Papers
On the convergence of policy gradient methods to Nash equilibria in general stochastic games
Angeliki Giannou, Kyriakos Lotidis, Panayotis Mertikopoulos et al.
On the Convergence of Stochastic Multi-Objective Gradient Manipulation and Beyond
Shiji Zhou, Wenpeng Zhang, Jiyan Jiang et al.
On the Convergence Theory for Hessian-Free Bilevel Algorithms
Daouda Sow, Kaiyi Ji, Yingbin Liang
On the detrimental effect of invariances in the likelihood for variational inference
Richard Kurle, Ralf Herbrich, Tim Januschowski et al.
On the difficulty of learning chaotic dynamics with RNNs
Jonas Mikhaeil, Zahra Monfared, Daniel Durstewitz
On the Discrimination Risk of Mean Aggregation Feature Imputation in Graphs
Arjun Subramonian, Kai-Wei Chang, Yizhou Sun
On the Double Descent of Random Features Models Trained with SGD
Fanghui Liu, Johan Suykens, Volkan Cevher
On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning
Mandi Zhao, Pieter Abbeel, Stephen James
On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning
Lorenzo Bonicelli, Matteo Boschini, Angelo Porrello et al.
On the Effectiveness of Persistent Homology
Renata Turkes, Guido F. Montufar, Nina Otter
On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias
Itay Safran, Gal Vardi, Jason Lee
On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood
Moses Charikar, Zhihao Jiang, Kirankumar Shiragur et al.
On the Epistemic Limits of Personalized Prediction
Lucas Monteiro Paes, Carol Long, Berk Ustun et al.
On the Frequency-bias of Coordinate-MLPs
Sameera Ramasinghe, Lachlan E. MacDonald, Simon Lucey
On the Generalizability and Predictability of Recommender Systems
Duncan McElfresh, Sujay Khandagale, Jonathan Valverde et al.
On the generalization of learning algorithms that do not converge
Nisha Chandramoorthy, Andreas Loukas, Khashayar Gatmiry et al.
On the Generalization Power of the Overfitted Three-Layer Neural Tangent Kernel Model
Peizhong Ju, Xiaojun Lin, Ness Shroff
On the Global Convergence Rates of Decentralized Softmax Gradient Play in Markov Potential Games
Runyu Zhang, Jincheng Mei, Bo Dai et al.
On the Identifiability of Nonlinear ICA: Sparsity and Beyond
Yujia Zheng, Ignavier Ng, Kun Zhang
On the Importance of Gradient Norm in PAC-Bayesian Bounds
Itai Gat, Yossi Adi, Alex Schwing et al.
On the inability of Gaussian process regression to optimally learn compositional functions
Matteo Giordano, Kolyan Ray, Johannes Schmidt-Hieber
On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity
Vincent Szolnoky, Viktor Andersson, Balazs Kulcsar et al.
On the Learning Mechanisms in Physical Reasoning
Shiqian Li, Kewen Wu, Chi Zhang et al.
On the Limitations of Stochastic Pre-processing Defenses
Yue Gao, I Shumailov, Kassem Fawaz et al.