| 1 |
Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning |
| 2 |
Concentration of Multilinear Functions of the Ising Model with Applications to Network Data |
| 3 |
Deep Subspace Clustering Networks |
| 4 |
Attentional Pooling for Action Recognition |
| 5 |
On the Consistency of Quick Shift |
| 6 |
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization |
| 7 |
Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis |
| 8 |
Dilated Recurrent Neural Networks |
| 9 |
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs |
| 10 |
Scalable Generalized Linear Bandits: Online Computation and Hashing |
| 11 |
Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models |
| 12 |
Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent |
| 13 |
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning |
| 14 |
Interactive Submodular Bandit |
| 15 |
Learning to See Physics via Visual De-animation |
| 16 |
Label Efficient Learning of Transferable Representations acrosss Domains and Tasks |
| 17 |
Decoding with Value Networks for Neural Machine Translation |
| 18 |
Parametric Simplex Method for Sparse Learning |
| 19 |
Group Sparse Additive Machine |
| 20 |
Uprooting and Rerooting Higher-Order Graphical Models |
| 21 |
The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings |
| 22 |
From Parity to Preference-based Notions of Fairness in Classification |
| 23 |
Inferring Generative Model Structure with Static Analysis |
| 24 |
Structured Embedding Models for Grouped Data |
| 25 |
A Linear-Time Kernel Goodness-of-Fit Test |
| 26 |
Cortical microcircuits as gated-recurrent neural networks |
| 27 |
k-Support and Ordered Weighted Sparsity for Overlapping Groups: Hardness and Algorithms |
| 28 |
A simple model of recognition and recall memory |
| 29 |
On Structured Prediction Theory with Calibrated Convex Surrogate Losses |
| 30 |
Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model |
| 31 |
MaskRNN: Instance Level Video Object Segmentation |
| 32 |
Gated Recurrent Convolution Neural Network for OCR |
| 33 |
Towards Accurate Binary Convolutional Neural Network |
| 34 |
Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks |
| 35 |
Learning a Multi-View Stereo Machine |
| 36 |
Phase Transitions in the Pooled Data Problem |
| 37 |
Universal Style Transfer via Feature Transforms |
| 38 |
On the Model Shrinkage Effect of Gamma Process Edge Partition Models |
| 39 |
Pose Guided Person Image Generation |
| 40 |
Inference in Graphical Models via Semidefinite Programming Hierarchies |
| 41 |
Variable Importance Using Decision Trees |
| 42 |
Preventing Gradient Explosions in Gated Recurrent Units |
| 43 |
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems |
| 44 |
f-GANs in an Information Geometric Nutshell |
| 45 |
Toward Multimodal Image-to-Image Translation |
| 46 |
Mixture-Rank Matrix Approximation for Collaborative Filtering |
| 47 |
Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms |
| 48 |
Learning with Average Top-k Loss |
| 49 |
Learning multiple visual domains with residual adapters |
| 50 |
Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions |
| 51 |
Learning Spherical Convolution for Fast Features from 360° Imagery |
| 52 |
MarrNet: 3D Shape Reconstruction via 2.5D Sketches |
| 53 |
Multimodal Learning and Reasoning for Visual Question Answering |
| 54 |
Adversarial Surrogate Losses for Ordinal Regression |
| 55 |
Hypothesis Transfer Learning via Transformation Functions |
| 56 |
Controllable Invariance through Adversarial Feature Learning |
| 57 |
Convergence Analysis of Two-layer Neural Networks with ReLU Activation |
| 58 |
Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization |
| 59 |
Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks |
| 60 |
Efficient Online Linear Optimization with Approximation Algorithms |
| 61 |
Geometric Descent Method for Convex Composite Minimization |
| 62 |
Diffusion Approximations for Online Principal Component Estimation and Global Convergence |
| 63 |
Avoiding Discrimination through Causal Reasoning |
| 64 |
Nonparametric Online Regression while Learning the Metric |
| 65 |
Recycling Privileged Learning and Distribution Matching for Fairness |
| 66 |
Safe and Nested Subgame Solving for Imperfect-Information Games |
| 67 |
Unsupervised Image-to-Image Translation Networks |
| 68 |
Coded Distributed Computing for Inverse Problems |
| 69 |
A Screening Rule for l1-Regularized Ising Model Estimation |
| 70 |
Improved Dynamic Regret for Non-degenerate Functions |
| 71 |
Learning Efficient Object Detection Models with Knowledge Distillation |
| 72 |
One-Sided Unsupervised Domain Mapping |
| 73 |
Deep Mean-Shift Priors for Image Restoration |
| 74 |
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees |
| 75 |
A New Theory for Matrix Completion |
| 76 |
Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes |
| 77 |
Lower bounds on the robustness to adversarial perturbations |
| 78 |
Minimizing a Submodular Function from Samples |
| 79 |
Introspective Classification with Convolutional Nets |
| 80 |
Label Distribution Learning Forests |
| 81 |
Unsupervised learning of object frames by dense equivariant image labelling |
| 82 |
Compression-aware Training of Deep Networks |
| 83 |
Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces |
| 84 |
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs |
| 85 |
Detrended Partial Cross Correlation for Brain Connectivity Analysis |
| 86 |
Contrastive Learning for Image Captioning |
