ICML 2019 Posters
Bayesian Deconditional Kernel Mean Embeddings by Kelvin Hsu et al.
Differentiable Linearized ADMM by Xingyu Xie et al.
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition by Yao Qin et al.
Revisiting precision recall definition for generative modeling by Loic Simon et al.
Locally Private Bayesian Inference for Count Models by Aaron Schein et al.
Breaking Inter-Layer Co-Adaptation by Classifier Anonymization by Ikuro Sato et al.
Multivariate Submodular Optimization by Richard Santiago et al.
Adaptive Neural Trees by Ryutaro Tanno et al.
Faster Attend-Infer-Repeat with Tractable Probabilistic Models by Karl Stelzner et al.
The Evolved Transformer by David So et al.
Distribution calibration for regression by Hao Song et al.
Fast Algorithm for Generalized Multinomial Models with Ranking Data by Jiaqi Gu et al.
Learning to Prove Theorems via Interacting with Proof Assistants by Kaiyu Yang et al.
Bayesian Optimization of Composite Functions by Raul Astudillo et al.
Blended Conditonal Gradients by Gábor Braun et al.
Noise2Self: Blind Denoising by Self-Supervision by Joshua Batson et al.
Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances by Bugra Can et al.
Stein Point Markov Chain Monte Carlo by Wilson Ye Chen et al.
Fast Direct Search in an Optimally Compressed Continuous Target Space for Efficient Multi-Label Active Learning by Weishi Shi et al.
Learning to Convolve: A Generalized Weight-Tying Approach by Nichita Diaconu et al.
On the statistical rate of nonlinear recovery in generative models with heavy-tailed data by Xiaohan Wei et al.
Imitation Learning from Imperfect Demonstration by Yueh-Hua Wu et al.
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication by Anastasia Koloskova et al.
On Scalable and Efficient Computation of Large Scale Optimal Transport by Yujia Xie et al.
Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops by Limor Gultchin et al.
Understanding Geometry of Encoder-Decoder CNNs by Jong Chul Ye et al.
Complementary-Label Learning for Arbitrary Losses and Models by Takashi Ishida et al.
Learning Optimal Fair Policies by Razieh Nabi et al.
A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent by Yongqiang Cai et al.
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions by Hao Wang et al.
On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference by Rohin Shah et al.
Understanding MCMC Dynamics as Flows on the Wasserstein Space by Chang Liu et al.
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search by Youhei Akimoto et al.
Remember and Forget for Experience Replay by Guido Novati et al.
Loss Landscapes of Regularized Linear Autoencoders by Daniel Kunin et al.
Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement by Wouter Kool et al.
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting by Ritchie Zhao et al.
On Learning Invariant Representations for Domain Adaptation by Han Zhao et al.
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning by Kyunghwan Son et al.
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction by Giulia Luise et al.
Unreproducible Research is Reproducible by Xavier Bouthillier et al.
Hybrid Models with Deep and Invertible Features by Eric Nalisnick et al.
Co-manifold learning with missing data by Gal Mishne et al.
Self-Supervised Exploration via Disagreement by Deepak Pathak et al.
Generalized Majorization-Minimization by Sobhan Naderi Parizi et al.
Fingerprint Policy Optimisation for Robust Reinforcement Learning by Supratik Paul et al.
Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs by Lingbing Guo et al.
On Variational Bounds of Mutual Information by Ben Poole et al.
Learning to Collaborate in Markov Decision Processes by Goran Radanovic et al.
Temporal Gaussian Mixture Layer for Videos by Aj Piergiovanni et al.
Cognitive model priors for predicting human decisions by David D. Bourgin et al.
Transfer of Samples in Policy Search via Multiple Importance Sampling by Andrea Tirinzoni et al.
Non-Monotonic Sequential Text Generation by Sean Welleck et al.
SWALP : Stochastic Weight Averaging in Low Precision Training by Guandao Yang et al.
