Papers
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space
Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu et al.
Convolutional dictionary learning based auto-encoders for natural exponential-family distributions
Bahareh Tolooshams, Andrew Song, Simona Temereanca et al.
Convolutional Kernel Networks for Graph-Structured Data
Dexiong Chen, Laurent Jacob, Julien Mairal
Cooperative Multi-Agent Bandits with Heavy Tails
Abhimanyu Dubey, Alex ‘Sandy’ Pentland
Coresets for Clustering in Graphs of Bounded Treewidth
Daniel Baker, Vladimir Braverman, Lingxiao Huang et al.
Coresets for Data-efficient Training of Machine Learning Models
Baharan Mirzasoleiman, Jeff Bilmes, Jure Leskovec
Correlation Clustering with Asymmetric Classification Errors
Jafar Jafarov, Sanchit Kalhan, Konstantin Makarychev et al.
Cost-Effective Interactive Attention Learning with Neural Attention Processes
Jay Heo, Junhyeon Park, Hyewon Jeong et al.
Cost-effectively Identifying Causal Effects When Only Response Variable is Observable
Tian-Zuo Wang, Xi-Zhu Wu, Sheng-Jun Huang et al.
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models
Yuta Saito, Shota Yasui
Countering Language Drift with Seeded Iterated Learning
Yuchen Lu, Soumye Singhal, Florian Strub et al.
CURL: Contrastive Unsupervised Representations for Reinforcement Learning
Michael Laskin, Aravind Srinivas, Pieter Abbeel
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness
Aounon Kumar, Alexander Levine, Tom Goldstein et al.
Customizing ML Predictions for Online Algorithms
Keerti Anand, Rong Ge, Debmalya Panigrahi
Data Amplification: Instance-Optimal Property Estimation
Yi Hao, Alon Orlitsky
Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models
Amrita Roy Chowdhury, Theodoros Rekatsinas, Somesh Jha
Data preprocessing to mitigate bias: A maximum entropy based approach
L. Elisa Celis, Vijay Keswani, Nisheeth Vishnoi
Data Valuation using Reinforcement Learning
Jinsung Yoon, Sercan Arik, Tomas Pfister
DeBayes: a Bayesian Method for Debiasing Network Embeddings
Maarten Buyl, Tijl De Bie
Debiased Sinkhorn barycenters
Hicham Janati, Marco Cuturi, Alexandre Gramfort
Decentralised Learning with Random Features and Distributed Gradient Descent
Dominic Richards, Patrick Rebeschini, Lorenzo Rosasco
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions
Michael Chang, Sid Kaushik, S. Matthew Weinberg et al.
Decision Trees for Decision-Making under the Predict-then-Optimize Framework
Adam N. Elmachtoub, Jason Cheuk Nam Liang, Ryan Mcnellis