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Learning Theory

5,312 papers

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Papers

Convergence of Gaussian Belief Propagation Under General Pairwise Factorization: Connecting Gaussian MRF with Pairwise Linear Gaussian Model JMLR 2019
Characterizing the Sample Complexity of Pure Private Learners JMLR 2019
On Asymptotic and Finite-Time Optimality of Bayesian Predictors JMLR 2019
Sparse Kernel Regression with Coefficient-based $\ell_q-$regularization JMLR 2019
Stochastic Canonical Correlation Analysis JMLR 2019
Does My Rebuttal Matter? Insights from a Major NLP Conference NAACL 2019
Inoculation by Fine-Tuning: A Method for Analyzing Challenge Datasets NAACL 2019
Studying the Inductive Biases of RNNs with Synthetic Variations of Natural Languages NAACL 2019
Benchmarking Hierarchical Script Knowledge NAACL 2019
Testing a Minimalist Grammar Parser on Italian Relative Clause Asymmetries NAACL 2019
Variational Regret Bounds for Reinforcement Learning UAI 2019
Be Greedy: How Chromatic Number meets Regret Minimization in Graph Bandits UAI 2019
Problem-dependent Regret Bounds for Online Learning with Feedback Graphs UAI 2019
On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits UAI 2019
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms NIPS 2018
Identification and Estimation of Causal Effects from Dependent Data NIPS 2018
How Many Samples are Needed to Estimate a Convolutional Neural Network? NIPS 2018
Critical initialisation for deep signal propagation in noisy rectifier neural networks NIPS 2018
Generalization Bounds for Uniformly Stable Algorithms NIPS 2018
Escaping Saddle Points in Constrained Optimization NIPS 2018
Generalisation in humans and deep neural networks NIPS 2018
Efficient online algorithms for fast-rate regret bounds under sparsity NIPS 2018
Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator NIPS 2018
Implicit Bias of Gradient Descent on Linear Convolutional Networks NIPS 2018
Data-Driven Clustering via Parameterized Lloyd's Families NIPS 2018