Downloads 2018

Format:
Posters:
Tutorials:
Invited talks:
Workshops:
Demonstrations:

Number of events: 709

$D^2$: Decentralized Training over Decentralized Data
10th International Workshop on Agents in Traffic and Transportation (ATT 2018)
19th International Workshop on Multi-Agent-Based Simulation (MABS 2018)
2nd International Joint Conference on Artificial Intelligence (IJCAI) Workshop on Artificial Intelligence in Affective Computing
31st International Workshop on Qualitative Reasoning (QR 2018)
6th Goal Reasoning Workshop
ABMUS-18 - Agent-Based Modelling of Urban Systems
A Boo(n) for Evaluating Architecture Performance
Accelerated Spectral Ranking
Accelerating Greedy Coordinate Descent Methods
Accelerating Natural Gradient with Higher-Order Invariance
Accurate Inference for Adaptive Linear Models
Accurate Uncertainties for Deep Learning Using Calibrated Regression
A Classification-Based Study of Covariate Shift in GAN Distributions
A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming
Active Learning with Logged Data
Active Testing: An Efficient and Robust Framework for Estimating Accuracy
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
Adaptive and Learning Agents 2018 (day 1)
Adaptive and Learning Agents 2018 (day 2)
Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits
Adaptive Sampled Softmax with Kernel Based Sampling
Adaptive Three Operator Splitting
Addressing Function Approximation Error in Actor-Critic Methods
A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning
A Distributed Second-Order Algorithm You Can Trust
ADMM and Accelerated ADMM as Continuous Dynamical Systems
Adversarial Attack on Graph Structured Data
Adversarial Learning with Local Coordinate Coding
Adversarially Regularized Autoencoders
Adversarial Regression with Multiple Learners
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
Adversarial Time-to-Event Modeling
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music
AI and Computational Psychology: Theories, Algorithms and Applications (CompPsy)
AI and Security: Lessons, Challenges and Future Directions
AI for Aging, Rehabilitation and Independent Assisted Living (ARIAL) and Intelligent Conversation Agents in Home and Geriatric Care Applications
AI for Synthetic Biology 2
AI-MHRI (AI for Multimodal Human-Robot Interaction) (day 1)
AI-MHRI (AI for Multimodal Human-Robot Interaction) (day 2)
ALAW - Agents Living in Augmented Worlds
Alternating Randomized Block Coordinate Descent
An Algorithmic Framework of Variable Metric Over-Relaxed Hybrid Proximal Extra-Gradient Method
An Alternative View: When Does SGD Escape Local Minima?
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model
Analyzing the Robustness of Nearest Neighbors to Adversarial Examples
Analyzing Uncertainty in Neural Machine Translation
An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning
An Efficient Semismooth Newton based Algorithm for Convex Clustering
An Estimation and Analysis Framework for the Rasch Model
An Inference-Based Policy Gradient Method for Learning Options
An Iterative, Sketching-based Framework for Ridge Regression
Anonymous Walk Embeddings
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions
Approximate message passing for amplitude based optimization
Approximation Algorithms for Cascading Prediction Models
Approximation Guarantees for Adaptive Sampling
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks
A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization
A Progressive Batching L-BFGS Method for Machine Learning
Architectures and Evaluation for Generality, Autonomy and Progress in AI (AEGAP)
A Reductions Approach to Fair Classification
A Robust Approach to Sequential Information Theoretic Planning
Artificial Intelligence for Knowledge Management
Artificial Intelligence for Wildlife Conservation (AIWC) Workshop
A Semantic Loss Function for Deep Learning with Symbolic Knowledge
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates
A Spectral Approach to Gradient Estimation for Implicit Distributions
A Spline Theory of Deep Learning
Asynchronous Byzantine Machine Learning (the case of SGD)
Asynchronous Decentralized Parallel Stochastic Gradient Descent
Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization
A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations
Attention-based Deep Multiple Instance Learning
A Two-Step Computation of the Exact GAN Wasserstein Distance
Augment and Reduce: Stochastic Inference for Large Categorical Distributions
Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data
A Unified Framework for Structured Low-rank Matrix Learning
Automatic Goal Generation for Reinforcement Learning Agents
AutoML 2018
