Papers
Improving Barely Supervised Learning by Discriminating Unlabeled Samples with Super-Class
Guan Gui, Zhen Zhao, Lei Qi et al.
Improving Certified Robustness via Statistical Learning with Logical Reasoning
Zhuolin Yang, Zhikuan Zhao, Boxin Wang et al.
Improving Diffusion Models for Inverse Problems using Manifold Constraints
Hyungjin Chung, Byeongsu Sim, Dohoon Ryu et al.
Improving GANs with A Dynamic Discriminator
Ceyuan Yang, Yujun Shen, Yinghao Xu et al.
Improving Generative Adversarial Networks via Adversarial Learning in Latent Space
Yang Li, Yichuan Mo, Liangliang Shi et al.
Improving Intrinsic Exploration with Language Abstractions
Jesse Mu, Victor Zhong, Roberta Raileanu et al.
Improving Multi-Task Generalization via Regularizing Spurious Correlation
Ziniu Hu, Zhe Zhao, Xinyang Yi et al.
Improving Neural Ordinary Differential Equations with Nesterov's Accelerated Gradient Method
Ho Huu Nghia Nguyen, Tan Nguyen, Huyen Vo et al.
Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors
Qixun Wang, Yifei Wang, Hong Zhu et al.
Improving Policy Learning via Language Dynamics Distillation
Victor Zhong, Jesse Mu, Luke Zettlemoyer et al.
Improving Self-Supervised Learning by Characterizing Idealized Representations
Yann Dubois, Stefano Ermon, Tatsunori B Hashimoto et al.
Improving Task-Specific Generalization in Few-Shot Learning via Adaptive Vicinal Risk Minimization
Long-Kai Huang, Ying Wei
Improving Transformer with an Admixture of Attention Heads
Tan Nguyen, Tam Nguyen, Hai Do et al.
Improving Variational Autoencoders with Density Gap-based Regularization
Jianfei Zhang, Jun Bai, Chenghua Lin et al.
Improving Zero-Shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions
Bogdan Mazoure, Ilya Kostrikov, Ofir Nachum et al.
Incentivizing Combinatorial Bandit Exploration
Xinyan Hu, Dung Ngo, Aleksandrs Slivkins et al.
Inception Transformer
Chenyang Si, Weihao Yu, Pan Zhou et al.
Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering
An Zhang, Wenchang Ma, Xiang Wang et al.
Increasing Confidence in Adversarial Robustness Evaluations
Roland S. Zimmermann, Wieland Brendel, Florian Tramer et al.
Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces
Leonard Papenmeier, Luigi Nardi, Matthias Poloczek
Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards
Ashwinkumar Badanidiyuru Varadaraja, Zhe Feng, Tianxi Li et al.
In Defense of the Unitary Scalarization for Deep Multi-Task Learning
Vitaly Kurin, Alessandro De Palma, Ilya Kostrikov et al.
Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models
Haoyue Dai, Peter Spirtes, Kun Zhang
Independence Testing for Bounded Degree Bayesian Networks
Arnab Bhattacharyya, Clément L Canonne, Qiping Yang
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples
Maura Pintor, Luca Demetrio, Angelo Sotgiu et al.