The 37th International Conference on Machine Learning (ICML 2020) will be held in Vienna, Austria from 12 July to 18 July, 2020. COMPSAC 2020 physical paper presentations sessions will not take place. We have plans to enable most normal conference events virtually. During this time, it is possible that there may be some interruptions in access to these archives. ICML 2020 Virtualization Survey Responses - Results. Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning, XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning, Projection-free Distributed Online Convex Optimization with $O(\sqrt{T})$ Communication Complexity, Why bigger is not always better: on finite and infinite neural networks, Double-Loop Unadjusted Langevin Algorithm, Fast OSCAR and OWL with Safe Screening Rules, Convergence of a Stochastic Gradient Method with Momentum for Non-Smooth Non-Convex Optimization, Learning Human Objectives by Evaluating Hypothetical Behavior, Hierarchical Generation of Molecular Graphs using Structural Motifs, Consistent Structured Prediction with Max-Min Margin Markov Networks, Bisection-Based Pricing for Repeated Contextual Auctions against Strategic Buyer, Smaller, more accurate regression forests using tree alternating optimization, Second-Order Provable Defenses against Adversarial Attacks, Cost-effective Interactive Attention Learning with Neural Attention Process, Projective Preferential Bayesian Optimization, A Graph to Graphs Framework for Retrosynthesis Prediction, PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization, Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders, Online Control of the False Coverage Rate and False Sign Rate, Optimal Continual Learning has Perfect Memory and is NP-hard, Tightening Exploration in Upper Confidence Reinforcement Learning, Abstraction Mechanisms Predict Generalization in Deep Neural Networks, Fairwashing explanations with off-manifold detergent, Reliable Fidelity and Diversity Metrics for Generative Models, Problems with Shapley-value-based explanations as feature importance measures, Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health, Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks, Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs, When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment, A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation, The Cost-free Nature of Optimally Tuning Tikhonov Regularizers and Other Ordered Smoothers, Voice Separation with an Unknown Number of Multiple Speakers, Feature Noise Induces Loss Discrepancy Across Groups, Coresets for Clustering in Graphs of Bounded Treewidth, Orthogonalized SGD and Nested Architectures for Anytime Neural Networks, Variance Reduction and Quasi-Newton for Particle-Based Variational Inference, DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Images, Coresets for Data-efficient Training of Machine Learning Models, The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks, A Mean Field Analysis Of Deep ResNet And Beyond: Towards Provably Optimization Via Overparameterization From Depth, Aligned Cross Entropy for Non-Autoregressive Machine Translation, Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation, Near-Tight Margin-Based Generalization Bounds for Support Vector Machines, Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation, Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making, Batch Reinforcement Learning with Hyperparameter Gradients, Optimal Randomized First-Order Methods for Least-Squares Problems. Deep learning has achieved great success in a variety of tasks such as recognizing objects in images, predicting the sentiment of sentences, or image/speech synthesis by training on a large-amount of data. Learning General-Purpose Controllers via Locally Communicating Sensorimotor Modules, Retrieval Augmented Language Model Pre-Training, Bayesian Graph Neural Networks with Adaptive Connection Sampling, Improved Communication Cost in Distributed PageRank Computation – A Theoretical Study, Online Bayesian Moment Matching based SAT Solver Heuristics, Learning From Irregularly-Sampled Time Series: A Missing Data Perspective, Closing the convergence gap of SGD without replacement, Communication-Efficient Federated Learning with Sketching, Ready Policy One: World Building Through Active Learning, Low-Rank Bottleneck in Multi-head Attention Models, Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead, On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness, Sparse Gaussian Processes with Spherical Harmonic Features, Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning, Variational Inference for Sequential Data with Future Likelihood Estimates, DropNet: Reducing Neural Network Complexity via Iterative Pruning, Accelerating Large-Scale Inference with Anisotropic