| A Bayesian nonparametric procedure for comparing algorithms |
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| A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models |
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| A Convex Optimization Framework for Bi-Clustering |
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| A Deeper Look at Planning as Learning from Replay |
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| A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data |
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| A Divide and Conquer Framework for Distributed Graph Clustering |
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| A Fast Variational Approach for Learning Markov Random Field Language Models |
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| A General Analysis of the Convergence of ADMM |
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| A Hybrid Approach for Probabilistic Inference using Random Projections |
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| A Linear Dynamical System Model for Text |
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| A Lower Bound for the Optimization of Finite Sums |
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| A Modified Orthant-Wise Limited Memory Quasi-Newton Method with Convergence Analysis |
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| A Multitask Point Process Predictive Model |
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| A Nearly-Linear Time Framework for Graph-Structured Sparsity |
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| A New Generalized Error Path Algorithm for Model Selection |
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4 |
| A Probabilistic Model for Dirty Multi-task Feature Selection |
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4 |
| A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning |
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| A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits |
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3 |
| A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate |
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3 |
| A Theoretical Analysis of Metric Hypothesis Transfer Learning |
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| A Unified Framework for Outlier-Robust PCA-like Algorithms |
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3 |
| A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data |
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| A low variance consistent test of relative dependency |
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| A trust-region method for stochastic variational inference with applications to streaming data |
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| Abstraction Selection in Model-based Reinforcement Learning |
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| Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams |
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| Active Nearest Neighbors in Changing Environments |
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| Adaptive Belief Propagation |
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| Adaptive Stochastic Alternating Direction Method of Multipliers |
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| Adding vs. Averaging in Distributed Primal-Dual Optimization |
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| Algorithms for the Hard Pre-Image Problem of String Kernels and the General Problem of String Prediction |
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| Alpha-Beta Divergences Discover Micro and Macro Structures in Data |
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| An Aligned Subtree Kernel for Weighted Graphs |
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| An Asynchronous Distributed Proximal Gradient Method for Composite Convex Optimization |
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3 |
| An Empirical Exploration of Recurrent Network Architectures |
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| An Empirical Study of Stochastic Variational Inference Algorithms for the Beta Bernoulli Process |
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| An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection |
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| An Online Learning Algorithm for Bilinear Models |
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| An embarrassingly simple approach to zero-shot learning |
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| Approval Voting and Incentives in Crowdsourcing |
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| Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games |
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| Asymmetric Transfer Learning with Deep Gaussian Processes |
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| Atomic Spatial Processes |
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| Attribute Efficient Linear Regression with Distribution-Dependent Sampling |
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| Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift |
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| Bayesian Multiple Target Localization |
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| Bayesian and Empirical Bayesian Forests |
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| BilBOWA: Fast Bilingual Distributed Representations without Word Alignments |
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| Bimodal Modelling of Source Code and Natural Language |
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| Binary Embedding: Fundamental Limits and Fast Algorithm |
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| Bipartite Edge Prediction via Transductive Learning over Product Graphs |
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| Blitz: A Principled Meta-Algorithm for Scaling Sparse Optimization |
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| Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions |
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| Budget Allocation Problem with Multiple Advertisers: A Game Theoretic View |
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| CUR Algorithm for Partially Observed Matrices |
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| Cascading Bandits: Learning to Rank in the Cascade Model |
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| Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components |
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| Celeste: Variational inference for a generative model of astronomical images |
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| Cheap Bandits |
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| Classification with Low Rank and Missing Data |
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| Community Detection Using Time-Dependent Personalized PageRank |
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| Complete Dictionary Recovery Using Nonconvex Optimization |
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| Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM |
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| Compressing Neural Networks with the Hashing Trick |
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| Consistent Multiclass Algorithms for Complex Performance Measures |
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| Consistent estimation of dynamic and multi-layer block models |
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| Context-based Unsupervised Data Fusion for Decision Making |
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| Controversy in mechanistic modelling with Gaussian processes |
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| Convergence rate of Bayesian tensor estimator and its minimax optimality |
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| Convex Calibrated Surrogates for Hierarchical Classification |
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| Convex Formulation for Learning from Positive and Unlabeled Data |
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3 |
| Convex Learning of Multiple Tasks and their Structure |
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| Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection |
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| Coresets for Nonparametric Estimation - the Case of DP-Means |
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| Correlation Clustering in Data Streams |
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| Counterfactual Risk Minimization: Learning from Logged Bandit Feedback |
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| DP-space: Bayesian Nonparametric Subspace Clustering with Small-variance Asymptotics |