Autonomy in Teams -- Joint Workshop on Sharing Autonomy in Human-Robot Interaction
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning
Autoregressive Convolutional Neural Networks for Asynchronous Time Series
Autoregressive Quantile Networks for Generative Modeling
Bandits with Delayed, Aggregated Anonymous Feedback
Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent
Bayesian Model Selection for Change Point Detection and Clustering
Bayesian Optimization of Combinatorial Structures
Bayesian Quadrature for Multiple Related Integrals
Bayesian Uncertainty Estimation for Batch Normalized Deep Networks
Been There, Done That: Meta-Learning with Episodic Recall
Best Arm Identification in Linear Bandits with Linear Dimension Dependency
Beyond 1/2-Approximation for Submodular Maximization on Massive Data Streams
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations
Beyond the One-Step Greedy Approach in Reinforcement Learning
Bilevel Programming for Hyperparameter Optimization and Meta-Learning
Binary Classification with Karmic, Threshold-Quasi-Concave Metrics
Binary Partitions with Approximate Minimum Impurity
Black-box Adversarial Attacks with Limited Queries and Information
Black Box FDR
Black-Box Variational Inference for Stochastic Differential Equations
Blind Justice: Fairness with Encrypted Sensitive Attributes
BOCK : Bayesian Optimization with Cylindrical Kernels
BOHB: Robust and Efficient Hyperparameter Optimization at Scale
Born Again Neural Networks
Bounding and Counting Linear Regions of Deep Neural Networks
Bounds on the Approximation Power of Feedforward Neural Networks
Bridging the Gap between Human and Automated Reasoning
Bucket Renormalization for Approximate Inference
Budgeted Experiment Design for Causal Structure Learning
Building Machines that Learn and Think Like People
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?
Candidates vs. Noises Estimation for Large Multi-Class Classification Problem
Canonical Tensor Decomposition for Knowledge Base Completion
Causal Bandits with Propagating Inference
Celer: a Fast Solver for the Lasso with Dual Extrapolation
Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions
Characterizing Implicit Bias in Terms of Optimization Geometry
Chi-square Generative Adversarial Network
Classification from Pairwise Similarity and Unlabeled Data
Clipped Action Policy Gradient
Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization
Clustering Semi-Random Mixtures of Gaussians
Coded Sparse Matrix Multiplication
Cognitive Vision: Integrated Vision and AI for Embodied Perception and Interaction
Communication-Computation Efficient Gradient Coding
Comparing Dynamics: Deep Neural Networks versus Glassy Systems
Comparison-Based Random Forests
Competitive Caching with Machine Learned Advice
Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations
Compiling Combinatorial Prediction Games
Composable Planning with Attributes
Composite Functional Gradient Learning of Generative Adversarial Models
Composite Marginal Likelihood Methods for Random Utility Models
Compressing Neural Networks using the Variational Information Bottelneck
Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn's Algorithm
Computer Games Workshop
Conditional Neural Processes
Conditional Noise-Contrastive Estimation of Unnormalised Models
Configurable Markov Decision Processes
Constant-Time Predictive Distributions for Gaussian Processes
Constrained Interacting Submodular Groupings
Constraining the Dynamics of Deep Probabilistic Models
ContextNet: Deep learning for Star Galaxy Classification
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing
Continual Reinforcement Learning with Complex Synapses
Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions
Continuous-Time Flows for Efficient Inference and Density Estimation
Convergence guarantees for a class of non-convex and non-smooth optimization problems
Convergent Tree Backup and Retrace with Function Approximation
Convolutional Imputation of Matrix Networks
Coordinated Exploration in Concurrent Reinforcement Learning
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization
CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions
CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning
Crowdsourcing with Arbitrary Adversaries
CRVI: Convex Relaxation for Variational Inference
Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks
Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Data-Dependent Stability of Stochastic Gradient Descent
Data Science meets Optimization
Data Summarization at Scale: A Two-Stage Submodular Approach
DCFNet: Deep Neural Network with Decomposed Convolutional Filters
Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
