NeurIPS 2019 Posters

Random Path Selection for Continual Learning by Jathushan Rajasegaran et al.
Trust Region-Guided Proximal Policy Optimization by Yuhui Wang et al.
Thompson Sampling for Multinomial Logit Contextual Bandits by Min-hwan Oh et al.
Diffusion Improves Graph Learning by Johannes Klicpera et al.
Random Tessellation Forests by Shufei Ge et al.
Control What You Can: Intrinsically Motivated Task-Planning Agent by Sebastian Blaes et al.
Curriculum-guided Hindsight Experience Replay by Meng Fang et al.
On the convergence of single-call stochastic extra-gradient methods by Yu-Guan Hsieh et al.
On two ways to use determinantal point processes for Monte Carlo integration by Guillaume Gautier et al.
Data Cleansing for Models Trained with SGD by Satoshi Hara et al.
MixMatch: A Holistic Approach to Semi-Supervised Learning by David Berthelot et al.
Non-Asymptotic Pure Exploration by Solving Games by Rémy Degenne et al.
Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior by Cheng-Chun Hsu et al.
Adversarial Music: Real world Audio Adversary against Wake-word Detection System by Juncheng Li et al.
Neural Attribution for Semantic Bug-Localization in Student Programs by Rahul Gupta et al.
Fast and Accurate Stochastic Gradient Estimation by Beidi Chen et al.
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness by Fanny Yang et al.
Exponentially convergent stochastic k-PCA without variance reduction by Cheng Tang
How degenerate is the parametrization of neural networks with the ReLU activation function? by Dennis Maximilian Elbrächter et al.
GOT: An Optimal Transport framework for Graph comparison by Hermina Petric Maretic et al.
Connective Cognition Network for Directional Visual Commonsense Reasoning by Aming Wu et al.
Shadowing Properties of Optimization Algorithms by Antonio Orvieto et al.
Continuous-time Models for Stochastic Optimization Algorithms by Antonio Orvieto et al.
Differential Privacy Has Disparate Impact on Model Accuracy by Eugene Bagdasaryan et al.
On Robustness of Principal Component Regression by Anish Agarwal et al.
Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem by DongDong Ge et al.
Abstraction based Output Range Analysis for Neural Networks by Pavithra Prabhakar et al.
Comparing Unsupervised Word Translation Methods Step by Step by Mareike Hartmann et al.
On the Downstream Performance of Compressed Word Embeddings by Avner May et al.
Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition by Jinwoo Choi et al.
Approximating the Permanent by Sampling from Adaptive Partitions by Jonathan Kuck et al.
Multivariate Triangular Quantile Maps for Novelty Detection by Jingjing Wang et al.
Statistical Model Aggregation via Parameter Matching by Mikhail Yurochkin et al.
A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening by Gecia Bravo Hermsdorff et al.
Levenshtein Transformer by Jiatao Gu et al.
Image Captioning: Transforming Objects into Words by Simao Herdade et al.
Glyce: Glyph-vectors for Chinese Character Representations by Yuxian Meng et al.
Complexity of Highly Parallel Non-Smooth Convex Optimization by Sebastien Bubeck et al.
KNG: The K-Norm Gradient Mechanism by Matthew Reimherr et al.
Elliptical Perturbations for Differential Privacy by Matthew Reimherr et al.
Kernel Stein Tests for Multiple Model Comparison by Jen Ning Lim et al.
Optimal Decision Tree with Noisy Outcomes by Su Jia et al.
Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks by Gauthier Gidel et al.
Focused Quantization for Sparse CNNs by Yiren Zhao et al.
Selective Sampling-based Scalable Sparse Subspace Clustering by Shin Matsushima et al.
Competitive Gradient Descent by Florian Schaefer et al.
Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions by Ashia C. Wilson et al.
Towards Hardware-Aware Tractable Learning of Probabilistic Models by Laura I. Galindez Olascoaga et al.
Neural Relational Inference with Fast Modular Meta-learning by Ferran Alet et al.