End-to-End Probabilistic Inference for Nonstationary Audio Analysis by William Wilkinson et al.
Rademacher Complexity for Adversarially Robust Generalization by Dong Yin et al.
Deep Compressed Sensing by Yan Wu et al.
Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel k k -means Clustering by Taisuke Yasuda et al.
LegoNet: Efficient Convolutional Neural Networks with Lego Filters by Zhaohui Yang et al.
Supervised Hierarchical Clustering with Exponential Linkage by Nishant Yadav et al.
ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation by Yuzhe Yang et al.
Information-Theoretic Considerations in Batch Reinforcement Learning by Jinglin Chen et al.
Maximum Entropy-Regularized Multi-Goal Reinforcement Learning by Rui Zhao et al.
Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits by Martin Zhang et al.
Large-Scale Sparse Kernel Canonical Correlation Analysis by Viivi Uurtio et al.
Incremental Randomized Sketching for Online Kernel Learning by Xiao Zhang et al.
Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models by Stefano Sarao Mannelli et al.
Trimming the L1 Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning by Jihun Yun et al.
Projections for Approximate Policy Iteration Algorithms by Riad Akrour et al.
Multi-objective training of Generative Adversarial Networks with multiple discriminators by Isabela Albuquerque et al.
Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference by Yatao Bian et al.
Scalable Fair Clustering by Arturs Backurs et al.
Analyzing Federated Learning through an Adversarial Lens by Arjun Nitin Bhagoji et al.
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing by Sami Abu-El-Haija et al.
Provable Guarantees for Gradient-Based Meta-Learning by Maria-Florina Balcan et al.
PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits by Arghya Roy Chaudhuri et al.
Communication-Constrained Inference and the Role of Shared Randomness by Jayadev Acharya et al.
Decentralized Exploration in Multi-Armed Bandits by Raphael Feraud et al.
Band-limited Training and Inference for Convolutional Neural Networks by Adam Dziedzic et al.
On the Connection Between Adversarial Robustness and Saliency Map Interpretability by Christian Etmann et al.
Graph U-Nets by Hongyang Gao et al.
Neural Network Attributions: A Causal Perspective by Aditya Chattopadhyay et al.
Robust Learning from Untrusted Sources by Nikola Konstantinov et al.
Lorentzian Distance Learning for Hyperbolic Representations by Marc Law et al.
Kernel Mean Matching for Content Addressability of GANs by Wittawat Jitkrittum et al.
Detecting Overlapping and Correlated Communities without Pure Nodes: Identifiability and Algorithm by Kejun Huang et al.
On the Universality of Invariant Networks by Haggai Maron et al.
Sublinear Time Nearest Neighbor Search over Generalized Weighted Space by Yifan Lei et al.
Disentangled Graph Convolutional Networks by Jianxin Ma et al.
MONK Outlier-Robust Mean Embedding Estimation by Median-of-Means by Matthieu Lerasle et al.
Using Pre-Training Can Improve Model Robustness and Uncertainty by Dan Hendrycks et al.
Adaptive Sensor Placement for Continuous Spaces by James Grant et al.
TarMAC: Targeted Multi-Agent Communication by Abhishek Das et al.
Imitating Latent Policies from Observation by Ashley Edwards et al.
Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA by Jordan Awan et al.
Learning to Generalize from Sparse and Underspecified Rewards by Rishabh Agarwal et al.
Adjustment Criteria for Generalizing Experimental Findings by Juan Correa et al.
Demystifying Dropout by Hongchang Gao et al.
Flat Metric Minimization with Applications in Generative Modeling by Thomas Möllenhoff et al.
Guarantees for Spectral Clustering with Fairness Constraints by Matthäus Kleindessner et al.
Multi-Object Representation Learning with Iterative Variational Inference by Klaus Greff et al.
Target-Based Temporal-Difference Learning by Donghwan Lee et al.
Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group by Mario Lezcano-Casado et al.
SELFIE: Refurbishing Unclean Samples for Robust Deep Learning by Hwanjun Song et al.
