### NIPS 2018 Events with Videos

## Invited Talks

- Accountability and Algorithmic Bias: Why Diversity and Inclusion Matters
- Machine Learning Meets Public Policy: What to Expect and How to Cope
- What Bodies Think About: Bioelectric Computation Outside the Nervous System, Primitive Cognition, and Synthetic Morphology
- Investigations into the Human-AI Trust Phenomenon
- Designing Computer Systems for Software 2.0

## Invited Talk (Breiman Lecture)s

## Invited Talk (Posner Lecture)s

## Orals

- On Neuronal Capacity
- Phase Retrieval Under a Generative Prior
- Dendritic cortical microcircuits approximate the backpropagation algorithm
- Spectral Filtering for General Linear Dynamical Systems
- Generalisation of structural knowledge in the hippocampal-entorhinal system
- Neural Ordinary Differential Equations
- Model-Agnostic Private Learning
- A probabilistic population code based on neural samples
- How Does Batch Normalization Help Optimization?
- Learning to Solve SMT Formulas
- Exploration in Structured Reinforcement Learning
- Visual Memory for Robust Path Following
- Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes
- Policy Optimization via Importance Sampling
- Isolating Sources of Disentanglement in Variational Autoencoders
- Stochastic Cubic Regularization for Fast Nonconvex Optimization
- Recurrent World Models Facilitate Policy Evolution
- Approximate Knowledge Compilation by Online Collapsed Importance Sampling
- Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems
- Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models
- Variational Inference with Tail-adaptive f-Divergence
- Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization
- Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
- Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning
- Optimal Algorithms for Non-Smooth Distributed Optimization in Networks
- Non-delusional Q-learning and value-iteration
- Learning to Reconstruct Shapes from Unseen Classes
- Smoothed analysis of the low-rank approach for smooth semidefinite programs