Vector Quantization, Associative Memory in Iterated Overparameterized Sigmoid Autoencoders, Streaming k-Submodular Maximization under Noise subject to Size Constraint, Rethinking Bias-Variance Trade-off for Generalization of Neural Networks, Private Query Release Assisted by Public Data, Fair Generative Modeling via Weak Supervision, Strategic Classification is Causal Modeling in Disguise, Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation, Sequence Generation with Mixed Representations, Online Learning for Active Cache Synchronization, Concise Explanations of Neural Networks using Adversarial Training, Logarithmic Regret for Online Control with Adversarial Noise, Recht-Re Noncommutative Arithmetic-Geometric Mean Conjecture is False, Regularized Optimal Transport is Ground Cost Adversarial, Explaining Groups of Points in Low-Dimensional Representations, The FAST Algorithm for Submodular Maximization, On Layer Normalization in the Transformer Architecture, Distributed Online Optimization over a Heterogeneous Network, Learning Portable Representations for High-Level Planning, Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions, Optimal Statistical Guaratees for Adversarially Robust Gaussian Classification, Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks, Model-Agnostic Characterization of Fairness Trade-offs, Attacks Which Do Not Kill Training Make Adversarial Learning Stronger, Training Binary Neural Networks using the Bayesian Learning Rule, Correlation Clustering with Asymmetric Classification Errors, More Information Supervised Probabilistic Deep Face Embedding Learning, Meta-Learning with Shared Amortized Variational Inference, NADS: Neural Architecture Distribution Search for Uncertainty Awareness, Model-Based Reinforcement Learning with Value-Targeted Regression, Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models, VFlow: More Expressive Generative Flows with Variational Data Augmentation, Predictive Sampling with Forecasting Autoregressive Models, Robust Bayesian Classification Using An Optimistic Score Ratio, Time-Consistent Self-Supervision for Semi-Supervised Learning, Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems, Q-value Path Decomposition for Deep Multiagent Reinforcement Learning, Adversarial Robustness via Runtime Masking and Cleansing, From Chaos to Order: Symmetry and Conservation Laws in Game Dynamics, Semismooth Newton Algorithm for Efficient Projections onto $\ell_{1, \infty}$-norm Ball, Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space, Boosting Deep Neural Network Efficiency with Dual-Module Inference, Up or Down? in Conjunction with ICML 2020 (FL-ICML'20) Workshop Date: July 18, 2020 ... Our workshop has no formal proceedings. The 34th International Conference on Machine Learning (ICML 2017) will be held in Sydney, Australia from August 6th to August 11th, 2017. We take inspiration from the contrastive pre-training suc- We will deepen our partnership with Black in AI at ICML, and we share its goals of increasing participation of Black researchers in the field of AI. Second, to provide constructive feedback to authors that they can use to improve their work. The proceedings of ICMI 2020 will be published by ACM as part of their series of International Conference Proceedings. PDF's -- 2019 Proceedings ... to announce the invited speakers and panelists as well as the Test of Time and Outstanding paper award recipients for ICML 2020! We have plans to enable most normal conference events virtually. The test of time talk will be live on Monday! This volume contains the papers accepted to the 25th International Conference on Machine Learning (ICML 2008). ... ICML 2003 Corporate Sponsors / xviii. Volume Edited by: Maria Florina Balcan Kilian Q. Weinberger Series Editors: Neil D. Lawrence Mark Reid 2019a; Chen et al., 2020) for language and vision respec-tively. Xingguo Li, Haoming Jiang, Jarvis Haupt, Raman Arora, Han Liu, Mingyi Hong, Tuo Zhao. Read a similar work just after this (ICML 2020, ICML 2019) - 1. 1. Search. ICML Proceedings. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. We will not accept papers that are already published though, because the goal … ICML 2020 [Proceedings] F-P. Paty A. d'Aspremont M. Cuturi: Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport: AISTATS 2020 (Notable Paper Award) [Proceedings] F-P. Paty M. Cuturi: Subspace Robust Wasserstein Distances: ICML 2019 (Top 20%) [Proceedings] [20-minute oral video] Talks & Tutorials. Set Functions for Time Series Max Horn 1 2Michael Moor Christian Bock Bastian Rieck 1 2Karsten Borgwardt Abstract Despite the eminent successes of deep neural networks, many architectures are often hard to transfer to irregularly-sampled and asynchronous Esther Rolf, Max Simchowitz, Sarah Dean, Lydia