Fair k-Center Clustering for Data Summarization by Matthäus Kleindessner et al.
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations by Wu Lin et al.
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap by Edwin Fong et al.
Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization by Baojian Zhou et al.
Error Feedback Fixes SignSGD and other Gradient Compression Schemes by Sai Praneeth Karimireddy et al.
GMNN: Graph Markov Neural Networks by Meng Qu et al.
Lipschitz Generative Adversarial Nets by Zhiming Zhou et al.
ELF OpenGo: an analysis and open reimplementation of AlphaZero by Yuandong Tian et al.
Nonparametric Bayesian Deep Networks with Local Competition by Konstantinos Panousis et al.
CompILE: Compositional Imitation Learning and Execution by Thomas Kipf et al.
Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication by Pedro Soto et al.
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning by Cédric Colas et al.
The Wasserstein Transform by Facundo Memoli et al.
Submodular Streaming in All Its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity by Ehsan Kazemi et al.
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning by Huaiyu Li et al.
Dirichlet Simplex Nest and Geometric Inference by Mikhail Yurochkin et al.
Bayesian Nonparametric Federated Learning of Neural Networks by Mikhail Yurochkin et al.
Dimensionality Reduction for Tukey Regression by Kenneth Clarkson et al.
Theoretically Principled Trade-off between Robustness and Accuracy by Hongyang Zhang et al.
Kernel Normalized Cut: a Theoretical Revisit by Yoshikazu Terada et al.
Target Tracking for Contextual Bandits: Application to Demand Side Management by Margaux Brégère et al.
Deep Gaussian Processes with Importance-Weighted Variational Inference by Hugh Salimbeni et al.
Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding by Muhammad Osama et al.
Composing Value Functions in Reinforcement Learning by Benjamin Van Niekerk et al.
Online Convex Optimization in Adversarial Markov Decision Processes by Aviv Rosenberg et al.
A Persistent Weisfeiler-Lehman Procedure for Graph Classification by Bastian Rieck et al.
First-Order Adversarial Vulnerability of Neural Networks and Input Dimension by Carl-Johann Simon-Gabriel et al.
Subspace Robust Wasserstein Distances by François-Pierre Paty et al.
Improving Adversarial Robustness via Promoting Ensemble Diversity by Tianyu Pang et al.
Voronoi Boundary Classification: A High-Dimensional Geometric Approach via Weighted Monte Carlo Integration by Vladislav Polianskii et al.
Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation by Ruohan Wang et al.
Learning Discrete Structures for Graph Neural Networks by Luca Franceschi et al.
A Contrastive Divergence for Combining Variational Inference and MCMC by Francisco Ruiz et al.
Context-Aware Zero-Shot Learning for Object Recognition by Eloi Zablocki et al.
Reinforcement Learning in Configurable Continuous Environments by Alberto Maria Metelli et al.
On the Complexity of Approximating Wasserstein Barycenters by Alexey Kroshnin et al.
Topological Data Analysis of Decision Boundaries with Application to Model Selection by Karthikeyan Natesan Ramamurthy et al.
Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient Algorithms by Ashok Makkuva et al.
SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver by Po-Wei Wang et al.
Data Poisoning Attacks on Stochastic Bandits by Fang Liu et al.
Relational Pooling for Graph Representations by Ryan Murphy et al.
Accelerated Flow for Probability Distributions by Amirhossein Taghvaei et al.
Mallows ranking models: maximum likelihood estimate and regeneration by Wenpin Tang
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables by Kate Rakelly et al.
Learning to Optimize Multigrid PDE Solvers by Daniel Greenfeld et al.
Towards Understanding Knowledge Distillation by Mary Phuong et al.
Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning by Dong Yin et al.
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence by Yi Xu et al.
Multi-Agent Adversarial Inverse Reinforcement Learning by Lantao Yu et al.