| 87 |
Safe Model-based Reinforcement Learning with Stability Guarantees |
| 88 |
Online multiclass boosting |
| 89 |
Matching on Balanced Nonlinear Representations for Treatment Effects Estimation |
| 90 |
Learning Overcomplete HMMs |
| 91 |
GP CaKe: Effective brain connectivity with causal kernels |
| 92 |
Decoupling "when to update" from "how to update" |
| 93 |
Self-Normalizing Neural Networks |
| 94 |
Learning to Pivot with Adversarial Networks |
| 95 |
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions |
| 96 |
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples |
| 97 |
Differentiable Learning of Submodular Functions |
| 98 |
Inductive Representation Learning on Large Graphs |
| 99 |
Subset Selection and Summarization in Sequential Data |
| 100 |
Question Asking as Program Generation |
| 101 |
Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces |
| 102 |
Gradient Descent Can Take Exponential Time to Escape Saddle Points |
| 103 |
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction |
| 104 |
One-Shot Imitation Learning |
| 105 |
Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding |
| 106 |
Integration Methods and Optimization Algorithms |
| 107 |
Sharpness, Restart and Acceleration |
| 108 |
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition |
| 109 |
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations |
| 110 |
Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data |
| 111 |
Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications |
| 112 |
Predictive-State Decoders: Encoding the Future into Recurrent Networks |
| 113 |
Optimistic posterior sampling for reinforcement learning: worst-case regret bounds |
| 114 |
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results |
| 115 |
Matching neural paths: transfer from recognition to correspondence search |
| 116 |
Linearly constrained Gaussian processes |
| 117 |
Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data |
| 118 |
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets |
| 119 |
Learning to Inpaint for Image Compression |
| 120 |
Adaptive Bayesian Sampling with Monte Carlo EM |
| 121 |
ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization |
| 122 |
Shape and Material from Sound |
| 123 |
Flexible statistical inference for mechanistic models of neural dynamics |
| 124 |
Online Prediction with Selfish Experts |
| 125 |
Tensor Biclustering |
| 126 |
DPSCREEN: Dynamic Personalized Screening |
| 127 |
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach |
| 128 |
Testing and Learning on Distributions with Symmetric Noise Invariance |
| 129 |
A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering |
| 130 |
Deanonymization in the Bitcoin P2P Network |
| 131 |
Accelerated consensus via Min-Sum Splitting |
| 132 |
Generalized Linear Model Regression under Distance-to-set Penalties |
| 133 |
Adaptive stimulus selection for optimizing neural population responses |
| 134 |
Nonbacktracking Bounds on the Influence in Independent Cascade Models |
| 135 |
Learning with Feature Evolvable Streams |
| 136 |
Online Convex Optimization with Stochastic Constraints |
| 137 |
Max-Margin Invariant Features from Transformed Unlabelled Data |
| 138 |
Regularized Modal Regression with Applications in Cognitive Impairment Prediction |
| 139 |
Translation Synchronization via Truncated Least Squares |
| 140 |
From which world is your graph |
| 141 |
A New Alternating Direction Method for Linear Programming |
| 142 |
Regret Analysis for Continuous Dueling Bandit |
| 143 |
Best Response Regression |
| 144 |
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning |
| 145 |
Learning Affinity via Spatial Propagation Networks |
| 146 |
Linear regression without correspondence |
| 147 |
NeuralFDR: Learning Discovery Thresholds from Hypothesis Features |
| 148 |
Cost efficient gradient boosting |
| 149 |
Probabilistic Rule Realization and Selection |
| 150 |
Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions |
| 151 |
A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis |
| 152 |
Learning Multiple Tasks with Multilinear Relationship Networks |
| 153 |
Deep Hyperalignment |
| 154 |
Online to Offline Conversions, Universality and Adaptive Minibatch Sizes |
| 155 |
Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure |
| 156 |
Deep Learning with Topological Signatures |
| 157 |
Predicting User Activity Level In Point Processes With Mass Transport Equation |
| 158 |
Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues |
| 159 |
Deep Dynamic Poisson Factorization Model |
| 160 |
Positive-Unlabeled Learning with Non-Negative Risk Estimator |
| 161 |
Optimal Sample Complexity of M-wise Data for Top-K Ranking |
| 162 |
Counterfactual Gaussian Processes for Reliable Decision-making and What-if Reasoning |
| 163 |
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding |
| 164 |
Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks |
| 165 |
Train longer, generalize better: closing the generalization gap in large batch training of neural networks |
| 166 |
Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks |
| 167 |
Model evidence from nonequilibrium simulations |
| 168 |
Minimal Exploration in Structured Stochastic Bandits |
| 169 |
Learned D-AMP: Principled Neural Network based Compressive Image Recovery |
| 170 |
Deliberation Networks: Sequence Generation Beyond One-Pass Decoding |
| 171 |
Adaptive Clustering through Semidefinite Programming |
| 172 |
Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning |
| 173 |
Repeated Inverse Reinforcement Learning |
| 174 |
The Numerics of GANs |
| 175 |
Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search |
| 176 |
Learning Chordal Markov Networks via Branch and Bound |
| 177 |
Revenue Optimization with Approximate Bid Predictions |
| 178 |
Solving Most Systems of Random Quadratic Equations |
| 179 |
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data |
| 180 |
Lookahead Bayesian Optimization with Inequality Constraints |
| 181 |
Hierarchical Methods of Moments |
| 182 |
Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts |
| 183 |
Revisit Fuzzy Neural Network: Demystifying Batch Normalization and ReLU with Generalized Hamming Network |
| 184 |
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization |
| 185 |
Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models |
| 186 |
Generating steganographic images via adversarial training |
| 187 |
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration |
| 188 |
PixelGAN Autoencoders |
| 189 |
Consistent Multitask Learning with Nonlinear Output Relations |
| 190 |
Alternating minimization for dictionary learning with random initialization |
| 191 |
Learning ReLUs via Gradient Descent |
| 192 |
Stabilizing Training of Generative Adversarial Networks through Regularization |
| 193 |
Expectation Propagation with Stochastic Kinetic Model in Complex Interaction Systems |
| 194 |
Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs |
| 195 |
Compatible Reward Inverse Reinforcement Learning |
| 196 |
First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization |
| 197 |
Hiding Images in Plain Sight: Deep Steganography |
| 198 |
Neural Program Meta-Induction |
| 199 |
Bayesian Dyadic Trees and Histograms for Regression |
| 200 |
A graph-theoretic approach to multitasking |
| 201 |
Consistent Robust Regression |
| 202 |
Natural Value Approximators: Learning when to Trust Past Estimates |
| 203 |
Bandits Dueling on Partially Ordered Sets |
| 204 |
Elementary Symmetric Polynomials for Optimal Experimental Design |
| 205 |
Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols |
| 206 |
Training Deep Networks without Learning Rates Through Coin Betting |
| 207 |
Pixels to Graphs by Associative Embedding |
| 208 |
Runtime Neural Pruning |
| 209 |
Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks |
| 210 |
MMD GAN: Towards Deeper Understanding of Moment Matching Network |
| 211 |
The Reversible Residual Network: Backpropagation Without Storing Activations |
| 212 |
Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe |
| 213 |
Zap Q-Learning |
| 214 |
Expectation Propagation for t-Exponential Family Using q-Algebra |
| 215 |
Few-Shot Learning Through an Information Retrieval Lens |
| 216 |
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation |
| 217 |
Associative Embedding: End-to-End Learning for Joint Detection and Grouping |
| 218 |
Practical Locally Private Heavy Hitters |
| 219 |
Large-Scale Quadratically Constrained Quadratic Program via Low-Discrepancy Sequences |
| 220 |
Inhomogeneous Hypergraph Clustering with Applications |
| 221 |
Differentiable Learning of Logical Rules for Knowledge Base Reasoning |
| 222 |
Masked Autoregressive Flow for Density Estimation |
| 223 |
Non-convex Finite-Sum Optimization Via SCSG Methods |
| 224 |
Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting |
| 225 |
An inner-loop free solution to inverse problems using deep neural networks |
| 226 |
OnACID: Online Analysis of Calcium Imaging Data in Real Time |
| 227 |
Collaborative PAC Learning |
| 228 |
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization |
| 229 |
Scalable Demand-Aware Recommendation |
| 230 |
SGD Learns the Conjugate Kernel Class of the Network |
| 231 |
Noise-Tolerant Interactive Learning Using Pairwise Comparisons |
| 232 |
Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems |
| 233 |
Generative Local Metric Learning for Kernel Regression |
| 234 |
Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications |
| 235 |
Fitting Low-Rank Tensors in Constant Time |
| 236 |
Deep Supervised Discrete Hashing |
| 237 |
Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation |
| 238 |
How regularization affects the critical points in linear networks |
| 239 |
Fisher GAN |
| 240 |
Information-theoretic analysis of generalization capability of learning algorithms |
| 241 |
Sparse Approximate Conic Hulls |
| 242 |
Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems |
| 243 |
Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System |
| 244 |
Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM |
| 245 |
EX2: Exploration with Exemplar Models for Deep Reinforcement Learning |
| 246 |
Multitask Spectral Learning of Weighted Automata |
| 247 |
Multi-way Interacting Regression via Factorization Machines |
| 248 |
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network |
| 249 |
Practical Data-Dependent Metric Compression with Provable Guarantees |
| 250 |
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models |
| 251 |
Nonlinear random matrix theory for deep learning |
| 252 |
Parallel Streaming Wasserstein Barycenters |
| 253 |
ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games |
| 254 |
Dual Discriminator Generative Adversarial Nets |
| 255 |
Dynamic Revenue Sharing |
| 256 |
Decomposition-Invariant Conditional Gradient for General Polytopes with Line Search |
