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。
Noisy Labels
- 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
Semi-Supervised Learning
- Semi-Supervised Learning with Normalizing Flows
- Negative Sampling in Semi-Supervised learning
- Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates
- Time-Consistent Self-Supervision for Semi-Supervised Learning
- Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data
- Deep Streaming Label Learning
Domain Adaptation
- 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
Data Bias, Weighting
- Adversarial Filters of Dataset Biases
- Optimizing Data Usage via Differentiable Rewards
- Data preprocessing to mitigate bias: A maximum entropy based approach
- DeBayes: a Bayesian Method for Debiasing Network Embeddings
- A Distributional Framework For Data Valuation
Class-Imbalance
- Class-Weighted Classification: Trade-offs and Robust Approaches
- Online Continual Learning from Imbalanced Data
- Logistic Regression for Massive Data with Rare Events
MixUp, Interpolation, Extrapolation, etc.
- Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
- Learning Representations that Support Extrapolation
- Extrapolation for Large-batch Training in Deep Learning
- Training Neural Networks for and by Interpolation
- Sequence Generation with Mixed Representations
PU Learning
- Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training
Active Learning
- Adaptive Region-Based Active Learning
GCN or Recommendation System
- 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?
Neural ODE
- 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
Calibration, Confidence, Out-of-distribution
- 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?
Federated Learning, Fairness
- Fair k-Centers via Maximum Matching
- Federated Learning with Only Positive Labels
Interesting Problems, Settings
- 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
Interesting Theory
- 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
Interesting Algorithm
- SoftSort: A Continuous Relaxation for the argsort Operator
- Boosting Deep Neural Network Efficiency with Dual-Module Inference
- Circuit-Based Intrinsic Methods to Detect Overfitting
- Learning Similarity Metrics for Numerical Simulations
- Deep Divergence Learning
- Consistent Estimators for Learning to Defer to an Expert
- Smaller, more accurate regression forests using tree alternating optimization
- Learning To Stop While Learning To Predict
- DROCC: Deep Robust One-Class Classification
Point Cloud, 3 dimension
- Hypernetwork approach to generating point clouds