| (Almost) No Label No Cry |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A Bayesian model for identifying hierarchically organised states in neural population activity |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Block-Coordinate Descent Approach for Large-scale Sparse Inverse Covariance Estimation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| A Boosting Framework on Grounds of Online Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Complete Variational Tracker |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Differential Equation for Modeling Nesterov’s Accelerated Gradient Method: Theory and Insights |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Drifting-Games Analysis for Online Learning and Applications to Boosting |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Dual Algorithm for Olfactory Computation in the Locust Brain |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A Filtering Approach to Stochastic Variational Inference |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Framework for Testing Identifiability of Bayesian Models of Perception |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A Latent Source Model for Online Collaborative Filtering |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| A Multiplicative Model for Learning Distributed Text-Based Attribute Representations |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Probabilistic Framework for Multimodal Retrieval using Integrative Indian Buffet Process |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Representation Theory for Ranking Functions |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| A Residual Bootstrap for High-Dimensional Regression with Near Low-Rank Designs |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Safe Screening Rule for Sparse Logistic Regression |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| A State-Space Model for Decoding Auditory Attentional Modulation from MEG in a Competing-Speaker Environment |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| A Statistical Decision-Theoretic Framework for Social Choice |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A Synaptical Story of Persistent Activity with Graded Lifetime in a Neural System |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A Unified Semantic Embedding: Relating Taxonomies and Attributes |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Wild Bootstrap for Degenerate Kernel Tests |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| A framework for studying synaptic plasticity with neural spike train data |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A provable SVD-based algorithm for learning topics in dominant admixture corpus |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| A statistical model for tensor PCA |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| A* Sampling |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Accelerated Mini-batch Randomized Block Coordinate Descent Method |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Active Learning and Best-Response Dynamics |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Active Regression by Stratification |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Advances in Learning Bayesian Networks of Bounded Treewidth |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Algorithm selection by rational metareasoning as a model of human strategy selection |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Algorithms for CVaR Optimization in MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Altitude Training: Strong Bounds for Single-Layer Dropout |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| An Accelerated Proximal Coordinate Gradient Method |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| An Autoencoder Approach to Learning Bilingual Word Representations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| An Integer Polynomial Programming Based Framework for Lifted MAP Inference |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Analog Memories in a Balanced Rate-Based Network of E-I Neurons |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Analysis of Brain States from Multi-Region LFP Time-Series |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| Analysis of Learning from Positive and Unlabeled Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Approximating Hierarchical MV-sets for Hierarchical Clustering |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS) |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Asynchronous Anytime Sequential Monte Carlo |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Attentional Neural Network: Feature Selection Using Cognitive Feedback |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Augur: Data-Parallel Probabilistic Modeling |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Automated Variational Inference for Gaussian Process Models |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Automatic Discovery of Cognitive Skills to Improve the Prediction of Student Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Bandit Convex Optimization: Towards Tight Bounds |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Bayes-Adaptive Simulation-based Search with Value Function Approximation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Bayesian Inference for Structured Spike and Slab Priors |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Bayesian Sampling Using Stochastic Gradient Thermostats |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Best-Arm Identification in Linear Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Beyond Disagreement-Based Agnostic Active Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Beyond the Birkhoff Polytope: Convex Relaxations for Vector Permutation Problems |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
3 |
| Biclustering Using Message Passing |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Blossom Tree Graphical Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Bounded Regret for Finite-Armed Structured Bandits |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bregman Alternating Direction Method of Multipliers |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Causal Inference through a Witness Protection Program |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Causal Strategic Inference in Networked Microfinance Economies |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Clamping Variables and Approximate Inference |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Clustered factor analysis of multineuronal spike data |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Clustering from Labels and Time-Varying Graphs |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Combinatorial Pure Exploration of Multi-Armed Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Communication Efficient Distributed Machine Learning with the Parameter Server |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Communication-Efficient Distributed Dual Coordinate Ascent |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Compressive Sensing of Signals from a GMM with Sparse Precision Matrices |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Computing Nash Equilibria in Generalized Interdependent Security Games |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Concavity of reweighted Kikuchi approximation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Conditional Random Field Autoencoders for Unsupervised Structured Prediction |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Conditional Swap Regret and Conditional Correlated Equilibrium |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Cone-Constrained Principal Component Analysis |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Consistency