| 257 |
VAIN: Attentional Multi-agent Predictive Modeling |
| 258 |
An Empirical Bayes Approach to Optimizing Machine Learning Algorithms |
| 259 |
Differentially Private Empirical Risk Minimization Revisited: Faster and More General |
| 260 |
Variational Inference via \chi Upper Bound Minimization |
| 261 |
On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning |
| 262 |
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning |
| 263 |
An Empirical Study on The Properties of Random Bases for Kernel Methods |
| 264 |
Bridging the Gap Between Value and Policy Based Reinforcement Learning |
| 265 |
Premise Selection for Theorem Proving by Deep Graph Embedding |
| 266 |
A Bayesian Data Augmentation Approach for Learning Deep Models |
| 267 |
Principles of Riemannian Geometry in Neural Networks |
| 268 |
Cold-Start Reinforcement Learning with Softmax Policy Gradient |
| 269 |
Online Dynamic Programming |
| 270 |
Alternating Estimation for Structured High-Dimensional Multi-Response Models |
| 271 |
Convolutional Gaussian Processes |
| 272 |
Estimation of the covariance structure of heavy-tailed distributions |
| 273 |
Mean Field Residual Networks: On the Edge of Chaos |
| 274 |
Decomposable Submodular Function Minimization: Discrete and Continuous |
| 275 |
Gauging Variational Inference |
| 276 |
Deep Recurrent Neural Network-Based Identification of Precursor microRNAs |
| 277 |
Robust Estimation of Neural Signals in Calcium Imaging |
| 278 |
State Aware Imitation Learning |
| 279 |
Beyond Parity: Fairness Objectives for Collaborative Filtering |
| 280 |
A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent |
| 281 |
Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach |
| 282 |
Model-Powered Conditional Independence Test |
| 283 |
Deep Voice 2: Multi-Speaker Neural Text-to-Speech |
| 284 |
Variance-based Regularization with Convex Objectives |
| 285 |
Deep Lattice Networks and Partial Monotonic Functions |
| 286 |
Continual Learning with Deep Generative Replay |
| 287 |
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms |
| 288 |
Learning Causal Structures Using Regression Invariance |
| 289 |
Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback |
| 290 |
Near Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem |
| 291 |
Reinforcement Learning under Model Mismatch |
| 292 |
Hierarchical Attentive Recurrent Tracking |
| 293 |
Tomography of the London Underground: a Scalable Model for Origin-Destination Data |
| 294 |
Rotting Bandits |
| 295 |
Unbiased estimates for linear regression via volume sampling |
| 296 |
Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search |
| 297 |
Adaptive Accelerated Gradient Converging Method under H"{o}lderian Error Bound Condition |
| 298 |
Stein Variational Gradient Descent as Gradient Flow |
| 299 |
Partial Hard Thresholding: Towards A Principled Analysis of Support Recovery |
| 300 |
Shallow Updates for Deep Reinforcement Learning |
| 301 |
LightGBM: A Highly Efficient Gradient Boosting Decision Tree |
| 302 |
Adversarial Ranking for Language Generation |
| 303 |
Regret Minimization in MDPs with Options without Prior Knowledge |
| 304 |
Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee |
| 305 |
Graph Matching via Multiplicative Update Algorithm |
| 306 |
Dynamic Importance Sampling for Anytime Bounds of the Partition Function |
| 307 |
Is the Bellman residual a bad proxy? |
| 308 |
Generalization Properties of Learning with Random Features |
| 309 |
Differentially private Bayesian learning on distributed data |
| 310 |
Learning to Compose Domain-Specific Transformations for Data Augmentation |
| 311 |
Wasserstein Learning of Deep Generative Point Process Models |
| 312 |
Ensemble Sampling |
| 313 |
Language Modeling with Recurrent Highway Hypernetworks |
| 314 |
Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter |
| 315 |
Bayesian Compression for Deep Learning |
| 316 |
Streaming Sparse Gaussian Process Approximations |
| 317 |
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning |
| 318 |
Sparse Embedded k-Means Clustering |
| 319 |
Dynamic-Depth Context Tree Weighting |
| 320 |
A Regularized Framework for Sparse and Structured Neural Attention |
| 321 |
Multi-output Polynomial Networks and Factorization Machines |
| 322 |
Clustering Billions of Reads for DNA Data Storage |
| 323 |
Multi-Objective Non-parametric Sequential Prediction |
| 324 |
A Universal Analysis of Large-Scale Regularized Least Squares Solutions |
| 325 |
Deep Sets |
| 326 |
ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events |
| 327 |
Process-constrained batch Bayesian optimisation |
| 328 |
Spherical convolutions and their application in molecular modelling |
| 329 |
Efficient Optimization for Linear Dynamical Systems with Applications to Clustering and Sparse Coding |
| 330 |
On Optimal Generalizability in Parametric Learning |
| 331 |
Near Optimal Sketching of Low-Rank Tensor Regression |
| 332 |
Tractability in Structured Probability Spaces |
| 333 |
Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit |
| 334 |
Gaussian process based nonlinear latent structure discovery in multivariate spike train data |
| 335 |
Neural system identification for large populations separating “what” and “where” |
| 336 |
Certified Defenses for Data Poisoning Attacks |
| 337 |
Eigen-Distortions of Hierarchical Representations |
| 338 |
Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization |
| 339 |