Decoupled Parallel Backpropagation with Convergence Guarantee
Decoupling Gradient-Like Learning Rules from Representations
Deep Asymmetric Multi-task Feature Learning
Deep Bayesian Nonparametric Tracking
Deep Density Destructors
Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions
Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global
Deep Models of Interactions Across Sets
Deep One-Class Classification
Deep Predictive Coding Network for Object Recognition
Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling
Deep Variational Reinforcement Learning for POMDPs
Defining and Designing Fair Algorithms
Delayed Impact of Fair Machine Learning
Dependent Relational Gamma Process Models for Longitudinal Networks
Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches
Detecting and Correcting for Label Shift with Black Box Predictors
Detecting non-causal artifacts in multivariate linear regression models
DiCE: The Infinitely Differentiable Monte Carlo Estimator
DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding
Differentiable Abstract Interpretation for Provably Robust Neural Networks
Differentiable Compositional Kernel Learning for Gaussian Processes
Differentiable Dynamic Programming for Structured Prediction and Attention
Differentiable plasticity: training plastic neural networks with backpropagation
Differentially Private Database Release via Kernel Mean Embeddings
Differentially Private Identity and Equivalence Testing of Discrete Distributions
Differentially Private Matrix Completion Revisited
Dimensionality-Driven Learning with Noisy Labels
Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
Disentangled Sequential Autoencoder
Disentangling by Factorising
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients
Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs
Distilling the Posterior in Bayesian Neural Networks
Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?
Distributed Clustering via LSH Based Data Partitioning
Distributed Nonparametric Regression under Communication Constraints
Does Distributionally Robust Supervised Learning Give Robust Classifiers?
Domain Adaptation for Visual Understanding
Do Outliers Ruin Collaboration?
DRACO: Byzantine-resilient Distributed Training via Redundant Gradients
Dropout Training, Data-dependent Regularization, and Generalization Bounds
DVAE++: Discrete Variational Autoencoders with Overlapping Transformations
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks
Dynamic Evaluation of Neural Sequence Models
Dynamic Regret of Strongly Adaptive Methods
Efficient and Consistent Adversarial Bipartite Matching
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning
Efficient end-to-end learning for quantizable representations
Efficient First-Order Algorithms for Adaptive Signal Denoising
Efficient Gradient-Free Variational Inference using Policy Search
Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation
Efficient Neural Architecture Search via Parameters Sharing
Efficient Neural Audio Synthesis
Eighth International Workshop on Statistical Relational AI
Enabling Reproducibility in Machine Learning MLTrain@RML
End-to-end Active Object Tracking via Reinforcement Learning
End-to-End Learning for the Deep Multivariate Probit Model
Engineering Multi-Agent Systems (day 1)
Engineering Multi-Agent Systems (day 2)
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors
Equivalence of Multicategory SVM and Simplex Cone SVM: Fast Computations and Statistical Theory
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization
Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap
Escaping Saddles with Stochastic Gradients
Essentially No Barriers in Neural Network Energy Landscape
Estimation of Markov Chain via Rank-constrained Likelihood
Explicit Inductive Bias for Transfer Learning with Convolutional Networks
Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search
Exploration in Reinforcement Learning
Exploring Hidden Dimensions in Accelerating Convolutional Neural Networks
Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples
Extreme Learning to Rank via Low Rank Assumption
Fair and Diverse DPP-Based Data Summarization
Fairness, Interpretability, and Explainability Federation of Workshops (day 1)
Fairness, Interpretability, and Explainability Federation of Workshops (day 2-3) (day 1)
Fairness, Interpretability, and Explainability Federation of Workshops (day 2-3) (day 2)
Fairness Without Demographics in Repeated Loss Minimization
Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Fast Approximate Spectral Clustering for Dynamic Networks
Fast Bellman Updates for Robust MDPs
Fast Decoding in Sequence Models Using Discrete Latent Variables
Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines
Fast Gradient-Based Methods with Exponential Rate: A Hybrid Control Framework
Fast Information-theoretic Bayesian Optimisation
Fast Maximization of Non-Submodular, Monotonic Functions on the Integer Lattice
Fast Parametric Learning with Activation Memorization
Fast Stochastic AUC Maximization with $O(1/n)$-Convergence Rate
Fast Variance Reduction Method with Stochastic Batch Size
FCA4AI 2018
Feasible Arm Identification
Federated AI for Robotics Workshop (F-Rob-2018)
Feedback-Based Tree Search for Reinforcement Learning
Finding Influential Training Samples for Gradient Boosted Decision Trees
Firing Bandits: Optimizing Crowdfunding
First international workshop on socio-cognitive systems
First Order Generative Adversarial Networks
First Workshop on Deep Learning for Safety-Critical in Engineering Systems
Fitting New Speakers Based on a Short Untranscribed Sample
Fixing a Broken ELBO
Focused Hierarchical RNNs for Conditional Sequence Processing
Fourier Policy Gradients
Frank-Wolfe with Subsampling Oracle
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents
Functional Gradient Boosting based on Residual Network Perception
GAIN: Missing Data Imputation using Generative Adversarial Nets
Game-Theoretic Mechanisms for Data and Information
Gated Path Planning Networks
Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks
Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data for Future Prediction
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression
Generative Temporal Models with Spatial Memory for Partially Observed Environments
Geodesic Convolutional Shape Optimization
Geometry in Machine Learning (GiMLi)
Geometry Score: A Method For Comparing Generative Adversarial Networks
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms
Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator
Goal Specifications for Reinforcement Learning
Goodness-of-fit Testing for Discrete Distributions via Stein Discrepancy
Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace
Gradient Coding from Cyclic MDS Codes and Expander Graphs
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima
Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
Gradually Updated Neural Networks for Large-Scale Image Recognition
Graphical Nonconvex Optimization via an Adaptive Convex Relaxation
Graph Networks as Learnable Physics Engines for Inference and Control
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
Greed is Still Good: Maximizing Monotone Submodular+Supermodular (BP) Functions
Hierarchical Clustering with Structural Constraints
Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series
Hierarchical Imitation and Reinforcement Learning
Hierarchical Long-term Video Prediction without Supervision
Hierarchical Multi-Label Classification Networks
Hierarchical Text Generation and Planning for Strategic Dialogue
High Performance Zero-Memory Overhead Direct Convolutions
High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach
Humanizing AI (HAI)
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
Image Transformer
Imitation Learning
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
Implicit Quantile Networks for Distributional Reinforcement Learning
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion
Importance Weighted Transfer of Samples in Reinforcement Learning
Improved large-scale graph learning through ridge spectral sparsification
Improved nearest neighbor search using auxiliary information and priority functions
Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems
Improved Training of Generative Adversarial Networks Using Representative Features
Improving Optimization in Models With Continuous Symmetry Breaking
Improving Regression Performance with Distributional Losses
Improving Sign Random Projections With Additional Information
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising
Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms
Inductive Two-Layer Modeling with Parametric Bregman Transfer
Inference Suboptimality in Variational Autoencoders
Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization
INSPECTRE: Privately Estimating the Unseen
Intelligence per Kilowatthour
Inter and Intra Topic Structure Learning with Word Embeddings
International Workshop on Automated Negotiation (ACAN)
International Workshop on Massively Multi-Agent Systems
International Workshop on Optimization in Multi-Agent Systems (OptMAS)
International Workshop on Real Time compliant Multi-Agent Systems (RTcMAS)
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
Invariance of Weight Distributions in Rectified MLPs
Investigating Human Priors for Playing Video Games
Is Generator Conditioning Causally Related to GAN Performance?