of weighted majority votes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Consistent Binary Classification with Generalized Performance Metrics |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Constrained convex minimization via model-based excessive gap |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Content-based recommendations with Poisson factorization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Controlling privacy in recommender systems |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Convex Deep Learning via Normalized Kernels |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Convex Optimization Procedure for Clustering: Theoretical Revisit |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Convolutional Kernel Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Convolutional Neural Network Architectures for Matching Natural Language Sentences |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Coresets for k-Segmentation of Streaming Data |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Covariance shrinkage for autocorrelated data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| DFacTo: Distributed Factorization of Tensors |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
5 |
| Decomposing Parameter Estimation Problems |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Deconvolution of High Dimensional Mixtures via Boosting, with Application to Diffusion-Weighted MRI of Human Brain |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Decoupled Variational Gaussian Inference |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Convolutional Neural Network for Image Deconvolution |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Fragment Embeddings for Bidirectional Image Sentence Mapping |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Deep Joint Task Learning for Generic Object Extraction |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Deep Learning Face Representation by Joint Identification-Verification |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Deep Networks with Internal Selective Attention through Feedback Connections |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Deep Recursive Neural Networks for Compositionality in Language |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Deep Symmetry Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Dependent nonparametric trees for dynamic hierarchical clustering |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Depth Map Prediction from a Single Image using a Multi-Scale Deep Network |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Design Principles of the Hippocampal Cognitive Map |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Deterministic Symmetric Positive Semidefinite Matrix Completion |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Difference of Convex Functions Programming for Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Dimensionality Reduction with Subspace Structure Preservation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Discovering Structure in High-Dimensional Data Through Correlation Explanation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Discovering, Learning and Exploiting Relevance |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Discrete Graph Hashing |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Discriminative Metric Learning by Neighborhood Gerrymandering |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Discriminative Unsupervised Feature Learning with Convolutional Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Distance-Based Network Recovery under Feature Correlation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Distributed Balanced Clustering via Mapping Coresets |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Distributed Bayesian Posterior Sampling via Moment Sharing |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Distributed Estimation, Information Loss and Exponential Families |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Distributed Parameter Estimation in Probabilistic Graphical Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Distributed Power-law Graph Computing: Theoretical and Empirical Analysis |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Diverse Randomized Agents Vote to Win |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Diverse Sequential Subset Selection for Supervised Video Summarization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Divide-and-Conquer Learning by Anchoring a Conical Hull |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Do Convnets Learn Correspondence? |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Do Deep Nets Really Need to be Deep? |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
3 |
| Dynamic Rank Factor Model for Text Streams |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Efficient Minimax Signal Detection on Graphs |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient Minimax Strategies for Square Loss Games |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Efficient Optimization for Average Precision SVM |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Efficient Partial Monitoring with Prior Information |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Sampling for Learning Sparse Additive Models in High Dimensions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient Structured Matrix Rank Minimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient learning by implicit exploration in bandit problems with side observations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Elementary Estimators for Graphical Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Estimation with Norm Regularization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Exact Post Model Selection Inference for Marginal Screening |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Exclusive Feature Learning on Arbitrary Structures via $\ell_{1,2}$-norm |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Expectation-Maximization for Learning Determinantal Point Processes |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Exploiting easy data in online optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Exponential Concentration of a Density Functional Estimator |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Extended and Unscented Gaussian Processes |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Extracting Certainty from Uncertainty: Transductive Pairwise Classification from Pairwise Similarities |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Extracting Latent Structure From Multiple Interacting Neural Populations |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Extremal Mechanisms for Local Differential Privacy |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Extreme bandits |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Factoring Variations in Natural Images with Deep Gaussian Mixture Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Fairness in Multi-Agent Sequential Decision-Making |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Fast Kernel Learning for Multidimensional Pattern Extrapolation |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
2 |
| Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Fast Prediction for Large-Scale Kernel Machines |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fast Sampling-Based Inference in Balanced Neuronal Networks |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fast Training of Pose Detectors in the Fourier Domain |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Fast and Robust Least Squares Estimation in Corrupted Linear Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Feature Cross-Substitution in Adversarial Classification |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Feedback Detection for Live Predictors |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Feedforward Learning of Mixture Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Finding a sparse vector in a subspace: Linear sparsity using alternating directions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Flexible Transfer Learning under Support and Model Shift |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
