Machine Learning Department at CMU | ICML 2019

Carnegie Mellon University, Accepted Papers at ICML 2019

CMU accepted papers to the International Conference on Machine Learning (ICML) 2019

Full List of Accepted Papers

Statistical Foundations of Virtual Democracy

TarMAC: Targeted Multi-Agent Communication

A Kernel Theory of Modern Data Augmentation

Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments

Nearest neighbor and kernel survival analysis: Nonasymptotic error bounds and strong consistency rates

Policy Certificates: Towards Accountable Reinforcement Learning

Deep Counterfactual Regret Minimization

Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment

Provably efficient RL with Rich Observations via Latent State Decoding

A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs

Gradient Descent Finds Global Minima of Deep Neural Networks

Stable-Predictive Optimistic Counterfactual Regret Minimization

Regret Circuits: Composability of Regret Minimizers

Provable Guarantees for Gradient-Based Meta-Learning

Dimensionality Reduction for Tukey Regression

Certified Adversarial Robustness via Randomized Smoothing

Provably Efficient Imitation Learning from Observation Alone

SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver

Collective Model Fusion for Multiple Black-Box Experts

Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks

Width Provably Matters in Optimization for Deep Linear Neural Networks

Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models

Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension

Faster Algorithms for Boolean Matrix Factorization

Contextual Memory Trees

Fault Tolerance in Iterative-Convergent Machine Learning

Wasserstein Adversarial Examples via Projected Sinkhorn Iterations

Learning to Explore via Disagreement

What is the Effect of Importance Weighting in Deep Learning?

Adversarial camera stickers: A physical camera-based attack on deep learning systems

On Learning Invariant Representation for Domain Adaptation

Finding Options that Minimize Planning Time

Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel kk-means Clustering

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