Exact Combinatorial Optimization with Graph Convolutional Neural Networks by Maxime Gasse et al.
Large-scale optimal transport map estimation using projection pursuit by Cheng Meng et al.
Communication trade-offs for Local-SGD with large step size by Aymeric Dieuleveut et al.
Inherent Weight Normalization in Stochastic Neural Networks by Georgios Detorakis et al.
A Primal Dual Formulation For Deep Learning With Constraints by Yatin Nandwani et al.
Differentially Private Markov Chain Monte Carlo by Mikko Heikkilä et al.
Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Suffices by Santosh Vempala et al.
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods by Maher Nouiehed et al.
PerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Points by Siyuan Huang et al.
Near Neighbor: Who is the Fairest of Them All? by Sariel Har-Peled et al.
Paradoxes in Fair Machine Learning by Paul Goelz et al.
Novel positional encodings to enable tree-based transformers by Vighnesh Shiv et al.
Fully Dynamic Consistent Facility Location by Vincent Cohen-Addad et al.
Learning Disentangled Representations for Recommendation by Jianxin Ma et al.
Kernel quadrature with DPPs by Ayoub Belhadji et al.
Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains by Elliot Meyerson et al.
Learning Conditional Deformable Templates with Convolutional Networks by Adrian Dalca et al.
D-VAE: A Variational Autoencoder for Directed Acyclic Graphs by Muhan Zhang et al.
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations by Debraj Basu et al.
Likelihood Ratios for Out-of-Distribution Detection by Jie Ren et al.
Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift by Jasper Snoek et al.
Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation by Devin Reich et al.
Information-Theoretic Confidence Bounds for Reinforcement Learning by Xiuyuan Lu et al.
The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers by Alex Lu et al.
On the (In)fidelity and Sensitivity of Explanations by Chih-Kuan Yeh et al.
An Algorithm to Learn Polytree Networks with Hidden Nodes by Firoozeh Sepehr et al.
Minimal Variance Sampling in Stochastic Gradient Boosting by Bulat Ibragimov et al.
Bayesian Optimization under Heavy-tailed Payoffs by Sayak Ray Chowdhury et al.
A Self Validation Network for Object-Level Human Attention Estimation by Zehua Zhang et al.
Non-Cooperative Inverse Reinforcement Learning by Xiangyuan Zhang et al.
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks by Gaël Letarte et al.
Neural Similarity Learning by Weiyang Liu et al.
Learning from Label Proportions with Generative Adversarial Networks by Jiabin Liu et al.
Learning Macroscopic Brain Connectomes via Group-Sparse Factorization by Farzane Aminmansour et al.
Asymmetric Valleys: Beyond Sharp and Flat Local Minima by Haowei He et al.
Bayesian Optimization with Unknown Search Space by Huong Ha et al.
Online Normalization for Training Neural Networks by Vitaliy Chiley et al.
Planning in entropy-regularized Markov decision processes and games by Jean-Bastien Grill et al.
Cross Attention Network for Few-shot Classification by Ruibing Hou et al.
Space and Time Efficient Kernel Density Estimation in High Dimensions by Arturs Backurs et al.
Certifying Geometric Robustness of Neural Networks by Mislav Balunovic et al.
A First-Order Algorithmic Framework for Distributionally Robust Logistic Regression by JIAJIN LI et al.
Optimal Sampling and Clustering in the Stochastic Block Model by Se-Young Yun et al.
A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning by Wenhao Yang et al.
Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds by Bo Yang et al.
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm by Giulia Luise et al.
Constraint-based Causal Structure Learning with Consistent Separating Sets by Honghao Li et al.
Deep Scale-spaces: Equivariance Over Scale by Daniel Worrall et al.
R2D2: Reliable and Repeatable Detector and Descriptor by Jerome Revaud et al.
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization by Thijs Vogels et al.
Random Projections and Sampling Algorithms for Clustering of High-Dimensional Polygonal Curves by Stefan Meintrup et al.