Unsupervised Sequence Classification using Sequential Output Statistics |
| 340 |
Subset Selection under Noise |
| 341 |
Collecting Telemetry Data Privately |
| 342 |
Concrete Dropout |
| 343 |
Adaptive Batch Size for Safe Policy Gradients |
| 344 |
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning |
| 345 |
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference |
| 346 |
Bayesian GAN |
| 347 |
Off-policy evaluation for slate recommendation |
| 348 |
A multi-agent reinforcement learning model of common-pool resource appropriation |
| 349 |
On the Optimization Landscape of Tensor Decompositions |
| 350 |
High-Order Attention Models for Visual Question Answering |
| 351 |
Sparse convolutional coding for neuronal assembly detection |
| 352 |
Quantifying how much sensory information in a neural code is relevant for behavior |
| 353 |
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks |
| 354 |
Reducing Reparameterization Gradient Variance |
| 355 |
Visual Reference Resolution using Attention Memory for Visual Dialog |
| 356 |
Joint distribution optimal transportation for domain adaptation |
| 357 |
Multiresolution Kernel Approximation for Gaussian Process Regression |
| 358 |
Collapsed variational Bayes for Markov jump processes |
| 359 |
Universal consistency and minimax rates for online Mondrian Forests |
| 360 |
Welfare Guarantees from Data |
| 361 |
Diving into the shallows: a computational perspective on large-scale shallow learning |
| 362 |
End-to-end Differentiable Proving |
| 363 |
Influence Maximization with \varepsilon-Almost Submodular Threshold Functions |
| 364 |
InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations |
| 365 |
Variational Laws of Visual Attention for Dynamic Scenes |
| 366 |
Recursive Sampling for the Nystrom Method |
| 367 |
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning |
| 368 |
Dynamic Routing Between Capsules |
| 369 |
Incorporating Side Information by Adaptive Convolution |
| 370 |
Conic Scan-and-Cover algorithms for nonparametric topic modeling |
| 371 |
FALKON: An Optimal Large Scale Kernel Method |
| 372 |
Structured Generative Adversarial Networks |
| 373 |
Conservative Contextual Linear Bandits |
| 374 |
Variational Memory Addressing in Generative Models |
| 375 |
On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm |
| 376 |
Scalable Levy Process Priors for Spectral Kernel Learning |
| 377 |
Deep Hyperspherical Learning |
| 378 |
Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction |
| 379 |
On-the-fly Operation Batching in Dynamic Computation Graphs |
| 380 |
Nonlinear Acceleration of Stochastic Algorithms |
| 381 |
Optimized Pre-Processing for Discrimination Prevention |
| 382 |
YASS: Yet Another Spike Sorter |
| 383 |
Independence clustering (without a matrix) |
| 384 |
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders |
| 385 |
Adaptive Active Hypothesis Testing under Limited Information |
| 386 |
Streaming Weak Submodularity: Interpreting Neural Networks on the Fly |
| 387 |
Successor Features for Transfer in Reinforcement Learning |
| 388 |
Counterfactual Fairness |
| 389 |
Prototypical Networks for Few-shot Learning |
| 390 |
Triple Generative Adversarial Nets |
| 391 |
Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation |
| 392 |
Mapping distinct timescales of functional interactions among brain networks |
| 393 |
Multi-Armed Bandits with Metric Movement Costs |
| 394 |
Learning A Structured Optimal Bipartite Graph for Co-Clustering |
| 395 |
Learning Low-Dimensional Metrics |
| 396 |
The Marginal Value of Adaptive Gradient Methods in Machine Learning |
| 397 |
Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification |
| 398 |
Deconvolutional Paragraph Representation Learning |
| 399 |
Random Permutation Online Isotonic Regression |
| 400 |
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning |
| 401 |
Inverse Filtering for Hidden Markov Models |
| 402 |
Non-parametric Structured Output Networks |
| 403 |
Learning Active Learning from Data |
| 404 |
VAE Learning via Stein Variational Gradient Descent |
| 405 |
Reconstructing perceived faces from brain activations with deep adversarial neural decoding |
| 406 |
Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems |
| 407 |
Temporal Coherency based Criteria for Predicting Video Frames using Deep Multi-stage Generative Adversarial Networks |
| 408 |
Sobolev Training for Neural Networks |
| 409 |
Multi-Information Source Optimization |
| 410 |
Deep Reinforcement Learning from Human Preferences |
| 411 |
On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks |
| 412 |
Policy Gradient With Value Function Approximation For Collective Multiagent Planning |
| 413 |
Adversarial Symmetric Variational Autoencoder |
| 414 |
Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays |
| 415 |
A Minimax Optimal Algorithm for Crowdsourcing |
| 416 |
Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach |
| 417 |
A Decomposition of Forecast Error in Prediction Markets |
| 418 |
Safe Adaptive Importance Sampling |
| 419 |
Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net |
| 420 |
Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication |
| 421 |
Unsupervised Learning of Disentangled Representations from Video |
| 422 |
Federated Multi-Task Learning |
| 423 |
Is Input Sparsity Time Possible for Kernel Low-Rank Approximation? |
| 424 |
The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities |
| 425 |
Improved Graph Laplacian via Geometric Self-Consistency |