Iterative Amortized Inference
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets
Joint ICML and IJCAI Workshop on Computational Biology 2018
Joint Workshop on AI in Health (day 1)
Joint Workshop on AI in Health (day 2)
Joint Workshop on Multimedia for Cooking and Eating Activities and Multimedia Assisted Dietary Management (CEA/MADiMa2018)
Junction Tree Variational Autoencoder for Molecular Graph Generation
Katyusha X: Simple Momentum Method for Stochastic Sum-of-Nonconvex Optimization
K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning
Kernelized Synaptic Weight Matrices
Kernel Recursive ABC: Point Estimation with Intractable Likelihood
K-means clustering using random matrix sparsification
Knowledge Transfer with Jacobian Matching
Kronecker Recurrent Units
Language to Action: towards Interactive Task Learning with Physical Agents
Large-Scale Cox Process Inference using Variational Fourier Features
Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion
Latent Space Policies for Hierarchical Reinforcement Learning
LaVAN: Localized and Visible Adversarial Noise
LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration
Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations
Learning Adversarially Fair and Transferable Representations
Learning a Mixture of Two Multinomial Logits
Learning and Memorization
Learning and Reasoning: Principles & Applications to Everyday Spatial and Temporal Knowledge (day 1)
Learning and Reasoning: Principles & Applications to Everyday Spatial and Temporal Knowledge (day 2)
Learning Binary Latent Variable Models: A Tensor Eigenpair Approach
Learning by Playing - Solving Sparse Reward Tasks from Scratch
Learning Compact Neural Networks with Regularization
Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry
Learning Deep ResNet Blocks Sequentially using Boosting Theory
Learning Diffusion using Hyperparameters
Learning Dynamics of Linear Denoising Autoencoders
Learning equations for extrapolation and control
Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling
Learning Implicit Generative Models with the Method of Learned Moments
Learning Independent Causal Mechanisms
Learning in Integer Latent Variable Models with Nested Automatic Differentiation
Learning in Reproducing Kernel Kreı̆n Spaces
Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations
Learning Localized Spatio-Temporal Models From Streaming Data
Learning Longer-term Dependencies in RNNs with Auxiliary Losses
Learning long term dependencies via Fourier recurrent units
Learning Low-Dimensional Temporal Representations
Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time
Learning Memory Access Patterns
Learning One Convolutional Layer with Overlapping Patches
Learning Policy Representations in Multiagent Systems
Learning Registered Point Processes from Idiosyncratic Observations
Learning Representations and Generative Models for 3D Point Clouds
Learning Semantic Representations for Unsupervised Domain Adaptation
Learning Steady-States of Iterative Algorithms over Graphs
Learning the Reward Function for a Misspecified Model
Learning to Act in Decentralized Partially Observable MDPs
Learning to Branch
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation
Learning to Explore via Meta-Policy Gradient
Learning to Optimize Combinatorial Functions
Learning to Reweight Examples for Robust Deep Learning
Learning to search with MCTSnets
Learning to Speed Up Structured Output Prediction
Learning unknown ODE models with Gaussian processes
Learning with Abandonment
Learning with Temporal Point Processes
Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator
Let’s be Honest: An Optimal No-Regret Framework for Zero-Sum Games
Level-Set Methods for Finite-Sum Constrained Convex Optimization
Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski $p$-Norms
Lifelong Learning: A Reinforcement Learning Approach
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data
Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design
Linear Spectral Estimators and an Application to Phase Retrieval
Linguistic and Cognitive Approaches To Dialog Agents (LaCATODA 2018)
Lipschitz Continuity in Model-based Reinforcement Learning
Local Convergence Properties of SAGA/Prox-SVRG and Acceleration
Local Density Estimation in High Dimensions
Locally Private Hypothesis Testing
Local Private Hypothesis Testing: Chi-Square Tests
Loss Decomposition for Fast Learning in Large Output Spaces
Low-Rank Riemannian Optimization on Positive Semidefinite Stochastic Matrices with Applications to Graph Clustering
Lyapunov Functions for First-Order Methods: Tight Automated Convergence Guarantees
Machine learning for Causal Inference, Counterfactual Prediction, and Autonomous Action (CausalML)
Machine Learning for Personalised Health
Machine Learning in Automated Mechanism Design for Pricing and Auctions
Machine Learning: The Debates
Machine Theory of Mind
MAGAN: Aligning Biological Manifolds
Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits
Markov Modulated Gaussian Cox Processes for Semi-Stationary Intensity Modeling of Events Data