Grid Saliency for Context Explanations of Semantic Segmentation by Lukas Hoyer et al.
Progressive Augmentation of GANs by Dan Zhang et al.
Backpropagation-Friendly Eigendecomposition by Wei Wang et al.
Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs by Lorenzo Dall'Amico et al.
Universal Invariant and Equivariant Graph Neural Networks by Nicolas Keriven et al.
From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI by Roman Beliy et al.
XNAS: Neural Architecture Search with Expert Advice by Niv Nayman et al.
Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation by Qiming ZHANG et al.
An Adaptive Empirical Bayesian Method for Sparse Deep Learning by Wei Deng et al.
Private Hypothesis Selection by Mark Bun et al.
Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis by Xihui Liu et al.
Transfer Learning via Minimizing the Performance Gap Between Domains by Boyu Wang et al.
REM: From Structural Entropy to Community Structure Deception by Yiwei Liu et al.
NAT: Neural Architecture Transformer for Accurate and Compact Architectures by Yong Guo et al.
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks by Qiyang Li et al.
Online Convex Matrix Factorization with Representative Regions by Jianhao Peng et al.
Quantum Embedding of Knowledge for Reasoning by Dinesh Garg et al.
A Composable Specification Language for Reinforcement Learning Tasks by Kishor Jothimurugan et al.
Multi-Criteria Dimensionality Reduction with Applications to Fairness by Uthaipon Tantipongpipat et al.
Deep Leakage from Gradients by Ligeng Zhu et al.
Pure Exploration with Multiple Correct Answers by Rémy Degenne et al.
Are Disentangled Representations Helpful for Abstract Visual Reasoning? by Sjoerd van Steenkiste et al.
Recovering Bandits by Ciara Pike-Burke et al.
Object landmark discovery through unsupervised adaptation by Enrique Sanchez et al.
Exploration via Hindsight Goal Generation by Zhizhou Ren et al.
Accurate, reliable and fast robustness evaluation by Wieland Brendel et al.
Recurrent Space-time Graph Neural Networks by Andrei Nicolicioiu et al.
Streaming Bayesian Inference for Crowdsourced Classification by Edoardo Manino et al.
Causal Regularization by Dominik Janzing
Deep Multimodal Multilinear Fusion with High-order Polynomial Pooling by Ming Hou et al.
Input Similarity from the Neural Network Perspective by Guillaume Charpiat et al.
Implicit Regularization for Optimal Sparse Recovery by Tomas Vaskevicius et al.
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks by Aaron Voelker et al.
On the Power and Limitations of Random Features for Understanding Neural Networks by Gilad Yehudai et al.
Learning-Based Low-Rank Approximations by Piotr Indyk et al.
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference by Cole Hurwitz et al.
Better Transfer Learning with Inferred Successor Maps by Tamas Madarasz et al.
A Tensorized Transformer for Language Modeling by Xindian Ma et al.
Domes to Drones: Self-Supervised Active Triangulation for 3D Human Pose Reconstruction by Aleksis Pirinen et al.
Latent Ordinary Differential Equations for Irregularly-Sampled Time Series by Yulia Rubanova et al.
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks by Difan Zou et al.
Ultra Fast Medoid Identification via Correlated Sequential Halving by Tavor Baharav et al.
Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization by Koen Helwegen et al.
Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments by Vasilis Syrgkanis et al.
Equipping Experts/Bandits with Long-term Memory by Kai Zheng et al.
Necessary and Sufficient Geometries for Gradient Methods by Daniel Levy et al.
Co-Generation with GANs using AIS based HMC by Tiantian Fang et al.
General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme by Tao Sun et al.
Incremental Scene Synthesis by Benjamin Planche et al.
Multiview Aggregation for Learning Category-Specific Shape Reconstruction by Srinath Sridhar et al.
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis by Kundan Kumar et al.
Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games by Kaiqing Zhang et al.
Fast structure learning with modular regularization by Greg Ver Steeg et al.
Predicting the Politics of an Image Using Webly Supervised Data by Christopher Thomas et al.