| 426 |
Dual Path Networks |
| 427 |
Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers |
| 428 |
A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks |
| 429 |
Distral: Robust multitask reinforcement learning |
| 430 |
Online Learning of Optimal Bidding Strategy in Repeated Multi-Commodity Auctions |
| 431 |
Trimmed Density Ratio Estimation |
| 432 |
Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems |
| 433 |
Visual Interaction Networks: Learning a Physics Simulator from Video |
| 434 |
Reconstruct & Crush Network |
| 435 |
Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach |
| 436 |
Simple strategies for recovering inner products from coarsely quantized random projections |
| 437 |
Discovering Potential Correlations via Hypercontractivity |
| 438 |
Doubly Stochastic Variational Inference for Deep Gaussian Processes |
| 439 |
Ranking Data with Continuous Labels through Oriented Recursive Partitions |
| 440 |
Scalable Model Selection for Belief Networks |
| 441 |
Targeting EEG/LFP Synchrony with Neural Nets |
| 442 |
Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs |
| 443 |
Non-Stationary Spectral Kernels |
| 444 |
Overcoming Catastrophic Forgetting by Incremental Moment Matching |
| 445 |
Balancing information exposure in social networks |
| 446 |
SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud |
| 447 |
Query Complexity of Clustering with Side Information |
| 448 |
QMDP-Net: Deep Learning for Planning under Partial Observability |
| 449 |
Robust Optimization for Non-Convex Objectives |
| 450 |
Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation |
| 451 |
Adaptive Classification for Prediction Under a Budget |
| 452 |
Convergence rates of a partition based Bayesian multivariate density estimation method |
| 453 |
Affine-Invariant Online Optimization and the Low-rank Experts Problem |
| 454 |
Beyond Worst-case: A Probabilistic Analysis of Affine Policies in Dynamic Optimization |
| 455 |
A Unified Approach to Interpreting Model Predictions |
| 456 |
Stochastic Approximation for Canonical Correlation Analysis |
| 457 |
Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice |
| 458 |
Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions |
| 459 |
Scalable Variational Inference for Dynamical Systems |
| 460 |
Context Selection for Embedding Models |
| 461 |
Working hard to know your neighbor's margins: Local descriptor learning loss |
| 462 |
Accelerated Stochastic Greedy Coordinate Descent by Soft Thresholding Projection onto Simplex |
| 463 |
Multi-Task Learning for Contextual Bandits |
| 464 |
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon |
| 465 |
Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds |
| 466 |
Selective Classification for Deep Neural Networks |
| 467 |
Minimax Estimation of Bandable Precision Matrices |
| 468 |
Monte-Carlo Tree Search by Best Arm Identification |
| 469 |
Group Additive Structure Identification for Kernel Nonparametric Regression |
| 470 |
Fast, Sample-Efficient Algorithms for Structured Phase Retrieval |
| 471 |
Hash Embeddings for Efficient Word Representations |
| 472 |
Online Learning for Multivariate Hawkes Processes |
| 473 |
Maximum Margin Interval Trees |
| 474 |
DropoutNet: Addressing Cold Start in Recommender Systems |
| 475 |
A simple neural network module for relational reasoning |
| 476 |
Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes |
| 477 |
Online Reinforcement Learning in Stochastic Games |
| 478 |
Position-based Multiple-play Bandit Problem with Unknown Position Bias |
| 479 |
Active Exploration for Learning Symbolic Representations |
| 480 |
Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling |
| 481 |
Fair Clustering Through Fairlets |
| 482 |
Polynomial time algorithms for dual volume sampling |
| 483 |
Hindsight Experience Replay |
| 484 |
Stochastic and Adversarial Online Learning without Hyperparameters |
| 485 |
Teaching Machines to Describe Images with Natural Language Feedback |
| 486 |
Perturbative Black Box Variational Inference |
| 487 |
GibbsNet: Iterative Adversarial Inference for Deep Graphical Models |
| 488 |
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space |
| 489 |
Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization |
| 490 |
Learning Graph Representations with Embedding Propagation |
| 491 |
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes |
| 492 |
A-NICE-MC: Adversarial Training for MCMC |
| 493 |
Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models |
| 494 |
Real-Time Bidding with Side Information |
| 495 |
Saliency-based Sequential Image Attention with Multiset Prediction |
| 496 |
Variational Inference for Gaussian Process Models with Linear Complexity |
| 497 |
K-Medoids For K-Means Seeding |
| 498 |
Identifying Outlier Arms in Multi-Armed Bandit |
| 499 |
Online Learning with Transductive Regret |
| 500 |
Riemannian approach to batch normalization |
| 501 |
Self-supervised Learning of Motion Capture |
| 502 |
Triangle Generative Adversarial Networks |
| 503 |
PRUNE: Preserving Proximity and Global Ranking for Network Embedding |
| 504 |
Bayesian Optimization with Gradients |
| 505 |
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation |
| 506 |
Renyi Differential Privacy Mechanisms for Posterior Sampling |
| 507 |
Online Learning with a Hint |
| 508 |
Identification of Gaussian Process State Space Models |
| 509 |
Robust Imitation of Diverse Behaviors |
| 510 |
