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09/08/2023
On the Efficacy of Multi-scale Data Samplers for Vision Applications
Multi-scale resolution training has seen an increased adoption across mu...
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03/15/2023
Reinforce Data, Multiply Impact: Improved Model Accuracy and Robustness with Dataset Reinforcement
We propose Dataset Reinforcement, a strategy to improve a dataset once s...
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07/05/2022
An Empirical Study of Implicit Regularization in Deep Offline RL
Deep neural networks are the most commonly used function approximators i...
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02/20/2022
Efficient Continual Learning Ensembles in Neural Network Subspaces
A growing body of research in continual learning focuses on the catastro...
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02/01/2022
Architecture Matters in Continual Learning
A large body of research in continual learning is devoted to overcoming ...
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10/21/2021
Wide Neural Networks Forget Less Catastrophically
A growing body of research in continual learning is devoted to overcomin...
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06/24/2021
Task-agnostic Continual Learning with Hybrid Probabilistic Models
Learning new tasks continuously without forgetting on a constantly chang...
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11/23/2020
Balance Regularized Neural Network Models for Causal Effect Estimation
Estimating individual and average treatment effects from observational d...
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10/09/2020
Linear Mode Connectivity in Multitask and Continual Learning
Continual (sequential) training and multitask (simultaneous) training ar...
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10/06/2020
The Effectiveness of Memory Replay in Large Scale Continual Learning
We study continual learning in the large scale setting where tasks in th...
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06/19/2020
SOLA: Continual Learning with Second-Order Loss Approximation
Neural networks have achieved remarkable success in many cognitive tasks...
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06/17/2020
A maximum-entropy approach to off-policy evaluation in average-reward MDPs
This work focuses on off-policy evaluation (OPE) with function approxima...
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06/12/2020
Understanding the Role of Training Regimes in Continual Learning
Catastrophic forgetting affects the training of neural networks, limitin...
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06/10/2020
Learning to Incentivize Other Learning Agents
The challenge of developing powerful and general Reinforcement Learning ...
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04/24/2020
Dropout as an Implicit Gating Mechanism For Continual Learning
In recent years, neural networks have demonstrated an outstanding abilit...
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02/13/2020
Self-Distillation Amplifies Regularization in Hilbert Space
Knowledge distillation introduced in the deep learning context is a meth...
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10/15/2019
Orthogonal Gradient Descent for Continual Learning
Neural networks are achieving state of the art and sometimes super-human...
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06/13/2019
Cross-View Policy Learning for Street Navigation
The ability to navigate from visual observations in unfamiliar environme...
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02/09/2019
Improved Knowledge Distillation via Teacher Assistant: Bridging the Gap Between Student and Teacher
Despite the fact that deep neural networks are powerful models and achie...
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02/10/2018
More Robust Doubly Robust Off-policy Evaluation
We study the problem of off-policy evaluation (OPE) in reinforcement lea...
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12/12/2017
Hawkes Processes for Invasive Species Modeling and Management
The spread of invasive species to new areas threatens the stability of e...
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05/23/2017
Wasserstein Learning of Deep Generative Point Process Models
Point processes are becoming very popular in modeling asynchronous seque...
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03/04/2017
Recurrent Poisson Factorization for Temporal Recommendation
Poisson factorization is a probabilistic model of users and items for re...
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10/24/2016
Distilling Information Reliability and Source Trustworthiness from Digital Traces
Online knowledge repositories typically rely on their users or dedicated...
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05/22/2016
Smart broadcasting: Do you want to be seen?
Many users in online social networks are constantly trying to gain atten...
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03/29/2016
Detecting weak changes in dynamic events over networks
Large volume of networked streaming event data are becoming increasingly...
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02/14/2016
Learning Granger Causality for Hawkes Processes
Learning Granger causality for general point processes is a very challen...
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07/08/2015