Massively Parallel Algorithms and Hardness for Single-Linkage Clustering under $\ell_p$ Distances
Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order
Max-Mahalanobis Linear Discriminant Analysis Networks
Mean Field Multi-Agent Reinforcement Learning
Measuring abstract reasoning in neural networks
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
Message Passing Stein Variational Gradient Descent
Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory
Minibatch Gibbs Sampling on Large Graphical Models
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models
Minimax Concave Penalized Multi-Armed Bandit Model with High-Dimensional Covariates
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
Mixed batches and symmetric discriminators for GAN training
Mix & Match - Agent Curricula for Reinforcement Learning
Modeling Others using Oneself in Multi-Agent Reinforcement Learning
Modeling Sparse Deviations for Compressed Sensing using Generative Models
Model-Level Dual Learning
Modern Trends in Nonconvex Optimization for Machine Learning
More Robust Doubly Robust Off-policy Evaluation
MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning
Multicalibration: Calibration for the (Computationally-Identifiable) Masses
Multi-Fidelity Black-Box Optimization with Hierarchical Partitions
Mutual Information Neural Estimation
Nearly Optimal Robust Subspace Tracking
Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices
NetGAN: Generating Graphs via Random Walks
Network Global Testing by Counting Graphlets
Neural Abstract Machines & Program Induction Workshop v2.0 (NAMPI_v2)
Neural Autoregressive Flows
Neural Dynamic Programming for Musical Self Similarity
Neural Inverse Rendering for General Reflectance Photometric Stereo
Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions
Neural Program Synthesis from Diverse Demonstration Videos
Neural Relational Inference for Interacting Systems
Noise2Noise: Learning Image Restoration without Clean Data
Noisin: Unbiased Regularization for Recurrent Neural Networks
Noisy Natural Gradient as Variational Inference
Non-convex Conditional Gradient Sliding
Nonconvex Optimization for Regression with Fairness Constraints
Non-linear motor control by local learning in spiking neural networks
Nonoverlap-Promoting Variable Selection
Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
Nonparametric variable importance using an augmented neural network with multi-task learning
Not All Samples Are Created Equal: Deep Learning with Importance Sampling
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis
On Acceleration with Noise-Corrupted Gradients
One-Shot Segmentation in Clutter
On Learning Sparsely Used Dictionaries from Incomplete Samples
Online Convolutional Sparse Coding with Sample-Dependent Dictionary
Online Learning with Abstention
Online Linear Quadratic Control
On Matching Pursuit and Coordinate Descent
On Nesting Monte Carlo Estimators
On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups
On the Implicit Bias of Dropout
On the Limitations of First-Order Approximation in GAN Dynamics
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
On the Power of Over-parametrization in Neural Networks with Quadratic Activation
On the Relationship between Data Efficiency and Error for Uncertainty Sampling
On the Spectrum of Random Features Maps of High Dimensional Data
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo
Open Category Detection with PAC Guarantees
Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods
Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces
Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data
Optimization, fast and slow: optimally switching between local and Bayesian optimization
Optimization Landscape and Expressivity of Deep CNNs
Optimization Perspectives on Learning to Control
Optimizing the Latent Space of Generative Networks
Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis
Orthogonal Machine Learning: Power and Limitations
Orthogonal Recurrent Neural Networks with Scaled Cayley Transform
Out-of-sample extension of graph adjacency spectral embedding
Overcoming Catastrophic Forgetting with Hard Attention to the Task
Parallel and Streaming Algorithms for K-Core Decomposition
Parallel Bayesian Network Structure Learning
Parallel WaveNet: Fast High-Fidelity Speech Synthesis
Parameterized Algorithms for the Matrix Completion Problem
Partial Optimality and Fast Lower Bounds for Weighted Correlation Clustering
Path Consistency Learning in Tsallis Entropy Regularized MDPs
Path-Level Network Transformation for Efficient Architecture Search
Pathwise Derivatives Beyond the Reparameterization Trick
PDE-Net: Learning PDEs from Data
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos
PixelSNAIL: An Improved Autoregressive Generative Model
Planning and Learning (PAL-18)
Policy and Value Transfer in Lifelong Reinforcement Learning
Policy Optimization as Wasserstein Gradient Flows
Policy Optimization with Demonstrations
Practical Contextual Bandits with Regression Oracles
prDeep: Robust Phase Retrieval with a Flexible Deep Network