Image Synthesis with a Single (Robust) Classifier by Shibani Santurkar et al.
PRNet: Self-Supervised Learning for Partial-to-Partial Registration by Yue Wang et al.
Combining Generative and Discriminative Models for Hybrid Inference by Victor Garcia Satorras et al.
Chasing Ghosts: Instruction Following as Bayesian State Tracking by Peter Anderson et al.
Learning Sample-Specific Models with Low-Rank Personalized Regression by Ben Lengerich et al.
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction by Difan Zou et al.
An Improved Analysis of Training Over-parameterized Deep Neural Networks by Difan Zou et al.
On Adversarial Mixup Resynthesis by Christopher Beckham et al.
Putting An End to End-to-End: Gradient-Isolated Learning of Representations by Sindy Löwe et al.
Screening Sinkhorn Algorithm for Regularized Optimal Transport by Mokhtar Z. Alaya et al.
Learning low-dimensional state embeddings and metastable clusters from time series data by Yifan Sun et al.
Weight Agnostic Neural Networks by Adam Gaier et al.
Combinatorial Bayesian Optimization using the Graph Cartesian Product by Changyong Oh et al.
Emergence of Object Segmentation in Perturbed Generative Models by Adam Bielski et al.
Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling by Andrey Kolobov et al.
Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries by Fuwen Tan et al.
Block Coordinate Regularization by Denoising by Yu Sun et al.
Kernel Instrumental Variable Regression by Rahul Singh et al.
Meta-Surrogate Benchmarking for Hyperparameter Optimization by Aaron Klein et al.
Learning Generalizable Device Placement Algorithms for Distributed Machine Learning by ravichandra addanki et al.
DATA: Differentiable ArchiTecture Approximation by Jianlong Chang et al.
Wasserstein Weisfeiler-Lehman Graph Kernels by Matteo Togninalli et al.
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation by Ruibo Tu et al.
A Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation by Xueying Bai et al.
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians by Gautam Kamath et al.
Limitations of Lazy Training of Two-layers Neural Network by Song Mei et al.
Making AI Forget You: Data Deletion in Machine Learning by Antonio Ginart et al.
Global Sparse Momentum SGD for Pruning Very Deep Neural Networks by Xiaohan Ding et al.
Policy Poisoning in Batch Reinforcement Learning and Control by Yuzhe Ma et al.
Uncertainty-based Continual Learning with Adaptive Regularization by Hongjoon Ahn et al.
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning by Valerio Perrone et al.
Compacting, Picking and Growing for Unforgetting Continual Learning by Ching-Yi Hung et al.
Consistency-based Semi-supervised Learning for Object detection by Jisoo Jeong et al.
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics by Niru Maheswaranathan et al.
Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks by Mahyar Fazlyab et al.
Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems by Asma Ghandeharioun et al.
What the Vec? Towards Probabilistically Grounded Embeddings by Carl Allen et al.
Multi-relational Poincaré Graph Embeddings by Ivana Balazevic et al.
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers by Igor Fedorov et al.
Learning Reward Machines for Partially Observable Reinforcement Learning by Rodrigo Toro Icarte et al.
Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes by Jun Yang et al.
Flattening a Hierarchical Clustering through Active Learning by Fabio Vitale et al.
Budgeted Reinforcement Learning in Continuous State Space by Nicolas Carrara et al.
Max-value Entropy Search for Multi-Objective Bayesian Optimization by Syrine Belakaria et al.
MaCow: Masked Convolutional Generative Flow by Xuezhe Ma et al.
Average Individual Fairness: Algorithms, Generalization and Experiments by Saeed Sharifi-Malvajerdi et al.
On the Utility of Learning about Humans for Human-AI Coordination by Micah Carroll et al.
Manifold denoising by Nonlinear Robust Principal Component Analysis by He Lyu et al.
Surround Modulation: A Bio-inspired Connectivity Structure for Convolutional Neural Networks by Hosein Hasani et al.
Rethinking the CSC Model for Natural Images by Dror Simon et al.