A Recurrent Neural Cascade-based Model for Continuous-Time Diffusion by Sylvain Lamprier
Improving Neural Language Modeling via Adversarial Training by Dilin Wang et al.
Processing Megapixel Images with Deep Attention-Sampling Models by Angelos Katharopoulos et al.
Noisy Dual Principal Component Pursuit by Tianyu Ding et al.
Adversarial camera stickers: A physical camera-based attack on deep learning systems by Juncheng Li et al.
Imputing Missing Events in Continuous-Time Event Streams by Hongyuan Mei et al.
Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment by Chen Huang et al.
POPQORN: Quantifying Robustness of Recurrent Neural Networks by Ching-Yun Ko et al.
Learning with Bad Training Data via Iterative Trimmed Loss Minimization by Yanyao Shen et al.
Formal Privacy for Functional Data with Gaussian Perturbations by Ardalan Mirshani et al.
Natural Analysts in Adaptive Data Analysis by Tijana Zrnic et al.
Improved Parallel Algorithms for Density-Based Network Clustering by Mohsen Ghaffari et al.
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization by Giulia Denevi et al.
Does Data Augmentation Lead to Positive Margin? by Shashank Rajput et al.
Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning by Casey Chu et al.
Unsupervised Deep Learning by Neighbourhood Discovery by Jiabo Huang et al.
Classification from Positive, Unlabeled and Biased Negative Data by Yu-Guan Hsieh et al.
Robust Inference via Generative Classifiers for Handling Noisy Labels by Kimin Lee et al.
Similarity of Neural Network Representations Revisited by Simon Kornblith et al.
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data by Vatsal Sharan et al.
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models by Michael Oberst et al.
Cross-Domain 3D Equivariant Image Embeddings by Carlos Esteves et al.
Random Matrix Improved Covariance Estimation for a Large Class of Metrics by Malik Tiomoko et al.
Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation by Ahsan Alvi et al.
Learning Optimal Linear Regularizers by Matthew Streeter
Adaptive Scale-Invariant Online Algorithms for Learning Linear Models by Michal Kempka et al.
Exploring the Landscape of Spatial Robustness by Logan Engstrom et al.
Combating Label Noise in Deep Learning using Abstention by Sunil Thulasidasan et al.
Differentially Private Fair Learning by Matthew Jagielski et al.
Characterizing Well-Behaved vs. Pathological Deep Neural Networks by Antoine Labatie
On Medians of (Randomized) Pairwise Means by Pierre Laforgue et al.
Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance by Dasaem Jeong et al.
Robust Decision Trees Against Adversarial Examples by Hongge Chen et al.
Action Robust Reinforcement Learning and Applications in Continuous Control by Chen Tessler et al.
Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities by Octavian Ganea et al.
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations by Daniel Brown et al.
Weakly-Supervised Temporal Localization via Occurrence Count Learning by Julien Schroeter et al.
AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs by Gabriele Abbati et al.
GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver by David John et al.
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets by Chen Zhu et al.
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations by Alex Lamb et al.
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation by Marco Ancona et al.
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems by Ted Meeds et al.
Towards a Deep and Unified Understanding of Deep Neural Models in NLP by Chaoyu Guan et al.
Rotation Invariant Householder Parameterization for Bayesian PCA by Rajbir Nirwan et al.
Sorting Out Lipschitz Function Approximation by Cem Anil et al.
Guided evolutionary strategies: augmenting random search with surrogate gradients by Niru Maheswaranathan et al.
Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension by Jisu Kim et al.
Metropolis-Hastings Generative Adversarial Networks by Ryan Turner et al.
Distributed Learning over Unreliable Networks by Chen Yu et al.
Active Learning for Probabilistic Structured Prediction of Cuts and Matchings by Sima Behpour et al.
Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models by Dilin Wang et al.
Policy Consolidation for Continual Reinforcement Learning by Christos Kaplanis et al.
Active Embedding Search via Noisy Paired Comparisons by Gregory Canal et al.