Learning Theory

Competing AI: How does competition feedback affect machine learning? AISTATS 2021 Computational Analyses of the Electoral College: Campaigning Is Hard But Approximately Manageable AAAI 2021 Conditional probing: measuring usable information beyond a baseline EMNLP 2021 Continual Learning in the Teacher-Student Setup: Impact of Task Similarity ICML 2021 Continual Learning via Bit-Level Information Preserving CVPR 2021 Continuous vs. Discrete Optimization of Deep Neural Networks NeurIPS 2021 Continuum-Armed Bandits: A Function Space Perspective AISTATS 2021 Contrastive Neural Architecture Search With Neural Architecture Comparators CVPR 2021 Convergence rates and approximation results for SGD and its continuous-time counterpart COLT 2021 Correlated Stochastic Block Models: Exact Graph Matching with Applications to Recovering Communities NeurIPS 2021 Corruption-robust exploration in episodic reinforcement learning COLT 2021 Counterfactual Maximum Likelihood Estimation for Training Deep Networks NeurIPS 2021 Credit Assignment Through Broadcasting a Global Error Vector NeurIPS 2021 Data and Parameter Scaling Laws for Neural Machine Translation EMNLP 2021 Data driven semi-supervised learning NeurIPS 2021 Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification NeurIPS 2021 Decentralized Learning in Online Queuing Systems NeurIPS 2021 Deciding What to Learn: A Rate-Distortion Approach ICML 2021 Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach ICLR 2021 Decomposition-Guided Reductions for Argumentation and Treewidth IJCAI 2021 Deep Equals Shallow for ReLU Networks in Kernel Regimes ICLR 2021 Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space NeurIPS 2021 Deep Learning Through the Lens of Example Difficulty NeurIPS 2021 Deep Networks Provably Classify Data on Curves NeurIPS 2021 Deep Neural Tangent Kernel and Laplace Kernel Have the Same RKHS ICLR 2021