Papers
On Monotonic Linear Interpolation of Neural Network Parameters
James R Lucas, Juhan Bae, Michael R Zhang et al.
On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification
Zahra Babaiee, Ramin Hasani, Mathias Lechner et al.
On Perceptual Lossy Compression: The Cost of Perceptual Reconstruction and An Optimal Training Framework
Zeyu Yan, Fei Wen, Rendong Ying et al.
On-Policy Deep Reinforcement Learning for the Average-Reward Criterion
Yiming Zhang, Keith W Ross
On Proximal Policy Optimization’s Heavy-tailed Gradients
Saurabh Garg, Joshua Zhanson, Emilio Parisotto et al.
On Recovering from Modeling Errors Using Testing Bayesian Networks
Haiying Huang, Adnan Darwiche
On Reinforcement Learning with Adversarial Corruption and Its Application to Block MDP
Tianhao Wu, Yunchang Yang, Simon Du et al.
On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game
Shuang Qiu, Jieping Ye, Zhaoran Wang et al.
On Robust Mean Estimation under Coordinate-level Corruption
Zifan Liu, Jong Ho Park, Theodoros Rekatsinas et al.
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes
Tim G. J. Rudner, Oscar Key, Yarin Gal et al.
On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients
Difan Zou, Quanquan Gu
On the difficulty of unbiased alpha divergence minimization
Tomas Geffner, Justin Domke
On the Explicit Role of Initialization on the Convergence and Implicit Bias of Overparametrized Linear Networks
Hancheng Min, Salma Tarmoun, Rene Vidal et al.
On-the-fly Rectification for Robust Large-Vocabulary Topic Inference
Moontae Lee, Sungjun Cho, Kun Dong et al.
On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models
Peizhong Ju, Xiaojun Lin, Ness Shroff
On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent
Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson et al.
On the Inherent Regularization Effects of Noise Injection During Training
Oussama Dhifallah, Yue Lu
On the Optimality of Batch Policy Optimization Algorithms
Chenjun Xiao, Yifan Wu, Jincheng Mei et al.
On the Predictability of Pruning Across Scales
Jonathan S Rosenfeld, Jonathan Frankle, Michael Carbin et al.
On the price of explainability for some clustering problems
Eduardo S Laber, Lucas Murtinho
On the Problem of Underranking in Group-Fair Ranking
Sruthi Gorantla, Amit Deshpande, Anand Louis
On the Random Conjugate Kernel and Neural Tangent Kernel
Zhengmian Hu, Heng Huang
On Variational Inference in Biclustering Models
Guanhua Fang, Ping Li