## Posters

- An intriguing failing of convolutional neural networks and the CoordConv solution
- Scalable methods for 8-bit training of neural networks
- Exact natural gradient in deep linear networks and its application to the nonlinear case
- DeepPINK: reproducible feature selection in deep neural networks
- Memory Replay GANs: Learning to Generate New Categories without Forgetting
- SLAYER: Spike Layer Error Reassignment in Time
- Video-to-Video Synthesis
- Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices
- A Convex Duality Framework for GANs
- HOUDINI: Lifelong Learning as Program Synthesis
- Knowledge Distillation by On-the-Fly Native Ensemble
- Middle-Out Decoding
- A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication
- A Bridging Framework for Model Optimization and Deep Propagation
- Expanding Holographic Embeddings for Knowledge Completion
- GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training
- Learning towards Minimum Hyperspherical Energy
- Flexible neural representation for physics prediction
- Bilinear Attention Networks
- Predictive Uncertainty Estimation via Prior Networks
- Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds
- Deep Neural Nets with Interpolating Function as Output Activation
- On the Dimensionality of Word Embedding
- With Friends Like These, Who Needs Adversaries?
- Learning Versatile Filters for Efficient Convolutional Neural Networks
- Deep, complex, invertible networks for inversion of transmission effects in multimode optical fibres
- Phase Retrieval Under a Generative Prior
- BinGAN: Learning Compact Binary Descriptors with a Regularized GAN
- Active Matting
- Neural Tangent Kernel: Convergence and Generalization in Neural Networks
- Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering
- Neural Voice Cloning with a Few Samples
- Ex ante coordination and collusion in zero-sum multi-player extensive-form games
- Speaker-Follower Models for Vision-and-Language Navigation
- How Does Batch Normalization Help Optimization?
- Unsupervised Learning of Shape and Pose with Differentiable Point Clouds
- Unsupervised Learning of Artistic Styles with Archetypal Style Analysis
- See and Think: Disentangling Semantic Scene Completion
- Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation
- Chain of Reasoning for Visual Question Answering
- Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization
- Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
- Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with $\beta$-Divergences
- Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders
- Geometry Based Data Generation
- Deep Generative Markov State Models
- Bayesian Inference of Temporal Task Specifications from Demonstrations
- Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
- Differentially Private Testing of Identity and Closeness of Discrete Distributions
- The Price of Fair PCA: One Extra dimension
- MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare
- Training Deep Neural Networks with 8-bit Floating Point Numbers
- A Spectral View of Adversarially Robust Features
- Precision and Recall for Time Series
- Efficient Formal Safety Analysis of Neural Networks
- Learning filter widths of spectral decompositions with wavelets
- Sparsified SGD with Memory
- High Dimensional Linear Regression using Lattice Basis Reduction
- Delta-encoder: an effective sample synthesis method for few-shot object recognition
- Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects
- Hamiltonian Variational Auto-Encoder
- Practical exact algorithm for trembling-hand equilibrium refinements in games
- Critical initialisation for deep signal propagation in noisy rectifier neural networks
- A theory on the absence of spurious solutions for nonconvex and nonsmooth optimization
- End-to-End Differentiable Physics for Learning and Control
- Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo
- Learning Optimal Reserve Price against Non-myopic Bidders
- Deepcode: Feedback Codes via Deep Learning
- A Probabilistic U-Net for Segmentation of Ambiguous Images
- Unsupervised Depth Estimation, 3D Face Rotation and Replacement
- How to Start Training: The Effect of Initialization and Architecture
- Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model
- Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making
- Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution
- Quantifying Learning Guarantees for Convex but Inconsistent Surrogates
- Learning Safe Policies with Expert Guidance
- Learning to Decompose and Disentangle Representations for Video Prediction
- Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images
- Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs
- Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds
- Neighbourhood Consensus Networks
- Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces
- Dynamic Network Model from Partial Observations
- Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues
- How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery?
- HOGWILD!-Gibbs can be PanAccurate
- Reducing Network Agnostophobia
- Improving Simple Models with Confidence Profiles
- Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization
- Blind Deconvolutional Phase Retrieval via Convex Programming
- Heterogeneous Multi-output Gaussian Process Prediction
- Coordinate Descent with Bandit Sampling
- The Physical Systems Behind Optimization Algorithms
- Legendre Decomposition for Tensors
- Scalable Laplacian K-modes
