Papers
Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning
Yunhao Tang, Alp Kucukelbir
Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes
Nhuong Nguyen, Toan Nguyen, PHUONG HA NGUYEN et al.
Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent
Frederik Kunstner, Raunak Kumar, Mark Schmidt
Hyperbolic graph embedding with enhanced semi-implicit variational inference.
Ali Lotfi Rezaabad, Rahi Kalantari, Sriram Vishwanath et al.
Hyperparameter Transfer Learning with Adaptive Complexity
Samuel Horváth, Aaron Klein, Peter Richtarik et al.
Identification of Matrix Joint Block Diagonalization
Yunfeng Cai, Ping Li
Implicit Regularization via Neural Feature Alignment
Aristide Baratin, Thomas George, César Laurent et al.
Improved Complexity Bounds in Wasserstein Barycenter Problem
Darina Dvinskikh, Daniil Tiapkin
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data
Zhun Deng, Linjun Zhang, Amirata Ghorbani et al.
Improving Classifier Confidence using Lossy Label-Invariant Transformations
Sooyong Jang, Insup Lee, James Weimer
Improving KernelSHAP: Practical Shapley Value Estimation Using Linear Regression
Ian Covert, Su-In Lee
Improving predictions of Bayesian neural nets via local linearization
Alexander Immer, Maciej Korzepa, Matthias Bauer
Independent Innovation Analysis for Nonlinear Vector Autoregressive Process
Hiroshi Morioka, Hermanni Hälvä, Aapo Hyvarinen
Inductive Mutual Information Estimation: A Convex Maximum-Entropy Copula Approach
Yves-Laurent Kom Samo
Inference in Stochastic Epidemic Models via Multinomial Approximations
Nick Whiteley, Lorenzo Rimella
Influence Decompositions For Neural Network Attribution
Kyle Reing, Greg Ver Steeg, Aram Galstyan
Instance-Wise Minimax-Optimal Algorithms for Logistic Bandits
Marc Abeille, Louis Faury, Clement Calauzenes
Interpretable Random Forests via Rule Extraction
Clément Bénard, Gérard Biau, Sébastien da Veiga et al.
Iterative regularization for convex regularizers
Cesare Molinari, Mathurin Massias, Lorenzo Rosasco et al.
Kernel Distributionally Robust Optimization: Generalized Duality Theorem and Stochastic Approximation
Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl et al.
Kernel Interpolation for Scalable Online Gaussian Processes
Samuel Stanton, Wesley Maddox, Ian Delbridge et al.
Kernel regression in high dimensions: Refined analysis beyond double descent
Fanghui Liu, Zhenyu Liao, Johan Suykens
Large Scale K-Median Clustering for Stable Clustering Instances
Konstantin Voevodski
LassoNet: Neural Networks with Feature Sparsity
Ismael Lemhadri, Feng Ruan, Rob Tibshirani
Last iterate convergence in no-regret learning: constrained min-max optimization for convex-concave landscapes
Qi Lei, Sai Ganesh Nagarajan, Ioannis Panageas et al.