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction
Prediction and Generative Modeling in Reinforcement Learning
Prediction Rule Reshaping
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
Privacy in Machine Learning and Artificial Intelligence (PiMLAI)
Probabilistic Boolean Tensor Decomposition
Probabilistic Recurrent State-Space Models
Probably Approximately Metric-Fair Learning
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs
Programmatically Interpretable Reinforcement Learning
Progress & Compress: A scalable framework for continual learning
Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity
Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy
Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope
Provable Variable Selection for Streaming Features
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing—and Back
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
QuantTree: Histograms for Change Detection in Multivariate Data Streams
Quasi-Monte Carlo Variational Inference
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors
RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks
Randomized Block Cubic Newton Method
Ranking Distributions based on Noisy Sorting
Rapid Adaptation with Conditionally Shifted Neurons
Rates of Convergence of Spectral Methods for Graphon Estimation
Rectify Heterogeneous Models with Semantic Mapping
Recurrent Predictive State Policy Networks
Regret Minimization for Partially Observable Deep Reinforcement Learning
Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training
Representation Learning on Graphs with Jumping Knowledge Networks
Representation Tradeoffs for Hyperbolic Embeddings
Residual Unfairness in Fair Machine Learning from Prejudiced Data
Revealing Common Statistical Behaviors in Heterogeneous Populations
Reviving and Improving Recurrent Back-Propagation
Riemannian Stochastic Recursive Gradient Algorithm with Retraction and Vector Transport and Its Convergence Analysis
RLlib: Abstractions for Distributed Reinforcement Learning
Robust and Scalable Models of Microbiome Dynamics
SADAGRAD: Strongly Adaptive Stochastic Gradient Methods
Safe Element Screening for Submodular Function Minimization
SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
Scalable approximate Bayesian inference for particle tracking data
Scalable Bilinear Pi Learning Using State and Action Features
Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)
Selecting Representative Examples for Program Synthesis
Self-Bounded Prediction Suffix Tree via Approximate String Matching
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings
Self-Imitation Learning
Semi-Amortized Variational Autoencoders
Semi-Implicit Variational Inference
Semiparametric Contextual Bandits
Semi-Supervised Learning on Data Streams via Temporal Label Propagation
Semi-Supervised Learning via Compact Latent Space Clustering
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
Shampoo: Preconditioned Stochastic Tensor Optimization
Signal and Noise Statistics Oblivious Orthogonal Matching Pursuit
signSGD: Compressed Optimisation for Non-Convex Problems
SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions
Smoothed Action Value Functions for Learning Gaussian Policies
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Solving Partial Assignment Problems using Random Clique Complexes
Sound Abstraction and Decomposition of Probabilistic Programs
SparseMAP: Differentiable Sparse Structured Inference
Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection
Spectrally Approximating Large Graphs with Smaller Graphs
Spline Filters For End-to-End Deep Learning
Spotlight: Optimizing Device Placement for Training Deep Neural Networks
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks
SQL-Rank: A Listwise Approach to Collaborative Ranking
Stability and Generalization of Learning Algorithms that Converge to Global Optima
Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization
Stagewise Safe Bayesian Optimization with Gaussian Processes
State Abstractions for Lifelong Reinforcement Learning
State Space Gaussian Processes with Non-Gaussian Likelihood
Stein Points
Stein Variational Gradient Descent Without Gradient
Stein Variational Message Passing for Continuous Graphical Models
Stochastic PCA with $\ell_2$ and $\ell_1$ Regularization
Stochastic Proximal Algorithms for AUC Maximization
Stochastic Training of Graph Convolutional Networks with Variance Reduction
Stochastic Variance-Reduced Cubic Regularized Newton Method
Stochastic Variance-Reduced Hamilton Monte Carlo Methods
Stochastic Variance-Reduced Policy Gradient
Stochastic Video Generation with a Learned Prior
Stochastic Wasserstein Barycenters
StrassenNets: Deep Learning with a Multiplication Budget
Streaming Principal Component Analysis in Noisy Setting
Stronger Generalization Bounds for Deep Nets via a Compression Approach
Structured Control Nets for Deep Reinforcement Learning
Structured Evolution with Compact Architectures for Scalable Policy Optimization
Structured Output Learning with Abstention: Application to Accurate Opinion Prediction