3 |
| From MAP to Marginals: Variational Inference in Bayesian Submodular Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| From Stochastic Mixability to Fast Rates |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Gaussian Process Volatility Model |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| General Stochastic Networks for Classification |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| General Table Completion using a Bayesian Nonparametric Model |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Generalized Dantzig Selector: Application to the k-support norm |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Generalized Unsupervised Manifold Alignment |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Generative Adversarial Nets |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Global Belief Recursive Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Global Sensitivity Analysis for MAP Inference in Graphical Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Graph Clustering With Missing Data: Convex Algorithms and Analysis |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Greedy Subspace Clustering |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Grouping-Based Low-Rank Trajectory Completion and 3D Reconstruction |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Hardness of parameter estimation in graphical models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| How hard is my MDP?" The distribution-norm to the rescue" |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| How transferable are features in deep neural networks? |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Identifying and attacking the saddle point problem in high-dimensional non-convex optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improved Distributed Principal Component Analysis |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improved Multimodal Deep Learning with Variation of Information |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Incremental Clustering: The Case for Extra Clusters |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Incremental Local Gaussian Regression |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Inference by Learning: Speeding-up Graphical Model Optimization via a Coarse-to-Fine Cascade of Pruning Classifiers |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Inferring synaptic conductances from spike trains with a biophysically inspired point process model |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Information-based learning by agents in unbounded state spaces |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Iterative Neural Autoregressive Distribution Estimator NADE-k |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
3 |
| Just-In-Time Learning for Fast and Flexible Inference |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Kernel Mean Estimation via Spectral Filtering |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| LSDA: Large Scale Detection through Adaptation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Large-Margin Convex Polytope Machine |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Large-scale L-BFGS using MapReduce |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Latent Support Measure Machines for Bag-of-Words Data Classification |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Chordal Markov Networks by Dynamic Programming |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Deep Features for Scene Recognition using Places Database |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Distributed Representations for Structured Output Prediction |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning From Weakly Supervised Data by The Expectation Loss SVM (e-SVM) algorithm |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Generative Models with Visual Attention |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Learning Mixed Multinomial Logit Model from Ordinal Data |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Mixtures of Ranking Models |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning Mixtures of Submodular Functions for Image Collection Summarization |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Multiple Tasks in Parallel with a Shared Annotator |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Optimal Commitment to Overcome Insecurity |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Shuffle Ideals Under Restricted Distributions |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Time-Varying Coverage Functions |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning a Concept Hierarchy from Multi-labeled Documents |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning convolution filters for inverse covariance estimation of neural network connectivity |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning on graphs using Orthonormal Representation is Statistically Consistent |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Learning the Learning Rate for Prediction with Expert Advice |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning to Discover Efficient Mathematical Identities |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning to Optimize via Information-Directed Sampling |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning to Search in Branch and Bound Algorithms |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Learning with Fredholm Kernels |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Learning with Pseudo-Ensembles |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Local Decorrelation For Improved Pedestrian Detection |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Local Linear Convergence of Forward--Backward under Partial Smoothness |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Low Rank Approximation Lower Bounds in Row-Update Streams |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Low-Rank Time-Frequency Synthesis |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Low-dimensional models of neural population activity in sensory cortical circuits |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Magnitude-sensitive preference formation` |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Making Pairwise Binary Graphical Models Attractive |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Median Selection Subset Aggregation for Parallel Inference |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Message Passing Inference for Large Scale Graphical Models with High Order Potentials |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Metric Learning for Temporal Sequence Alignment |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Mind the Nuisance: Gaussian Process Classification using Privileged Noise |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Minimax-optimal Inference from Partial Rankings |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Mode Estimation for High Dimensional Discrete Tree Graphical Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Model-based Reinforcement Learning and the Eluder Dimension |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Modeling Deep Temporal Dependencies with Recurrent Grammar Cells"" |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Mondrian Forests: Efficient Online Random Forests |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Multi-Class