Papers
Interpolation between Residual and Non-Residual Networks
Zonghan Yang, Yang Liu, Chenglong Bao et al.
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
Omer Gottesman, Joseph Futoma, Yao Liu et al.
Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge
Laura Rieger, Chandan Singh, William Murdoch et al.
Interpreting Robust Optimization via Adversarial Influence Functions
Zhun Deng, Cynthia Dwork, Jialiang Wang et al.
Intrinsic Reward Driven Imitation Learning via Generative Model
Xingrui Yu, Yueming Lyu, Ivor Tsang
Invariant Causal Prediction for Block MDPs
Amy Zhang, Clare Lyle, Shagun Sodhani et al.
Invariant Rationalization
Shiyu Chang, Yang Zhang, Mo Yu et al.
Invariant Risk Minimization Games
Kartik Ahuja, Karthikeyan Shanmugam, Kush Varshney et al.
Inverse Active Sensing: Modeling and Understanding Timely Decision-Making
Daniel Jarrett, Mihaela Van Der Schaar
Invertible generative models for inverse problems: mitigating representation error and dataset bias
Muhammad Asim, Mara Daniels, Oscar Leong et al.
Involutive MCMC: a Unifying Framework
Kirill Neklyudov, Max Welling, Evgenii Egorov et al.
IPBoost – Non-Convex Boosting via Integer Programming
Marc Pfetsch, Sebastian Pokutta
Is Local SGD Better than Minibatch SGD?
Blake Woodworth, Kumar Kshitij Patel, Sebastian Stich et al.
Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing
Sanghamitra Dutta, Dennis Wei, Hazar Yueksel et al.
It’s Not What Machines Can Learn, It’s What We Cannot Teach
Gal Yehuda, Moshe Gabel, Assaf Schuster
Kernel interpolation with continuous volume sampling
Ayoub Belhadji, Rémi Bardenet, Pierre Chainais
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data
Tamara Fernandez, Nicolas Rivera, Wenkai Xu et al.
Kernel Methods for Cooperative Multi-Agent Contextual Bandits
Abhimanyu Dubey, Alex ‘Sandy’ Pentland
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy et al.
k-means++: few more steps yield constant approximation
Davin Choo, Christoph Grunau, Julian Portmann et al.
Knowing The What But Not The Where in Bayesian Optimization
Vu Nguyen, Michael A. Osborne
Label-Noise Robust Domain Adaptation
Xiyu Yu, Tongliang Liu, Mingming Gong et al.
Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks
Alexander Shevchenko, Marco Mondelli