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent |
| 511 |
Local Aggregative Games |
| 512 |
A Sample Complexity Measure with Applications to Learning Optimal Auctions |
| 513 |
Thinking Fast and Slow with Deep Learning and Tree Search |
| 514 |
EEG-GRAPH: A Factor-Graph-Based Model for Capturing Spatial, Temporal, and Observational Relationships in Electroencephalograms |
| 515 |
Improving the Expected Improvement Algorithm |
| 516 |
Hybrid Reward Architecture for Reinforcement Learning |
| 517 |
Approximate Supermodularity Bounds for Experimental Design |
| 518 |
Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification |
| 519 |
AdaGAN: Boosting Generative Models |
| 520 |
Straggler Mitigation in Distributed Optimization Through Data Encoding |
| 521 |
Multi-View Decision Processes: The Helper-AI Problem |
| 522 |
A Greedy Approach for Budgeted Maximum Inner Product Search |
| 523 |
SVD-Softmax: Fast Softmax Approximation on Large Vocabulary Neural Networks |
| 524 |
Plan, Attend, Generate: Planning for Sequence-to-Sequence Models |
| 525 |
Task-based End-to-end Model Learning in Stochastic Optimization |
| 526 |
ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching |
| 527 |
Finite sample analysis of the GTD Policy Evaluation Algorithms in Markov Setting |
| 528 |
On the Complexity of Learning Neural Networks |
| 529 |
Hierarchical Implicit Models and Likelihood-Free Variational Inference |
| 530 |
Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference |
| 531 |
Approximation and Convergence Properties of Generative Adversarial Learning |
| 532 |
From Bayesian Sparsity to Gated Recurrent Nets |
| 533 |
Min-Max Propagation |
| 534 |
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? |
| 535 |
Gradient descent GAN optimization is locally stable |
| 536 |
Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks |
| 537 |
Dualing GANs |
| 538 |
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model |
| 539 |
Do Deep Neural Networks Suffer from Crowding? |
| 540 |
Learning from Complementary Labels |
| 541 |
Online control of the false discovery rate with decaying memory |
| 542 |
Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes |
| 543 |
Discriminative State Space Models |
| 544 |
On Fairness and Calibration |
| 545 |
Imagination-Augmented Agents for Deep Reinforcement Learning |
| 546 |
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations |
| 547 |
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning |
| 548 |
Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra |
| 549 |
Asynchronous Parallel Coordinate Minimization for MAP Inference |
| 550 |
Multiscale Quantization for Fast Similarity Search |
| 551 |
Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space |
| 552 |
Improved Training of Wasserstein GANs |
| 553 |
Learning Populations of Parameters |
| 554 |
Clustering with Noisy Queries |
| 555 |
Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods |
| 556 |
Training Quantized Nets: A Deeper Understanding |
| 557 |
Permutation-based Causal Inference Algorithms with Interventions |
| 558 |
Time-dependent spatially varying graphical models, with application to brain fMRI data analysis |
| 559 |
Gradient Methods for Submodular Maximization |
| 560 |
Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization |
| 561 |
The Importance of Communities for Learning to Influence |
| 562 |
Multiplicative Weights Update with Constant Step-Size in Congestion Games: Convergence, Limit Cycles and Chaos |
| 563 |
Learning Neural Representations of Human Cognition across Many fMRI Studies |
| 564 |
A KL-LUCB algorithm for Large-Scale Crowdsourcing |
| 565 |
Collaborative Deep Learning in Fixed Topology Networks |
| 566 |
Fast-Slow Recurrent Neural Networks |
| 567 |
Learning Disentangled Representations with Semi-Supervised Deep Generative Models |
| 568 |
Self-Supervised Intrinsic Image Decomposition |
| 569 |
Exploring Generalization in Deep Learning |
| 570 |
A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control |
| 571 |
Fader Networks:Manipulating Images by Sliding Attributes |
| 572 |
Action Centered Contextual Bandits |
| 573 |
Estimating Mutual Information for Discrete-Continuous Mixtures |
| 574 |
Attention is All you Need |
| 575 |
Recurrent Ladder Networks |
| 576 |
Parameter-Free Online Learning via Model Selection |
| 577 |
Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction |
| 578 |
Unbounded cache model for online language modeling with open vocabulary |
| 579 |
Predictive State Recurrent Neural Networks |
| 580 |
Early stopping for kernel boosting algorithms: A general analysis with localized complexities |
| 581 |
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability |
| 582 |
Convolutional Phase Retrieval |
| 583 |
Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein’s Lemma |
| 584 |
Gaussian Quadrature for Kernel Features |
| 585 |
Value Prediction Network |
| 586 |
A Learning Error Analysis for Structured Prediction with Approximate Inference |
| 587 |
Efficient Second-Order Online Kernel Learning with Adaptive Embedding |
| 588 |
Implicit Regularization in Matrix Factorization |
| 589 |
Optimal Shrinkage of Singular Values Under Random Data Contamination |
| 590 |
Countering Feedback Delays in Multi-Agent Learning |
| 591 |
Asynchronous Coordinate Descent under More Realistic Assumptions |
| 592 |
Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls |
| 593 |
Hierarchical Clustering Beyond the Worst-Case |