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors
Structured Variationally Auto-encoded Optimization
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering
Subspace Embedding and Linear Regression with Orlicz Norm
Synthesizing Programs for Images using Reinforced Adversarial Learning
Synthesizing Robust Adversarial Examples
TACO: Learning Task Decomposition via Temporal Alignment for Control
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service
Tempered Adversarial Networks
Temporal Poisson Square Root Graphical Models
Tenth International Workshop Modelling and Reasoning in Context (MRC)
Testing Sparsity over Known and Unknown Bases
The 2018 Joint Workshop on Machine Learning for Music
The 3rd International workshop on biomedical informatics with optimization and machine learning (BOOM)
The 3rd International Workshop on Knowledge Discovery in Healthcare Data
The AAMAS-IJCAI Workshop on Agents and Incentives in Artificial Intelligence (day 1)
The AAMAS-IJCAI Workshop on Agents and Incentives in Artificial Intelligence (day 2)
The Dynamics of Learning: A Random Matrix Approach
The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference
The Generalization Error of Dictionary Learning with Moreau Envelopes
The Hidden Vulnerability of Distributed Learning in Byzantium
The Hierarchical Adaptive Forgetting Variational Filter
The Joint International Workshop on Social Influence Analysis and Mining Actionable Insights from Social Networks (SocInf+MAISoN 2018)
The Limits of Maxing, Ranking, and Preference Learning
The Mechanics of n-Player Differentiable Games
The Mirage of Action-Dependent Baselines in Reinforcement Learning
The Multilinear Structure of ReLU Networks
Theoretical Analysis of Image-to-Image Translation with Adversarial Learning
Theoretical Analysis of Sparse Subspace Clustering with Missing Entries
Theoretical Foundations and Applications of Deep Generative Models (day 1)
Theoretical Foundations and Applications of Deep Generative Models (day 2)
Theory of Deep Learning
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning
The Uncertainty Bellman Equation and Exploration
The Weighted Kendall and High-order Kernels for Permutations
The Well-Tempered Lasso
Thompson Sampling for Combinatorial Semi-Bandits
Tighter Variational Bounds are Not Necessarily Better
Tight Regret Bounds for Bayesian Optimization in One Dimension
Time Limits in Reinforcement Learning
Topological mixture estimation
To Understand Deep Learning We Need to Understand Kernel Learning
Towards Binary-Valued Gates for Robust LSTM Training
Towards Black-box Iterative Machine Teaching
Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron
Towards Fast Computation of Certified Robustness for ReLU Networks
Towards learning with limited labels: Equivariance, Invariance, and Beyond
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
Toward Theoretical Understanding of Deep Learning
Tractable Probabilistic Models
Trainable Calibration Measures for Neural Networks from Kernel Mean Embeddings
Training Neural Machines with Trace-Based Supervision
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement
Transfer Learning via Learning to Transfer
Transformation Autoregressive Networks
Tree Edit Distance Learning via Adaptive Symbol Embeddings
Tropical Geometry of Deep Neural Networks
TRUST Workshop
Ultra Large-Scale Feature Selection using Count-Sketches
Unbiased Objective Estimation in Predictive Optimization
Understanding and Simplifying One-Shot Architecture Search
Understanding Generalization and Optimization Performance of Deep CNNs
Understanding the Loss Surface of Neural Networks for Binary Classification
Understanding your Neighbors: Practical Perspectives From Modern Analysis
Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control
Using Inherent Structures to design Lean 2-layer RBMs
Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning
Variable Selection via Penalized Neural Network: a Drop-Out-One Loss Approach
Variance Regularized Counterfactual Risk Minimization via Variational Divergence Minimization
Variational Bayes and Beyond: Bayesian Inference for Big Data
Variational Bayesian dropout: pitfalls and fixes
Variational Inference and Model Selection with Generalized Evidence Bounds
Variational Network Inference: Strong and Stable with Concrete Support
Video Prediction with Appearance and Motion Conditions
Visualizing and Understanding Atari Agents
Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic Buyer
Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?
Weightless: Lossy weight encoding for deep neural network compression
Which Training Methods for GANs do actually Converge?
WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models
Workshop on AI for Internet of Things
Workshop on Efficient Credit Assignment in Deep Learning and Deep Reinforcement Learning (ECA) (day 1)
Workshop on Efficient Credit Assignment in Deep Learning and Deep Reinforcement Learning (ECA) (day 2)
WSNet: Compact and Efficient Networks Through Weight Sampling
Yes, but Did It Work?: Evaluating Variational Inference