Deep Boosting |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-Resolution Cascades for Multiclass Object Detection |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multi-Scale Spectral Decomposition of Massive Graphs |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-scale Graphical Models for Spatio-Temporal Processes |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Multiscale Fields of Patterns |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multitask learning meets tensor factorization: task imputation via convex optimization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Multivariate Regression with Calibration |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Multivariate f-divergence Estimation With Confidence |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Near-Optimal Density Estimation in Near-Linear Time Using Variable-Width Histograms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Near-Optimal-Sample Estimators for Spherical Gaussian Mixtures |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Near-optimal Reinforcement Learning in Factored MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Near-optimal sample compression for nearest neighbors |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Neural Word Embedding as Implicit Matrix Factorization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| New Rules for Domain Independent Lifted MAP Inference |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Non-convex Robust PCA |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Nonparametric Bayesian inference on multivariate exponential families |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Object Localization based on Structural SVM using Privileged Information |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On Communication Cost of Distributed Statistical Estimation and Dimensionality |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On Integrated Clustering and Outlier Detection |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Iterative Hard Thresholding Methods for High-dimensional M-Estimation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Model Parallelization and Scheduling Strategies for Distributed Machine Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| On Multiplicative Multitask Feature Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| On Prior Distributions and Approximate Inference for Structured Variables |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On Sparse Gaussian Chain Graph Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On a Theory of Nonparametric Pairwise Similarity for Clustering: Connecting Clustering to Classification |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Computational Efficiency of Training Neural Networks |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
2 |
| On the Convergence Rate of Decomposable Submodular Function Minimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Information Theoretic Limits of Learning Ising Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Number of Linear Regions of Deep Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| On the relations of LFPs & Neural Spike Trains |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Online Decision-Making in General Combinatorial Spaces |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Optimization for Max-Norm Regularization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online and Stochastic Gradient Methods for Non-decomposable Loss Functions |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Online combinatorial optimization with stochastic decision sets and adversarial losses |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Optimal Neural Codes for Control and Estimation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Optimal Regret Minimization in Posted-Price Auctions with Strategic Buyers |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimal Teaching for Limited-Capacity Human Learners |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimal decision-making with time-varying evidence reliability |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Optimal prior-dependent neural population codes under shared input noise |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Optimal rates for k-NN density and mode estimation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimistic Planning in Markov Decision Processes Using a Generative Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Optimizing Energy Production Using Policy Search and Predictive State Representations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimizing F-Measures by Cost-Sensitive Classification |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Orbit Regularization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| PAC-Bayesian AUC classification and scoring |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| PEWA: Patch-based Exponentially Weighted Aggregation for image denoising |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Parallel Direction Method of Multipliers |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Parallel Double Greedy Submodular Maximization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Parallel Feature Selection Inspired by Group Testing |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Parallel Sampling of HDPs using Sub-Cluster Splits |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Partition-wise Linear Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Permutation Diffusion Maps (PDM) with Application to the Image Association Problem in Computer Vision |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Positive Curvature and Hamiltonian Monte Carlo |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Pre-training of Recurrent Neural Networks via Linear Autoencoders |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Predicting Useful Neighborhoods for Lazy Local Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Predictive Entropy Search for Efficient Global Optimization of Black-box Functions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Probabilistic Differential Dynamic Programming |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Probabilistic ODE Solvers with Runge-Kutta Means |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Probabilistic low-rank matrix completion on finite alphabets |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Projecting Markov Random Field Parameters for Fast Mixing |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Projective dictionary pair learning for pattern classification |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Provable Submodular Minimization using Wolfe's Algorithm |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Provable Tensor Factorization with Missing Data |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Quantized Estimation of Gaussian Sequence Models in Euclidean Balls |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Quantized Kernel Learning for Feature Matching |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Randomized Experimental Design for Causal Graph Discovery |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Ranking via Robust Binary Classification |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Rates of Convergence for Nearest Neighbor Classification |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Real-Time Decoding of an Integrate and Fire Encoder |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Recovery of Coherent Data via Low-Rank Dictionary Pursuit |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Recurrent Models of Visual Attention |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Recursive Context Propagation Network for Semantic Scene Labeling |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Recursive Inversion Models for Permutations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reducing the Rank in Relational Factorization Models by Including Observable Patterns |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Repeated Contextual Auctions with Strategic Buyers |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Reputation-based Worker Filtering in Crowdsourcing |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Restricted Boltzmann machines modeling human choice |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Robust Bayesian Max-Margin Clustering |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Robust Classification Under Sample Selection Bias |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