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| DRAW: A Recurrent Neural Network For Image Generation |
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| Dealing with small data: On the generalization of context trees |
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| Deep Edge-Aware Filters |
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| Deep Learning with Limited Numerical Precision |
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| Deep Unsupervised Learning using Nonequilibrium Thermodynamics |
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| Deterministic Independent Component Analysis |
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| DiSCO: Distributed Optimization for Self-Concordant Empirical Loss |
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| Differentially Private Bayesian Optimization |
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| Discovering Temporal Causal Relations from Subsampled Data |
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| Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM |
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| Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds |
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| Distributed Gaussian Processes |
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| Distributed Inference for Dirichlet Process Mixture Models |
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| Distributional Rank Aggregation, and an Axiomatic Analysis |
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| Double Nyström Method: An Efficient and Accurate Nyström Scheme for Large-Scale Data Sets |
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| Dynamic Sensing: Better Classification under Acquisition Constraints |
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| Efficient Learning in Large-Scale Combinatorial Semi-Bandits |
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| Efficient Training of LDA on a GPU by Mean-for-Mode Estimation |
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| Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE) |
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| Entropic Graph-based Posterior Regularization |
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| Entropy evaluation based on confidence intervals of frequency estimates : Application to the learning of decision trees |
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| Entropy-Based Concentration Inequalities for Dependent Variables |
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| Exponential Integration for Hamiltonian Monte Carlo |
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| Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods |
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| Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets |
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| Faster cover trees |
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| Feature-Budgeted Random Forest |
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| Fictitious Self-Play in Extensive-Form Games |
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| Finding Galaxies in the Shadows of Quasars with Gaussian Processes |
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| Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis |
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| Fixed-point algorithms for learning determinantal point processes |
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| Following the Perturbed Leader for Online Structured Learning |
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2 |
| From Word Embeddings To Document Distances |
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| Functional Subspace Clustering with Application to Time Series |
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4 |
| Gated Feedback Recurrent Neural Networks |
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3 |
| Generalization error bounds for learning to rank: Does the length of document lists matter? |
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| Generative Moment Matching Networks |
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5 |
| Geometric Conditions for Subspace-Sparse Recovery |
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| Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems |
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4 |
| Gradient-based Hyperparameter Optimization through Reversible Learning |
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5 |
| Guaranteed Tensor Decomposition: A Moment Approach |
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| Harmonic Exponential Families on Manifolds |
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3 |
| Hashing for Distributed Data |
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4 |
| HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades |
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2 |
| Hidden Markov Anomaly Detection |
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3 |
| High Confidence Policy Improvement |
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3 |
| High Dimensional Bayesian Optimisation and Bandits via Additive Models |
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4 |
| How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances? |
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| How Hard is Inference for Structured Prediction? |
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2 |
| Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning |
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1 |
| Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs |
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3 |
| Inference in a Partially Observed Queuing Model with Applications in Ecology |
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1 |
| Inferring Graphs from Cascades: A Sparse Recovery Framework |
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1 |
| Information Geometry and Minimum Description Length Networks |
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✅ |
5 |
| Intersecting Faces: Non-negative Matrix Factorization With New Guarantees |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Is Feature Selection Secure against Training Data Poisoning? |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| K-hyperplane Hinge-Minimax Classifier |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
4 |
| Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP) |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Landmarking Manifolds with Gaussian Processes |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Large-Scale Markov Decision Problems with KL Control Cost and its Application to Crowdsourcing |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Large-scale Distributed Dependent Nonparametric Trees |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Large-scale log-determinant computation through stochastic Chebyshev expansions |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning Deep Structured Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Fast-Mixing Models for Structured Prediction |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Local Invariant Mahalanobis Distances |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Parametric-Output HMMs with Two Aliased States |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Program Embeddings to Propagate Feedback on Student Code |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Scale-Free Networks by Dynamic Node Specific Degree Prior |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Submodular Losses with the Lovasz Hinge |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Learning Transferable Features with Deep Adaptation Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Word Representations with Hierarchical Sparse Coding |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning from Corrupted Binary Labels via Class-Probability Estimation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Search Better than Your Teacher |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Long Short-Term Memory Over Recursive Structures |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Low Rank Approximation using Error Correcting Coding Matrices |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Low-Rank Matrix Recovery from Row-and-Column Affine Measurements |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| MADE: Masked Autoencoder for Distribution Estimation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| MRA-based Statistical Learning from Incomplete Rankings |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Manifold-valued Dirichlet Processes |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Markov Chain Monte Carlo and Variational Inference: Bridging the Gap |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Markov Mixed