| 594 |
Invariance and Stability of Deep Convolutional Representations |
| 595 |
Statistical Cost Sharing |
| 596 |
The Expressive Power of Neural Networks: A View from the Width |
| 597 |
Spectrally-normalized margin bounds for neural networks |
| 598 |
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes |
| 599 |
Population Matching Discrepancy and Applications in Deep Learning |
| 600 |
Scalable Planning with Tensorflow for Hybrid Nonlinear Domains |
| 601 |
Boltzmann Exploration Done Right |
| 602 |
Learned in Translation: Contextualized Word Vectors |
| 603 |
Neural Discrete Representation Learning |
| 604 |
Generalizing GANs: A Turing Perspective |
| 605 |
Scalable Log Determinants for Gaussian Process Kernel Learning |
| 606 |
Poincaré Embeddings for Learning Hierarchical Representations |
| 607 |
Learning Combinatorial Optimization Algorithms over Graphs |
| 608 |
Robust Conditional Probabilities |
| 609 |
Learning with Bandit Feedback in Potential Games |
| 610 |
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments |
| 611 |
Communication-Efficient Distributed Learning of Discrete Distributions |
| 612 |
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles |
| 613 |
When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness |
| 614 |
Matrix Norm Estimation from a Few Entries |
| 615 |
Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons |
| 616 |
Causal Effect Inference with Deep Latent-Variable Models |
| 617 |
Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity |
| 618 |
Gradient Episodic Memory for Continual Learning |
| 619 |
Effective Parallelisation for Machine Learning |
| 620 |
Semisupervised Clustering, AND-Queries and Locally Encodable Source Coding |
| 621 |
Clustering Stable Instances of Euclidean k-means. |
| 622 |
Good Semi-supervised Learning That Requires a Bad GAN |
| 623 |
On Blackbox Backpropagation and Jacobian Sensing |
| 624 |
Protein Interface Prediction using Graph Convolutional Networks |
| 625 |
Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities |
| 626 |
Towards Generalization and Simplicity in Continuous Control |
| 627 |
Random Projection Filter Bank for Time Series Data |
| 628 |
Filtering Variational Objectives |
| 629 |
On Frank-Wolfe and Equilibrium Computation |
| 630 |
Modulating early visual processing by language |
| 631 |
Learning Mixture of Gaussians with Streaming Data |
| 632 |
Practical Hash Functions for Similarity Estimation and Dimensionality Reduction |
| 633 |
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium |
| 634 |
The Scaling Limit of High-Dimensional Online Independent Component Analysis |
| 635 |
Approximation Algorithms for \ell_0-Low Rank Approximation |
| 636 |
The power of absolute discounting: all-dimensional distribution estimation |
| 637 |
Few-Shot Adversarial Domain Adaptation |
| 638 |
Spectral Mixture Kernels for Multi-Output Gaussian Processes |
| 639 |
Neural Expectation Maximization |
| 640 |
Learning Linear Dynamical Systems via Spectral Filtering |
| 641 |
Z-Forcing: Training Stochastic Recurrent Networks |
| 642 |
Learning Hierarchical Information Flow with Recurrent Neural Modules |
| 643 |
Neural Variational Inference and Learning in Undirected Graphical Models |
| 644 |
Subspace Clustering via Tangent Cones |
| 645 |
The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process |
| 646 |
Inverse Reward Design |
| 647 |
Structured Bayesian Pruning via Log-Normal Multiplicative Noise |
| 648 |
Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin |
| 649 |
Acceleration and Averaging in Stochastic Descent Dynamics |
| 650 |
Kernel functions based on triplet comparisons |
| 651 |
An Error Detection and Correction Framework for Connectomics |
| 652 |
Style Transfer from Non-Parallel Text by Cross-Alignment |
| 653 |
Cross-Spectral Factor Analysis |
| 654 |
Stochastic Submodular Maximization: The Case of Coverage Functions |
| 655 |
Affinity Clustering: Hierarchical Clustering at Scale |
| 656 |
Unsupervised Transformation Learning via Convex Relaxations |
| 657 |
A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening |
| 658 |
Linear Time Computation of Moments in Sum-Product Networks |
| 659 |
A Meta-Learning Perspective on Cold-Start Recommendations for Items |
| 660 |
Predicting Scene Parsing and Motion Dynamics in the Future |
| 661 |
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference |
| 662 |
Efficient Approximation Algorithms for Strings Kernel Based Sequence Classification |
| 663 |
Kernel Feature Selection via Conditional Covariance Minimization |
| 664 |
Convergence of Gradient EM on Multi-component Mixture of Gaussians |
| 665 |
Real Time Image Saliency for Black Box Classifiers |
| 666 |
Houdini: Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples |
| 667 |
Efficient and Flexible Inference for Stochastic Systems |
| 668 |
When Cyclic Coordinate Descent Outperforms Randomized Coordinate Descent |
| 669 |
Active Learning from Peers |
| 670 |
Experimental Design for Learning Causal Graphs with Latent Variables |
| 671 |
Learning to Model the Tail |
| 672 |
Stochastic Mirror Descent in Variationally Coherent Optimization Problems |
| 673 |
On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models |
| 674 |
Maxing and Ranking with Few Assumptions |
| 675 |
On clustering network-valued data |
| 676 |
A General Framework for Robust Interactive Learning |
| 677 |
Multi-view Matrix Factorization for Linear Dynamical System Estimation |