3 |
| Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Robust Logistic Regression and Classification |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust Tensor Decomposition with Gross Corruption |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Rounding-based Moves for Metric Labeling |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Scalable Inference for Neuronal Connectivity from Calcium Imaging |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Scalable Kernel Methods via Doubly Stochastic Gradients |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Scalable Non-linear Learning with Adaptive Polynomial Expansions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Scale Adaptive Blind Deblurring |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Scaling-up Importance Sampling for Markov Logic Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Searching for Higgs Boson Decay Modes with Deep Learning |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| Self-Adaptable Templates for Feature Coding |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Self-Paced Learning with Diversity |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Semi-supervised Learning with Deep Generative Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Sensory Integration and Density Estimation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Sequence to Sequence Learning with Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sequential Monte Carlo for Graphical Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| SerialRank: Spectral Ranking using Seriation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Shape and Illumination from Shading using the Generic Viewpoint Assumption |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Shaping Social Activity by Incentivizing Users |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Simple MAP Inference via Low-Rank Relaxations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Smoothed Gradients for Stochastic Variational Inference |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sparse Bayesian structure learning with “dependent relevance determination” priors |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Sparse Multi-Task Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sparse PCA via Covariance Thresholding |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Sparse PCA with Oracle Property |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Sparse Polynomial Learning and Graph Sketching |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sparse Space-Time Deconvolution for Calcium Image Analysis |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Spatio-temporal Representations of Uncertainty in Spiking Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Spectral Clustering of graphs with the Bethe Hessian |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Spectral Learning of Mixture of Hidden Markov Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Spectral Methods for Indian Buffet Process Inference |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Spectral Methods for Supervised Topic Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Spectral k-Support Norm Regularization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Spike Frequency Adaptation Implements Anticipative Tracking in Continuous Attractor Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Stochastic Multi-Armed-Bandit Problem with Non-stationary Rewards |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Stochastic Network Design in Bidirected Trees |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Stochastic Proximal Gradient Descent with Acceleration Techniques |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic variational inference for hidden Markov models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Streaming, Memory Limited Algorithms for Community Detection |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Structure Regularization for Structured Prediction |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Structure learning of antiferromagnetic Ising models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Submodular Attribute Selection for Action Recognition in Video |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Subspace Embeddings for the Polynomial Kernel |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Testing Unfaithful Gaussian Graphical Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Blinded Bandit: Learning with Adaptive Feedback |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Infinite Mixture of Infinite Gaussian Mixtures |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| The Large Margin Mechanism for Differentially Private Maximization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Noisy Power Method: A Meta Algorithm with Applications |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The limits of squared Euclidean distance regularization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Tight Continuous Relaxation of the Balanced k-Cut Problem |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Tight convex relaxations for sparse matrix factorization |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
1 |
| Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Time--Data Tradeoffs by Aggressive Smoothing |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Top Rank Optimization in Linear Time |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Transportability from Multiple Environments with Limited Experiments: Completeness Results |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Tree-structured Gaussian Process Approximations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Two-Stream Convolutional Networks for Action Recognition in Videos |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Universal Option Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Unsupervised Deep Haar Scattering on Graphs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Unsupervised Transcription of Piano Music |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Unsupervised learning of an efficient short-term memory network |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Using Convolutional Neural Networks to Recognize Rhythm Stimuli from Electroencephalography Recordings |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Variational Gaussian Process State-Space Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Weakly-supervised Discovery of Visual Pattern Configurations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Weighted importance sampling for off-policy learning with linear function approximation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Zero-shot recognition with unreliable attributes |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Zeta Hull Pursuits: Learning Nonconvex Data Hulls |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| large scale canonical correlation analysis with iterative least squares |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |