26 citations · 64 across the 16 of their papers we have counts for
16 papers
UserBERT: Modeling Long- and Short-Term User Preferences via Self-Supervision
Tianyu Li, Ali Cevahir, Derek Cho +3
E-commerce platforms generate vast amounts of customer behavior data, such as clicks and purchases, from millions of unique users every day. However, effectively using this data fo…
Learning Navigation Skills for Legged Robots with Learned Robot Embeddings
Joanne Truong, Denis Yarats, Tianyu Li +4
Recent work has shown results on learning navigation policies for idealized cylinder agents in simulation and transferring them to real wheeled robots. Deploying such navigation po…
Neural Representations in Hybrid Recommender Systems: Prediction versus Regularization
Ramin Raziperchikolaei, Tianyu Li, Young-joo Chung
Autoencoder-based hybrid recommender systems have become popular recently because of their ability to learn user and item representations by reconstructing various information sour…
Connecting Weighted Automata, Tensor Networks and Recurrent Neural Networks through Spectral Learning
Tianyu Li, Doina Precup, Guillaume Rabusseau
In this paper, we present connections between three models used in different research fields: weighted finite automata~(WFA) from formal languages and linguistics, recurrent neural…
Learning to Profile: User Meta-Profile Network for Few-Shot Learning
Hao Gong, Qifang Zhao, Tianyu Li +2
Meta-learning approaches have shown great success in vision and language domains. However, few studies discuss the practice of meta-learning for large-scale industrial applications…
Planning in Learned Latent Action Spaces for Generalizable Legged Locomotion
Tianyu Li, Roberto Calandra, Deepak Pathak +3
Hierarchical learning has been successful at learning generalizable locomotion skills on walking robots in a sample-efficient manner. However, the low-dimensional "latent" action u…
Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces
Bogdan Mazoure, Thang Doan, Tianyu Li +4
We propose a general framework for policy representation for reinforcement learning tasks. This framework involves finding a low-dimensional embedding of the policy on a reproducin…
Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning
Tianyu Li, Bogdan Mazoure, Doina Precup +1
Learning and planning in partially-observable domains is one of the most difficult problems in reinforcement learning. Traditional methods consider these two problems as independen…
Learning Classifiers on Positive and Unlabeled Data with Policy Gradient
Tianyu Li, Chien-Chih Wang, Yukun Ma +4
Existing algorithms aiming to learn a binary classifier from positive (P) and unlabeled (U) data generally require estimating the class prior or label noises ahead of building a cl…
Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learning
Tianyu Li, Nathan Lambert, Roberto Calandra +2
Learning to locomote to arbitrary goals on hardware remains a challenging problem for reinforcement learning. In this paper, we present a hierarchical learning framework that impro…
Bayesian Optimization in Variational Latent Spaces with Dynamic Compression
Rika Antonova, Akshara Rai, Tianyu Li +1
Data-efficiency is crucial for autonomous robots to adapt to new tasks and environments. In this work we focus on robotics problems with a budget of only 10-20 trials. This is a ve…
GRP Model for Sensorimotor Learning
Tianyu Li, Bolun Dai
Learning from complex demonstrations is challenging, especially when the demonstration consists of different strategies. A popular approach is to use a deep neural network to perfo…
Deep Heterogeneous Autoencoders for Collaborative Filtering
Tianyu Li, Yukun Ma, Jiu Xu +3
This paper leverages heterogeneous auxiliary information to address the data sparsity problem of recommender systems. We propose a model that learns a shared feature space from het…
Using Deep Reinforcement Learning to Learn High-Level Policies on the ATRIAS Biped
Tianyu Li, Akshara Rai, Hartmut Geyer +1
Learning controllers for bipedal robots is a challenging problem, often requiring expert knowledge and extensive tuning of parameters that vary in different situations. Recently, d…
Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning
Guillaume Rabusseau, Tianyu Li, Doina Precup
In this paper, we unravel a fundamental connection between weighted finite automata~(WFAs) and second-order recurrent neural networks~(2-RNNs): in the case of sequences of discrete…
Neural Network Based Nonlinear Weighted Finite Automata
Tianyu Li, Guillaume Rabusseau, Doina Precup
Weighted finite automata (WFA) can expressively model functions defined over strings but are inherently linear models. Given the recent successes of nonlinear models in machine lea…