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Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation

NeurIPS, 2021
Jamie Cui*, Chaochao Chen*, Lingjuan Lyu, Carl Yang*, Li Wang*

Social recommendation has shown promising improvements over traditional systems since it leverages social correlation data as an additional input. Most existing works assume that all data are available to the recommendation platform. However, in practice, user-item interacti…

Anti-Backdoor Learning: Training Clean Models on Poisoned Data

NeurIPS, 2021
Yige Li*, Xixiang Lyu*, Nodens Koren*, Lingjuan Lyu, Bo Li*, Xingjun Ma*

Backdoor attack has emerged as a major security threat to deep neural networks(DNNs). While existing defense methods have demonstrated promising results on detecting and erasing backdoor triggers, it is still not clear if measures can be taken to avoid the triggers from bein…

Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning

NeurIPS, 2021
Xu Xinyi*, Lingjuan Lyu, Xingjun Ma*, Chenglin Miao*, Chuan-Sheng Foo*, Bryan Kian Hsiang Low*

Collaborative machine learning provides a promising framework for different agents to pool their resources (e.g., data) for a common learning task. In realistic settings where agents are self-interested and not altruistic, they may be unwilling to share data or model without…

Expert Human-Level Driving in Gran Turismo Sport Using Deep Reinforcement Learning with Image-based Representation

NeurIPS, 2021
Ryuji Imamura, Takuma Seno, Kenta Kawamoto, Michael Spranger

When humans play virtual racing games, they use visual environmental information on the game screen to understand the rules within the environments. In contrast, a state-of-the-art realistic racing game AI agent that outperforms human players does not use image-based environ…

d3rlpy: An Offline Deep Reinforcement Learning Library

NeurIPS, 2021
Takuma Seno, Michita Imai*

In this paper, we introduce d3rlpy, an open-sourced offline deep reinforcement learning (RL) library for Python. d3rlpy supports a number of offline deep RL algorithms as well as online algorithms via a user-friendly API. To assist deep RL research and development projects, …

Assessing SATNet's Ability to Solve the Symbol Grounding Problem

NeurIPS, 2020
Michael Spranger, Oscar Chang*, Lampros Flokas*, Hod Lipson*

SATNet is an award-winning MAXSAT solver that can be used to infer logical rules and integrated as a differentiable layer in a deep neural network. It had been shown to solve Sudoku puzzles visually from examples of puzzle digit images, and was heralded as an impressive achi…

Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation

NeurIPS, 2020
Uchenna Akujuobi, Jun Chen*, Mohamed Elhoseiny*, Michael Spranger, Xiangliang Zhang*

Understanding the relationships between biomedical terms like viruses, drugs, and symptoms is essential in the fight against diseases. Many attempts have been made to introduce the use of machine learning to the scientific process of hypothesis generation (HG), which refers …

Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks

NeurIPS, 2020
Lemeng Wu*, Bo Liu*, Peter Stone, Qiang Liu*

We propose firefly neural architecture descent, a general framework for progressively and dynamically growing neural networks to jointly optimize the networks' parameters and architectures. Our method works in a steepest descent fashion, which iteratively finds the best netw…

An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch

NeurIPS, 2020
Siddharth Desai*, Ishan Durugkar*, Haresh Karnan*, Garrett Warnell*, Josiah Hanna*, Peter Stone

We examine the problem of transferring a policy learned in a source environment to a target environment with different dynamics, particularly in the case where it is critical to reduce the amount of interaction with the target environment during learning. This problem is par…

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