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning by Andreas Kirsch et al.
Sample Complexity of Learning Mixture of Sparse Linear Regressions by Akshay Krishnamurthy et al.
Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights by Maria Jahja et al.
Adaptively Aligned Image Captioning via Adaptive Attention Time by Lun Huang et al.
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement by Chao Yang et al.
ODE2VAE: Deep generative second order ODEs with Bayesian neural networks by Cagatay Yildiz et al.
Full-Gradient Representation for Neural Network Visualization by Suraj Srinivas et al.
Bayesian Batch Active Learning as Sparse Subset Approximation by Robert Pinsler et al.
The Synthesis of XNOR Recurrent Neural Networks with Stochastic Logic by Arash Ardakani et al.
Reliable training and estimation of variance networks by Nicki Skafte et al.
Accurate Layerwise Interpretable Competence Estimation by Vickram Rajendran et al.
Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD by PHUONG_HA NGUYEN et al.
q-means: A quantum algorithm for unsupervised machine learning by Iordanis Kerenidis et al.
Park: An Open Platform for Learning-Augmented Computer Systems by Hongzi Mao et al.
Self-Supervised Generalisation with Meta Auxiliary Learning by Shikun Liu et al.
Learning Nonsymmetric Determinantal Point Processes by Mike Gartrell et al.
Efficient Deep Approximation of GMMs by Shirin Jalali et al.
Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural Networks by Gunjan Verma et al.
Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback by Shuai Zheng et al.
Theoretical Analysis of Adversarial Learning: A Minimax Approach by Zhuozhuo Tu et al.
Hyperparameter Learning via Distributional Transfer by Ho Chung Law et al.
Expressive power of tensor-network factorizations for probabilistic modeling by Ivan Glasser et al.
High-Quality Self-Supervised Deep Image Denoising by Samuli Laine et al.
Bridging Machine Learning and Logical Reasoning by Abductive Learning by Wang-Zhou Dai et al.
Zero-shot Knowledge Transfer via Adversarial Belief Matching by Paul Micaelli et al.
Transferable Normalization: Towards Improving Transferability of Deep Neural Networks by Ximei Wang et al.
Beating SGD Saturation with Tail-Averaging and Minibatching by Nicole Muecke et al.
Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning by Erwan Lecarpentier et al.
Stability of Graph Scattering Transforms by Fernando Gama et al.
Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees by Muhammad Osama et al.
Recurrent Kernel Networks by Dexiong Chen et al.
DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging by Matthieu SIMEONI et al.
Weighted Linear Bandits for Non-Stationary Environments by Yoan Russac et al.
Limits of Private Learning with Access to Public Data by Raef Bassily et al.
Private Stochastic Convex Optimization with Optimal Rates by Raef Bassily et al.
Unsupervised Scalable Representation Learning for Multivariate Time Series by Jean-Yves Franceschi et al.
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses by Ulysse Marteau-Ferey et al.
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods by Belhal Karimi et al.
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift by Stephan Rabanser et al.
Learning Representations for Time Series Clustering by Qianli Ma et al.
Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders by Natasa Tagasovska et al.
Single-Model Uncertainties for Deep Learning by Natasa Tagasovska et al.
Generalized Off-Policy Actor-Critic by Shangtong Zhang et al.
DAC: The Double Actor-Critic Architecture for Learning Options by Shangtong Zhang et al.
Trivializations for Gradient-Based Optimization on Manifolds by Mario Lezcano Casado
SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits by Etienne Boursier et al.
Combinatorial Bandits with Relative Feedback by Aadirupa Saha et al.
Selecting causal brain features with a single conditional independence test per feature by Atalanti Mastakouri et al.
Low-Complexity Nonparametric Bayesian Online Prediction with Universal Guarantees by Alix LHERITIER et al.
A Little Is Enough: Circumventing Defenses For Distributed Learning by Moran Baruch et al.
Optimal Stochastic and Online Learning with Individual Iterates by Yunwen Lei et al.
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs by Yusuke Tanaka et al.
