantees for Greedy Low Rank Optimization.Graph-based Isometry Invariant Representation Learning.Learning to Discover Cross-Domain Relations with Generative Adversarial Networks.SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization.Understanding Black-box Predictions via Influence Functions.Sub-sampled Cubic Regularization for Non-convex Optimization.PixelCNN Models with Auxiliary Variables for Natural Image Modeling.Evaluating Bayesian Models with Posterior Dispersion Indices.Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things.Conditional Accelerated Lazy Stochastic Gradient Descent.Bayesian inference on random simple graphs with power law degree distributions.Deriving Neural Architectures from Sequence and Graph Kernels.Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization.Learning to Align the Source Code to the Compiled Object Code.Dropout Inference in Bayesian Neural Networks with Alpha-divergences.Provable Alternating Gradient Descent for Non-negative Matrix Factorization with Strong Correlations.Provably Optimal Algorithms for Generalized Linear Contextual Bandits.Fast k-Nearest Neighbour Search via Prioritized DCI.Forest-type Regression with General Losses and Robust Forest.Stochastic Modified Equations and Adaptive Stochastic Gradient Algorithms.Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization.Exact MAP Inference by Avoiding Fractional Vertices.Leveraging Union of Subspace Structure to Improve Constrained Clustering.Analogical Inference for Multi-relational Embeddings.Dual Iterative Hard Thresholding: From Non-convex Sparse Minimization to Non-smooth Concave Maximization.Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence Labelling.Learning Infinite Layer Networks Without the Kernel Trick.Deep Transfer Learning with Joint Adaptation Networks.Multiplicative Normalizing Flows for Variational Bayesian Neural Networks.How Close Are the Eigenvectors of the Sample and Actual Covariance Matrices?Learning Deep Architectures via Generalized Whitened Neural Networks.Learning Gradient Descent: Better Generalization and Longer Horizons.Spherical Structured Feature Maps for Kernel Approximation.Stochastic Gradient MCMC Methods for Hidden Markov Models.Interactive Learning from Policy-Dependent Human Feedback.A Laplacian Framework for Option Discovery in Reinforcement Learning.On Mixed Memberships and Symmetric Nonnegative Matrix Factorizations.Bayesian Models of Data Streams with Hierarchical Power Priors.Just Sort It!
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