- Alternating optimization of decision trees, with application to learning sparse oblique trees
- Long short-term memory and Learning-to-learn in networks of spiking neurons
- Depth-Limited Solving for Imperfect-Information Games
- Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced
- Learning to Play With Intrinsically-Motivated, Self-Aware Agents
- Geometrically Coupled Monte Carlo Sampling
- Estimators for Multivariate Information Measures in General Probability Spaces
- Variational Learning on Aggregate Outputs with Gaussian Processes
- Causal Discovery from Discrete Data using Hidden Compact Representation
- Online Robust Policy Learning in the Presence of Unknown Adversaries
- Learning to Navigate in Cities Without a Map
- Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning
- Unsupervised Video Object Segmentation for Deep Reinforcement Learning
- Transfer of Deep Reactive Policies for MDP Planning
- Occam's razor is insufficient to infer the preferences of irrational agents
- Deep Reinforcement Learning of Marked Temporal Point Processes
- Playing hard exploration games by watching YouTube
- Representation Balancing MDPs for Off-policy Policy Evaluation
- Hardware Conditioned Policies for Multi-Robot Transfer Learning
- From Stochastic Planning to Marginal MAP
- Randomized Prior Functions for Deep Reinforcement Learning
- Scalable Coordinated Exploration in Concurrent Reinforcement Learning
- Learning Task Specifications from Demonstrations
- Reinforcement Learning for Solving the Vehicle Routing Problem
- Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
- Diversity-Driven Exploration Strategy for Deep Reinforcement Learning
- Fast Estimation of Causal Interactions using Wold Processes
- Scaling Gaussian Process Regression with Derivatives
- Incorporating Context into Language Encoding Models for fMRI
- Automating Bayesian optimization with Bayesian optimization
- SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient
- Do Less, Get More: Streaming Submodular Maximization with Subsampling
- FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
- A Bayesian Nonparametric View on Count-Min Sketch
- A Stein variational Newton method
- Discretely Relaxing Continuous Variables for tractable Variational Inference
- Understanding Batch Normalization
- Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
- Infinite-Horizon Gaussian Processes
- Visual Object Networks: Image Generation with Disentangled 3D Representations
- Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo
- Can We Gain More from Orthogonality Regularizations in Training Deep Networks?
- Discrimination-aware Channel Pruning for Deep Neural Networks
- Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization
- Deep State Space Models for Unconditional Word Generation
- Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo
- Probabilistic Model-Agnostic Meta-Learning
- Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages
- Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems
- Overlapping Clustering Models, and One (class) SVM to Bind Them All
- Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks
- Mallows Models for Top-k Lists
- Dual Swap Disentangling
- Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification
- The committee machine: Computational to statistical gaps in learning a two-layers neural network
- Entropy and mutual information in models of deep neural networks
- Lifelong Inverse Reinforcement Learning
- Towards Deep Conversational Recommendations
- Processing of missing data by neural networks
- Self-Supervised Generation of Spatial Audio for 360Â° Video
- Why Is My Classifier Discriminatory?
- Efficient Anomaly Detection via Matrix Sketching
- Stochastic Chebyshev Gradient Descent for Spectral Optimization
- Binary Classification from Positive-Confidence Data
- Query K-means Clustering and the Double Dixie Cup Problem
- (Probably) Concave Graph Matching
- Learning Loop Invariants for Program Verification
- ResNet with one-neuron hidden layers is a Universal Approximator
- Hyperbolic Neural Networks
- Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters
- Multi-Class Learning: From Theory to Algorithm
- Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators
- Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning
- Understanding Regularized Spectral Clustering via Graph Conductance
- A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
- Norm matters: efficient and accurate normalization schemes in deep networks
- Representation Learning of Compositional Data
- Boosted Sparse and Low-Rank Tensor Regression
- Wavelet regression and additive models for irregularly spaced data
- Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima
- Efficient nonmyopic batch active search
- Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
- Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
- A Smoother Way to Train Structured Prediction Models
- Unsupervised Learning of View-invariant Action Representations
- Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation
- CatBoost: unbiased boosting with categorical features
- Bandit Learning with Positive Externalities
- Adaptive Learning with Unknown Information Flows
- Learning convex bounds for linear quadratic control policy synthesis
- Multi-armed Bandits with Compensation
- Watch Your Step: Learning Node Embeddings via Graph Attention
- SimplE Embedding for Link Prediction in Knowledge Graphs
- Bandit Learning with Implicit Feedback