The Point Where Reality Meets Fantasy: Mixed Adversarial Generators for Image Splice Detection by Vladimir V. Kniaz et al.
Fast Decomposable Submodular Function Minimization using Constrained Total Variation by Senanayak Sesh Kumar Karri et al.
Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models by Vincent LE GUEN et al.
Implicit Posterior Variational Inference for Deep Gaussian Processes by Haibin YU et al.
Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters by Alberto Maria Metelli et al.
Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting by Jun Shu et al.
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle by Dinghuai Zhang et al.
Inherent Tradeoffs in Learning Fair Representations by Han Zhao et al.
Learning Neural Networks with Adaptive Regularization by Han Zhao et al.
Identifying Causal Effects via Context-specific Independence Relations by Santtu Tikka et al.
Are sample means in multi-armed bandits positively or negatively biased? by Jaehyeok Shin et al.
Post training 4-bit quantization of convolutional networks for rapid-deployment by Ron Banner et al.
Multiclass Performance Metric Elicitation by Gaurush Hiranandani et al.
Universal Approximation of Input-Output Maps by Temporal Convolutional Nets by Joshua Hanson et al.
Inverting Deep Generative models, One layer at a time by Qi Lei et al.
Primal-Dual Block Generalized Frank-Wolfe by Qi Lei et al.
Sparse High-Dimensional Isotonic Regression by David Gamarnik et al.
Optimizing Generalized PageRank Methods for Seed-Expansion Community Detection by Pan Li et al.
Doubly-Robust Lasso Bandit by Gi-Soo Kim et al.
MAVEN: Multi-Agent Variational Exploration by Anuj Mahajan et al.
iSplit LBI: Individualized Partial Ranking with Ties via Split LBI by Qianqian Xu et al.
Uniform convergence may be unable to explain generalization in deep learning by Vaishnavh Nagarajan et al.
Learning to Self-Train for Semi-Supervised Few-Shot Classification by Xinzhe Li et al.
Multi-mapping Image-to-Image Translation via Learning Disentanglement by Xiaoming Yu et al.
Multi-objective Bayesian optimisation with preferences over objectives by Majid Abdolshah et al.
Inducing brain-relevant bias in natural language processing models by Dan Schwartz et al.
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition by Satoshi Tsutsui et al.
On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons by Wenbo Ren et al.
Bayesian Joint Estimation of Multiple Graphical Models by Lingrui Gan et al.
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation by Shashank Rajput et al.
Spherical Text Embedding by Yu Meng et al.
Quality Aware Generative Adversarial Networks by KANCHARLA PARIMALA et al.
Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression by Ruidi Chen et al.
Knowledge Extraction with No Observable Data by Jaemin Yoo et al.
Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products by Tharun Kumar Reddy Medini et al.
Embedding Symbolic Knowledge into Deep Networks by Xie Yaqi et al.
Rethinking Kernel Methods for Node Representation Learning on Graphs by Yu Tian et al.
Distributed Low-rank Matrix Factorization With Exact Consensus by Zhihui Zhu et al.
Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation by Ke Wang et al.
Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization by Farzin Haddadpour et al.
Region Mutual Information Loss for Semantic Segmentation by Shuai Zhao et al.
Multi-marginal Wasserstein GAN by Jiezhang Cao et al.
DM2C: Deep Mixed-Modal Clustering by Yangbangyan Jiang et al.
Learning Robust Options by Conditional Value at Risk Optimization by Takuya Hiraoka et al.
First-order methods almost always avoid saddle points: The case of vanishing step-sizes by Ioannis Panageas et al.
DetNAS: Backbone Search for Object Detection by Yukang Chen et al.
Learning from brains how to regularize machines by Zhe Li et al.
Learning to Propagate for Graph Meta-Learning by LU LIU et al.
Initialization of ReLUs for Dynamical Isometry by Rebekka Burkholz et al.
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks by Sitao Luan et al.
Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples by Tengyu Xu et al.
Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards by Siyuan Li et al.