T. Liu, Daniel Björkegren, Moritz Hardt, Joshua Blumenstock. Adaptive Sketching for Fast and Convergent Canonical Polyadic Decomposition, The Sample Complexity of Best-$k$ Items Selection from Pairwise Comparisons, Hybrid Stochastic-Deterministic Minibatch Proximal Gradient: Less-Than-Single-Pass Optimization with Nearly Optimal Generalization, Universal Equivariant Multilayer Perceptrons, Automatic Reparameterisation of Probabilistic Programs, Stronger and Faster Wasserstein Adversarial Attacks, Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup, Robustness to Spurious Correlations via Human Annotations, Operation-Aware Soft Channel Pruning using Differentiable Masks, CURL: Contrastive Unsupervised Representation Learning for Reinforcement Learning, Dual Mirror Descent for Online Allocation Problems, Fully Parallel Hyperparameter Search: Reshaped Space-Filling, Striving for simplicity and performance in off-policy DRL: Output Normalization and Non-Uniform Sampling, DeBayes: a Bayesian method for debiasing network embeddings, Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data with RACE, The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation, Learning with Feature and Distribution Evolvable Streams, Extra-gradient with player sampling for faster convergence in n-player games, Information-Theoretic Local Minima Characterization and Regularization, Small Data, Big Decisions: Model Selection in the Small-Data Regime, A Sample Complexity Separation between Non-Convex and Convex Meta-Learning, Online Dense Subgraph Discovery via Blurred-Graph Feedback, Real-Time Optimisation for Online Learning in Auctions, Learning to Simulate and Design for Structural Engineering, On the consistency of top-k surrogate losses, Adversarial Attacks on Probabilistic Autoregressive Forecasting Models, T-GD: Transferable GAN-generated Images Detection Framework, Goodness-of-Fit Tests for Inhomogeneous Random Graphs, Adversarial Mutual Information for Text Generation, Active World Model Learning in Agent-rich Environments with Progress Curiosity, Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge, FedBoost: A Communication-Efficient Algorithm for Federated Learning, Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach, Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle, Representations for Stable Off-Policy Reinforcement Learning, Tensor denoising and completion based on ordinal observations, Improving Transformer Optimization Through Better Initialization, A Flexible Framework for Nonparametric Graphical Modeling that Accommodates Machine Learning, Sparsified Linear Programming for Zero-Sum Equilibrium Finding, Eliminating the Invariance on the Loss Landscape of Linear Autoencoders, On conditional versus marginal bias in multi-armed bandits, An Explicitly Relational Neural Network Architecture, A Game Theoretic Perspective on Model-Based Reinforcement Learning, Approximation Guarantees of Local Search Algorithms via Localizability of Set Functions, Consistent Estimators for Learning to Defer to an Expert, Nearly Linear Row Sampling Algorithm for Quantile Regression, Student Specialization in Deep Rectified Networks With Finite Width and Input Dimension, Provable guarantees for decision tree induction: the agnostic setting, Familywise Error Rate Control by Interactive Unmasking, Domain Aggregation Networks for Multi-Source Domain Adaptation, Generalized and Scalable Optimal Sparse Decision Trees, Data Amplification: Instance-Optimal Property Estimation, Sparse Convex Optimization via Adaptively Regularized Hard Thresholding, Online Pricing with Offline Data: Phase Transition and Inverse Square Law, Network Pruning by Greedy Subnetwork Selection, Forecasting sequential data using Consistent Koopman Autoencoders, Amortized Finite Element Analysis for Fast PDE-Constrained Optimization, Provable Self-Play Algorithms for Competitive Reinforcement Learning, Reinforcement Learning with Differential Privacy, Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM, Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion, Towards Accurate Post-training Network Quantization via Bit-Split and Stitching, Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions, A Distributional Framework For Data Valuation, Oracle Efficient Private Non-Convex Optimization, Explainable k-Means and k-Medians Clustering, On Validation and Planning of An Optimal Decision Rule with Application in Healthcare Studies, On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems, Feature-map-level Online Adversarial Knowledge Distillation, Robust One-Bit Recovery via ReLU Generative Networks: Near-Optimal Statistical Rate and Global Landscape Analysis, Rigging the Lottery: Making All Tickets Winners, Adaptive Sampling for Estimating Probability Distributions, Educating Text Autoencoders: Latent Representation Guidance via Denoising, Layered Sampling for Robust Optimization Problems, On Contrastive Learning for Likelihood-free Inference, Learning From Strategic Agents: Accuracy, Improvement, and Causality, Meta Variance Transfer: Learning to Augment from the Others, Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise, Progressive Identification of True Labels for Partial-Label Learning, Estimating Model Uncertainty of Neural Network in Sparse Information Form, Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion, Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization, Inertial Block Proximal Methods for Non-Convex Non-Smooth Optimization, Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search, Intrinsic Reward Driven Imitation Learning via Generative Model, Generalisation error in learning with random features and the hidden manifold model, Structured Policy Iteration for Linear Quadratic Regulator, Encoding Musical Style with Transformer Autoencoders, Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack, Graph Random Neural Features for Distance-Preserving Graph Representations, The Implicit Regularization of Stochastic Gradient Flow for Least Squares, Recovery of sparse signals from a mixture of linear samples, Neural Topic Modeling with Continual Lifelong Learning, Online Learned Continual Compression with Adaptive Quantization Modules, T-Basis: a Compact Representation for Neural Networks, Upper bounds for Model-Free Row-Sparse Principal Component Analysis, History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms, DeepCoDA: personalized interpretability for compositional health, Dynamics of Deep Neural Networks and Neural Tangent Hierarchy, Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization, Constant Curvature Graph Convolutional Networks, How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization, How recurrent networks implement contextual processing in sentiment analysis, Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses, Few-shot Relation Extraction via Bayesian Meta-learning on Task Graphs, Simple and Deep Graph Convolutional Networks, Two Routes to Scalable Credit Assignment without Weight Symmetry, Radioactive data: tracing through training, Learning Autoencoders with Relational Regularization, On Thompson Sampling with Langevin Algorithms, Training Deep Energy-Based Models with f-Divergence Minimization, Scalable Differentiable Physics for Learning and Control, Implicit Generative Modeling for Efficient Exploration, Optimally Solving Two-Agent Decentralized POMDPs Under One-Sided Information Sharing, TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics, Bayesian Optimisation over Multiple Continuous and Categorical Inputs, Entropy Minimization In Emergent Languages, Minimax Pareto Fairness: A Multi Objective Perspective, IPBoost – Non-Convex Boosting via Integer Programming, Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules, Goal-Aware Prediction: Learning to Model What Matters, DINO: Distributed Newton-Type Optimization Method, Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization, Piecewise Linear Regression via a Difference of Convex Functions, Inductive Bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters, Distance Metric Learning with Joint Representation Diversification, A simpler approach to accelerated optimization: iterative averaging meets optimism, Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization, On the Expressivity of Neural Networks for Deep Reinforcement Learning, PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination, Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay, Linear Mode Connectivity and the Lottery Ticket Hypothesis, Gradient Temporal-Difference Learning with Regularized Corrections, Task-Oriented Active Perception and Planning in Environments with Partially Known Semantics, Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees, Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking, On hyperparameter tuning in general clustering problemsm, Scalable Deep Generative Modeling for Sparse Graphs, Data Valuation using Reinforcement Learning, Representation Learning via Adversarially-Contrastive Optimal Transport, Class-Weighted Classification: Trade-offs and Robust Approaches, Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings, NGBoost: Natural Gradient Boosting for Probabilistic Prediction, Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation, CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods, Do We Really Need to Access the Source Data? 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No formal Proceedings as Supplementary Material via CMT Tsochantaridis, and Thomas Hofmann on tensor networks in 2020!
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