Papers
Implicit Generative Modeling for Efficient Exploration
Neale Ratzlaff, Qinxun Bai, Li Fuxin et al.
Implicit Geometric Regularization for Learning Shapes
Amos Gropp, Lior Yariv, Niv Haim et al.
Implicit Learning Dynamics in Stackelberg Games: Equilibria Characterization, Convergence Analysis, and Empirical Study
Tanner Fiez, Benjamin Chasnov, Lillian Ratliff
Implicit Regularization of Random Feature Models
Arthur Jacot, Berfin Simsek, Francesco Spadaro et al.
Improved Optimistic Algorithms for Logistic Bandits
Louis Faury, Marc Abeille, Clement Calauzenes et al.
Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards
Aadirupa Saha, Pierre Gaillard, Michal Valko
Improving generalization by controlling label-noise information in neural network weights
Hrayr Harutyunyan, Kyle Reing, Greg Ver Steeg et al.
Improving Generative Imagination in Object-Centric World Models
Zhixuan Lin, Yi-Fu Wu, Skand Peri et al.
Improving Molecular Design by Stochastic Iterative Target Augmentation
Kevin Yang, Wengong Jin, Kyle Swanson et al.
Improving Robustness of Deep-Learning-Based Image Reconstruction
Ankit Raj, Yoram Bresler, Bo Li
Improving the Gating Mechanism of Recurrent Neural Networks
Albert Gu, Caglar Gulcehre, Thomas Paine et al.
Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking
Haoran Sun, Songtao Lu, Mingyi Hong
Improving Transformer Optimization Through Better Initialization
Xiao Shi Huang, Felipe Perez, Jimmy Ba et al.
Imputer: Sequence Modelling via Imputation and Dynamic Programming
William Chan, Chitwan Saharia, Geoffrey Hinton et al.
Incremental Sampling Without Replacement for Sequence Models
Kensen Shi, David Bieber, Charles Sutton
In Defense of Uniform Convergence: Generalization via Derandomization with an Application to Interpolating Predictors
Jeffrey Negrea, Gintare Karolina Dziugaite, Daniel Roy
Individual Calibration with Randomized Forecasting
Shengjia Zhao, Tengyu Ma, Stefano Ermon
Individual Fairness for k-Clustering
Sepideh Mahabadi, Ali Vakilian
Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks
Mark Kurtz, Justin Kopinsky, Rati Gelashvili et al.
Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters
Subho Banerjee, Saurabh Jha, Zbigniew Kalbarczyk et al.
Inductive Relation Prediction by Subgraph Reasoning
Komal Teru, Etienne Denis, Will Hamilton
Inertial Block Proximal Methods for Non-Convex Non-Smooth Optimization
Hien Le, Nicolas Gillis, Panagiotis Patrinos
Inexact Tensor Methods with Dynamic Accuracies
Nikita Doikov, Yurii Nesterov
Inferring DQN structure for high-dimensional continuous control
Andrey Sakryukin, Chedy Raissi, Mohan Kankanhalli