MonoForest framework for tree ensemble analysis by Igor Kuralenok et al.
Network Pruning via Transformable Architecture Search by Xuanyi Dong et al.
Multiway clustering via tensor block models by Miaoyan Wang et al.
Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning by Wenjie Shi et al.
Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition by Xuesong Niu et al.
Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization by Yujiao Shi et al.
This Looks Like That: Deep Learning for Interpretable Image Recognition by Chaofan Chen et al.
Deep Set Prediction Networks by Yan Zhang et al.
Graph Agreement Models for Semi-Supervised Learning by Otilia Stretcu et al.
Learning metrics for persistence-based summaries and applications for graph classification by Qi Zhao et al.
Unsupervised Meta-Learning for Few-Shot Image Classification by Siavash Khodadadeh et al.
Finite-Sample Analysis for SARSA with Linear Function Approximation by Shaofeng Zou et al.
Optimal Sparse Decision Trees by Xiyang Hu et al.
Learning Data Manipulation for Augmentation and Weighting by Zhiting Hu et al.
Gaussian-Based Pooling for Convolutional Neural Networks by Takumi Kobayashi
Machine Teaching of Active Sequential Learners by Tomi Peltola et al.
Learning step sizes for unfolded sparse coding by Pierre Ablin et al.
Online-Within-Online Meta-Learning by Giulia Denevi et al.
Unified Language Model Pre-training for Natural Language Understanding and Generation by Li Dong et al.
Cross-Domain Transferability of Adversarial Perturbations by Muhammad Muzammal Naseer et al.
Large Memory Layers with Product Keys by Guillaume Lample et al.
Positive-Unlabeled Compression on the Cloud by Yixing Xu et al.
Multiclass Learning from Contradictions by Sauptik Dhar et al.
Efficient Meta Learning via Minibatch Proximal Update by Pan Zhou et al.
Mining GOLD Samples for Conditional GANs by Sangwoo Mo et al.
DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters by W. O. K. Asiri Suranga Wijesinghe et al.
Bayesian Learning of Sum-Product Networks by Martin Trapp et al.
Conformal Prediction Under Covariate Shift by Ryan J. Tibshirani et al.
Balancing Efficiency and Fairness in On-Demand Ridesourcing by Nixie S. Lesmana et al.
Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update by Su Young Lee et al.
Distinguishing Distributions When Samples Are Strategically Transformed by Hanrui Zhang et al.
State Aggregation Learning from Markov Transition Data by Yaqi Duan et al.
The Landscape of Non-convex Empirical Risk with Degenerate Population Risk by Shuang Li et al.
Zero-shot Learning via Simultaneous Generating and Learning by Hyeonwoo Yu et al.
Memory-oriented Decoder for Light Field Salient Object Detection by Miao Zhang et al.
Learning Erdos-Renyi Random Graphs via Edge Detecting Queries by Zihan Li et al.
Fast Low-rank Metric Learning for Large-scale and High-dimensional Data by Han Liu et al.
Learning Perceptual Inference by Contrasting by Chi Zhang et al.
Neural Spline Flows by Conor Durkan et al.
Coresets for Archetypal Analysis by Sebastian Mair et al.
Sequential Neural Processes by Gautam Singh et al.
Learning Mixtures of Plackett-Luce Models from Structured Partial Orders by Zhibing Zhao et al.
Efficient Forward Architecture Search by Hanzhang Hu et al.
Domain Generalization via Model-Agnostic Learning of Semantic Features by Qi Dou et al.
Variational Bayesian Decision-making for Continuous Utilities by Tomasz Kuśmierczyk et al.
Deep Signature Transforms by Patrick Kidger et al.
Real-Time Reinforcement Learning by Simon Ramstedt et al.
Safe Exploration for Interactive Machine Learning by Matteo Turchetta et al.
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control by Armin Lederer et al.
Implicit Generation and Modeling with Energy Based Models by Yilun Du et al.
Adversarial Examples Are Not Bugs, They Are Features by Andrew Ilyas et al.