
Harmonization with Flowbased Causal Inference
Heterogeneity in medical data, e.g., from data collected at different si...
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Boosting a Model Zoo for MultiTask and Continual Learning
Leveraging data from multiple tasks, either all at once, or incrementall...
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Deformable Linear Object Prediction Using Locally Linear Latent Dynamics
We propose a framework for deformable linear object prediction. Predicti...
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Embracing the Disharmony in Heterogeneous Medical Data
Heterogeneity in medical imaging data is often tackled, in the context o...
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Continuous Doubly Constrained Batch Reinforcement Learning
Reliant on too many experiments to learn good actions, current Reinforce...
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Scalable Reinforcement Learning Policies for MultiAgent Control
This paper develops a stochastic MultiAgent Reinforcement Learning (MAR...
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An InformationGeometric Distance on the Space of Tasks
This paper computes a distance between tasks modeled as joint distributi...
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MIDAS: Multiagent Interactionaware Decisionmaking with Adaptive Strategies for Urban Autonomous Navigation
Autonomous navigation in crowded, complex urban environments requires in...
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Proximal Deterministic Policy Gradient
This paper introduces two simple techniques to improve offpolicy Reinfo...
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DDPG++: Striving for Simplicity in Continuouscontrol OffPolicy Reinforcement Learning
This paper prescribes a suite of techniques for offpolicy Reinforcement...
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Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation
Automated machine learning (AutoML) can produce complex model ensembles ...
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BayesRace: Learning to race autonomously using prior experience
Learning to race autonomously is a challenging problem. It requires perc...
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TraDE: Transformers for Density Estimation
We present TraDE, an attentionbased architecture for autoregressive de...
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A FreeEnergy Principle for Representation Learning
This paper employs a formal connection of machine learning with thermody...
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Directional Adversarial Training for Cost Sensitive Deep Learning Classification Applications
In many realworld applications of Machine Learning it is of paramount i...
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MetaQLearning
This paper introduces MetaQLearning (MQL), a new offpolicy algorithm ...
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A Baseline for FewShot Image Classification
Finetuning a deep network trained with the standard crossentropy loss ...
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P3O: Policyon Policyoff Policy Optimization
Onpolicy reinforcement learning (RL) algorithms have high sample comple...
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Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks
Stochastic gradient descent (SGD) is widely believed to perform implicit...
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Parle: parallelizing stochastic gradient descent
We propose a new algorithm called Parle for parallel training of deep ne...
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EntropySGD: Biasing Gradient Descent Into Wide Valleys
This paper proposes a new optimization algorithm called EntropySGD for ...
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Pratik Chaudhari
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