Membership Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Message Passing for Collective Graphical Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Metadata Dependent Mondrian Processes |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Mind the duality gap: safer rules for the Lasso |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Modeling Order in Neural Word Embeddings at Scale |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Moderated and Drifting Linear Dynamical Systems |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-Task Learning for Subspace Segmentation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Multi-instance multi-label learning in the presence of novel class instances |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multiview Triplet Embedding: Learning Attributes in Multiple Maps |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Nested Sequential Monte Carlo Methods |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Non-Linear Cross-Domain Collaborative Filtering via Hyper-Structure Transfer |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Non-Stationary Approximate Modified Policy Iteration |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Off-policy Model-based Learning under Unknown Factored Dynamics |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Deep Multi-View Representation Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On Greedy Maximization of Entropy |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On Symmetric and Asymmetric LSHs for Inner Product Search |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Rate of Convergence and Error Bounds for LSTD(λ) |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Relationship between Sum-Product Networks and Bayesian Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Learning of Eigenvectors |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Time Series Prediction with Missing Data |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Optimal and Adaptive Algorithms for Online Boosting |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Optimizing Neural Networks with Kronecker-factored Approximate Curvature |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Optimizing Non-decomposable Performance Measures: A Tale of Two Classes |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Ordered Stick-Breaking Prior for Sequential MCMC Inference of Bayesian Nonparametric Models |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Ordinal Mixed Membership Models |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| PU Learning for Matrix Completion |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Phrase-based Image Captioning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Predictive Entropy Search for Bayesian Optimization with Unknown Constraints |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Pushing the Limits of Affine Rank Minimization by Adapting Probabilistic PCA |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Qualitative Multi-Armed Bandits: A Quantile-Based Approach |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Rademacher Observations, Private Data, and Boosting |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Random Coordinate Descent Methods for Minimizing Decomposable Submodular Functions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Ranking from Stochastic Pairwise Preferences: Recovering Condorcet Winners and Tournament Solution Sets at the Top |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Reified Context Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Removing systematic errors for exoplanet search via latent causes |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Risk and Regret of Hierarchical Bayesian Learners |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Robust Estimation of Transition Matrices in High Dimensional Heavy-tailed Vector Autoregressive Processes |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust partially observable Markov decision process |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Safe Exploration for Optimization with Gaussian Processes |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Safe Subspace Screening for Nuclear Norm Regularized Least Squares Problems |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Scalable Bayesian Optimization Using Deep Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Scalable Deep Poisson Factor Analysis for Topic Modeling |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Scalable Model Selection for Large-Scale Factorial Relational Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scalable Variational Inference in Log-supermodular Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Show, Attend and Tell: Neural Image Caption Generation with Visual Attention |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Simple regret for infinitely many armed bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sparse Subspace Clustering with Missing Entries |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sparse Variational Inference for Generalized GP Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Spectral Clustering via the Power Method - Provably |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Spectral MLE: Top-K Rank Aggregation from Pairwise Comparisons |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Stay on path: PCA along graph paths |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Stochastic Dual Coordinate Ascent with Adaptive Probabilities |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic Optimization with Importance Sampling for Regularized Loss Minimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Streaming Sparse Principal Component Analysis |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Strongly Adaptive Online Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Structural Maxent Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Submodularity in Data Subset Selection and Active Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Subsampling Methods for Persistent Homology |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Support Matrix Machines |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Surrogate Functions for Maximizing Precision at the Top |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Swept Approximate Message Passing for Sparse Estimation |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Telling cause from effect in deterministic linear dynamical systems |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Benefits of Learning with Strongly Convex Approximate Inference |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The Composition Theorem for Differential Privacy |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The Hedge Algorithm on a Continuum |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Kendall and Mallows Kernels for Permutations |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| The Ladder: A Reliable Leaderboard for Machine Learning Competitions |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| The Power of Randomization: Distributed Submodular Maximization on Massive Datasets |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Theory of Dual-sparse Regularized Randomized Reduction |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Threshold Influence Model for Allocating Advertising Budgets |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Towards a Learning Theory of Cause-Effect Inference |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Tracking Approximate Solutions of Parameterized Optimization Problems over Multi-Dimensional (Hyper-)Parameter Domains |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Training Deep Convolutional Neural Networks to Play Go |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Trust Region Policy Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Universal Value Function Approximators |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Unsupervised Domain Adaptation by Backpropagation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Unsupervised Learning of Video Representations using LSTMs |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Variational Generative Stochastic Networks with Collaborative Shaping |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Variational Inference for Gaussian Process Modulated Poisson Processes |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Variational Inference with Normalizing Flows |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Vector-Space Markov Random Fields via Exponential Families |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Weight Uncertainty in Neural Network |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| \ell_1,p-Norm Regularization: Error Bounds and Convergence Rate Analysis of First-Order Methods |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |