ICML-20 待读的 Paper

2020.9.12
花了一上午的时间，过了一遍 ICML-2020 Accepted Paper List, 挑出了自己想读的 Paper。
主要关注于自己的一些研究点。

Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
Error-Bounded Correction of Noisy Labels
Does label smoothing mitigate label noise?
Deep k-NN for Noisy Labels
Improving generalization by controlling label-noise information in neural network weights
Normalized Loss Functions for Deep Learning with Noisy Labels
Variational Label Enhancement
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates
Searching to Exploit Memorization Effect in Learning with Noisy Labels
Label-Noise Robust Domain Adaptation
Training Binary Neural Networks through Learning with Noisy Supervision
Learning with Bounded Instance- and Label-dependent Label Noise
Progressive Identification of True Labels for Partial-Label Learning
Learning with Multiple Complementary Labels
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels

Continuously Indexed Domain Adaptation
RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
Understanding Self-Training for Gradual Domain Adaptation
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
LTF: A Label Transformation Framework for Correcting Label Shift
Label-Noise Robust Domain Adaptation

Continuous Graph Neural Networks
Simple and Deep Graph Convolutional Networks
Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters
Generalization and Representational Limits of Graph Neural Networks
Graph-based Nearest Neighbor Search: From Practice to Theory
Ordinal Non-negative Matrix Factorization for Recommendation
Improved Communication Cost in Distributed PageRank Computation – A Theoretical Study
Optimization and Analysis of the pAp@k Metric for Recommender Systems
Scalable and Efficient Comparison-based Search without Features
Learning to Rank Learning Curves
When Does Self-Supervision Help Graph Convolutional Networks?

Towards Adaptive Residual Network Training: A Neural-ODE Perspective
Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction
Approximation Capabilities of Neural ODEs and Invertible Residual Networks

Confidence-Aware Learning for Deep Neural Networks
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
Detecting Out-of-Distribution Examples with Gram Matrices
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure
Uncertainty Estimation Using a Single Deep Deterministic Neural Network
How Good is the Bayes Posterior in Deep Neural Networks Really?

Why Are Learned Indexes So Effective?
Learning with Feature and Distribution Evolvable Streams
Cost-effectively Identifying Causal Effects When Only Response Variable is Observable
Rigging the Lottery: Making All Tickets Win
Do We Need Zero Training Loss After Achieving Zero Training Error?
Small Data, Big Decisions: Model Selection in the Small-Data Regime
Why bigger is not always better: on finite and infinite neural networks
On Learning Sets of Symmetric Elements
Collaborative Machine Learning with Incentive-Aware Model Rewards
Generalisation error in learning with random features and the hidden manifold model
Sample Amplification: Increasing Dataset Size even when Learning is Impossible
When are Non-Parametric Methods Robust?
Performative Prediction
Supervised learning: no loss no cry
Teaching with Limited Information on the Learner's Behaviour
Learning De-biased Representations with Biased Representations
Do RNN and LSTM have Long Memory?
It's Not What Machines Can Learn, It's What We Cannot Teach
Enhancing Simple Models by Exploiting What They Already Know

On the Generalization Benefit of Noise in Stochastic Gradient Descent
Let's Agree to Agree: Neural Networks Share Classification Order on Real Datasets
Optimizer Benchmarking Needs to Account for Hyperparameter Tuning
On the Noisy Gradient Descent that Generalizes as SGD
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks
Understanding and Mitigating the Tradeoff between Robustness and Accuracy
The Implicit and Explicit Regularization Effects of Dropout
Optimal Continual Learning has Perfect Memory and is NP-hard
Curvature-corrected learning dynamics in deep neural networks
Explainable k-Means and k-Medians Clustering
Near-Tight Margin-Based Generalization Bounds for Support Vector Machines
Uniform Convergence of Rank-weighted Learning
Decision Trees for Decision-Making under the Predict-then-Optimize Framework