## Spotlights

- Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data
- Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces
- Global Geometry of Multichannel Sparse Blind Deconvolution on the Sphere
- Size-Noise Tradeoffs in Generative Networks
- Diffusion Maps for Textual Network Embedding
- Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds
- Neural Voice Cloning with a Few Samples
- Evolved Policy Gradients
- Differentially Private Testing of Identity and Closeness of Discrete Distributions
- Answerer in Questioner's Mind: Information Theoretic Approach to Goal-Oriented Visual Dialog
- Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning
- Local Differential Privacy for Evolving Data
- Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
- Bayesian Model-Agnostic Meta-Learning
- Differentially Private k-Means with Constant Multiplicative Error
- Learning to Optimize Tensor Programs
- Probabilistic Neural Programmed Networks for Scene Generation
- A Spectral View of Adversarially Robust Features
- A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks
- Bias and Generalization in Deep Generative Models: An Empirical Study
- Bounded-Loss Private Prediction Markets
- Generalizing Tree Probability Estimation via Bayesian Networks
- Robustness of conditional GANs to noisy labels
- cpSGD: Communication-efficient and differentially-private distributed SGD
- Geometry Based Data Generation
- BourGAN: Generative Networks with Metric Embeddings
- Adversarially Robust Generalization Requires More Data
- Point process latent variable models of larval zebrafish behavior
- Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
- Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples
- Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding
- Training Neural Networks Using Features Replay
- Towards Robust Detection of Adversarial Examples
- Learning Temporal Point Processes via Reinforcement Learning
- Step Size Matters in Deep Learning
- Neural Architecture Search with Bayesian Optimisation and Optimal Transport
- Precision and Recall for Time Series
- Neural Tangent Kernel: Convergence and Generalization in Neural Networks
- Data-Driven Clustering via Parameterized Lloyd's Families
- Bayesian Nonparametric Spectral Estimation
- Hierarchical Graph Representation Learning with Differentiable Pooling
- Supervising Unsupervised Learning
- A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem
- Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images
- Revisiting $(\epsilon, \gamma, \tau)$-similarity learning for domain adaptation
- Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs
- Delta-encoder: an effective sample synthesis method for few-shot object recognition
- Leveraged volume sampling for linear regression
- End-to-End Differentiable Physics for Learning and Control
- Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language
- Synthesize Policies for Transfer and Adaptation across Tasks and Environments
- Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes
- Neighbourhood Consensus Networks
- Sublinear Time Low-Rank Approximation of Distance Matrices
- Acceleration through Optimistic No-Regret Dynamics
- Recurrent Transformer Networks for Semantic Correspondence
- Minimax Statistical Learning with Wasserstein distances
- On Oracle-Efficient PAC RL with Rich Observations
- Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects
- Generalization Bounds for Uniformly Stable Algorithms
- Constant Regret, Generalized Mixability, and Mirror Descent
- Sanity Checks for Saliency Maps
- A loss framework for calibrated anomaly detection
- Efficient Online Portfolio with Logarithmic Regret
- A Probabilistic U-Net for Segmentation of Ambiguous Images
- Sharp Bounds for Generalized Uniformity Testing
- Solving Large Sequential Games with the Excessive Gap Technique
- Virtual Class Enhanced Discriminative Embedding Learning
- Convex Elicitation of Continuous Properties
- Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation
- Dynamic Network Model from Partial Observations
- The Nearest Neighbor Information Estimator is Adaptively Near Minimax Rate-Optimal
- Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
- Stochastic Nonparametric Event-Tensor Decomposition
- Contextual Stochastic Block Models
- Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
- On GANs and GMMs
- Entropy Rate Estimation for Markov Chains with Large State Space
- Meta-Reinforcement Learning of Structured Exploration Strategies
- GILBO: One Metric to Measure Them All
- Blind Deconvolutional Phase Retrieval via Convex Programming
- A Bayesian Approach to Generative Adversarial Imitation Learning
- Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization
- Visual Reinforcement Learning with Imagined Goals
- Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features
- On the Local Minima of the Empirical Risk
- Randomized Prior Functions for Deep Reinforcement Learning
- Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior
- How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery?
- Playing hard exploration games by watching YouTube
- Adversarially Robust Optimization with Gaussian Processes
- SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator
- Reducing Network Agnostophobia
- DAGs with NO TEARS: Continuous Optimization for Structure Learning
- Natasha 2: Faster Non-Convex Optimization Than SGD
- Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies
- Proximal Graphical Event Models
- Escaping Saddle Points in Constrained Optimization
- Geometrically Coupled Monte Carlo Sampling
- Heterogeneous Multi-output Gaussian Process Prediction
- On Coresets for Logistic Regression
- Scalable Laplacian K-modes
- GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
- Legendre Decomposition for Tensors
- Learning with SGD and Random Features
- Graphical model inference: Sequential Monte Carlo meets deterministic approximations
- Boolean Decision Rules via Column Generation
- KONG: Kernels for ordered-neighborhood graphs
- Boosting Black Box Variational Inference
- Fast greedy algorithms for dictionary selection with generalized sparsity constraints
- Quadrature-based features for kernel approximation
- Discretely Relaxing Continuous Variables for tractable Variational Inference
- Distributed $k$-Clustering for Data with Heavy Noise
- Statistical and Computational Trade-Offs in Kernel K-Means
- Implicit Reparameterization Gradients
- Do Less, Get More: Streaming Submodular Maximization with Subsampling
- Why Is My Classifier Discriminatory?
- Mirrored Langevin Dynamics
- Overlapping Clustering Models, and One (class) SVM to Bind Them All
- Human-in-the-Loop Interpretability Prior
- Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization
- Removing the Feature Correlation Effect of Multiplicative Noise
- Link Prediction Based on Graph Neural Networks
- Identification and Estimation of Causal Effects from Dependent Data
- Connectionist Temporal Classification with Maximum Entropy Regularization
- Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
- Causal Inference via Kernel Deviance Measures
- Entropy and mutual information in models of deep neural networks
- Automatic differentiation in ML: Where we are and where we should be going
- Removing Hidden Confounding by Experimental Grounding
- The committee machine: Computational to statistical gaps in learning a two-layers neural network
- Robust Subspace Approximation in a Stream
- Hyperbolic Neural Networks
- A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization
- Efficient nonmyopic batch active search
- Norm matters: efficient and accurate normalization schemes in deep networks
- Stochastic Chebyshev Gradient Descent for Spectral Optimization
- Interactive Structure Learning with Structural Query-by-Committee
- Constructing Fast Network through Deconstruction of Convolution
- LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning
- Contour location via entropy reduction leveraging multiple information sources
- A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
- Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames
- Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes
- Learning Libraries of Subroutines for Neurallyâ€“Guided Bayesian Program Induction
- Direct Runge-Kutta Discretization Achieves Acceleration
- Learning convex bounds for linear quadratic control policy synthesis
- Learning Loop Invariants for Program Verification
- Limited Memory Kelley's Method Converges for Composite Convex and Submodular Objectives
- Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning
- DeepProbLog: Neural Probabilistic Logic Programming
- (Probably) Concave Graph Matching
- Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
- Learning to Infer Graphics Programs from Hand-Drawn Images
- Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
- Bilevel learning of the Group Lasso structure
- Improving Neural Program Synthesis with Inferred Execution Traces
- Wasserstein Distributionally Robust Kalman Filtering
- Binary Classification from Positive-Confidence Data
- ResNet with one-neuron hidden layers is a Universal Approximator
- Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters
- Fully Understanding The Hashing Trick
- Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation
- Robust Hypothesis Testing Using Wasserstein Uncertainty Sets
- Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds
- Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
- Convergence of Cubic Regularization for Nonconvex Optimization under KL Property

## Talks

## Tutorials

- Adversarial Robustness: Theory and Practice
- Visualization for Machine Learning
- Scalable Bayesian Inference
- Unsupervised Deep Learning
- Common Pitfalls for Studying the Human Side of Machine Learning
- Negative Dependence, Stable Polynomials, and All That
- Automatic Machine Learning
- Statistical Learning Theory: a Hitchhiker's Guide
- Counterfactual Inference

Report issues here.