
Deformable Linear Object Prediction Using Locally Linear Latent Dynamics
We propose a framework for deformable linear object prediction. Predicti...
read it

Embracing the Disharmony in Heterogeneous Medical Data
Heterogeneity in medical imaging data is often tackled, in the context o...
read it

Continuous Doubly Constrained Batch Reinforcement Learning
Reliant on too many experiments to learn good actions, current Reinforce...
read it

Scalable Reinforcement Learning Policies for MultiAgent Control
This paper develops a stochastic MultiAgent Reinforcement Learning (MAR...
read it

An InformationGeometric Distance on the Space of Tasks
This paper computes a distance between tasks modeled as joint distributi...
read it

MIDAS: Multiagent Interactionaware Decisionmaking with Adaptive Strategies for Urban Autonomous Navigation
Autonomous navigation in crowded, complex urban environments requires in...
read it

Proximal Deterministic Policy Gradient
This paper introduces two simple techniques to improve offpolicy Reinfo...
read it

DDPG++: Striving for Simplicity in Continuouscontrol OffPolicy Reinforcement Learning
This paper prescribes a suite of techniques for offpolicy Reinforcement...
read it

Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation
Automated machine learning (AutoML) can produce complex model ensembles ...
read it

BayesRace: Learning to race autonomously using prior experience
Learning to race autonomously is a challenging problem. It requires perc...
read it

TraDE: Transformers for Density Estimation
We present TraDE, an attentionbased architecture for autoregressive de...
read it

A FreeEnergy Principle for Representation Learning
This paper employs a formal connection of machine learning with thermody...
read it

Directional Adversarial Training for Cost Sensitive Deep Learning Classification Applications
In many realworld applications of Machine Learning it is of paramount i...
read it

MetaQLearning
This paper introduces MetaQLearning (MQL), a new offpolicy algorithm ...
read it

A Baseline for FewShot Image Classification
Finetuning a deep network trained with the standard crossentropy loss ...
read it

P3O: Policyon Policyoff Policy Optimization
Onpolicy reinforcement learning (RL) algorithms have high sample comple...
read it

Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks
Stochastic gradient descent (SGD) is widely believed to perform implicit...
read it

Parle: parallelizing stochastic gradient descent
We propose a new algorithm called Parle for parallel training of deep ne...
read it

EntropySGD: Biasing Gradient Descent Into Wide Valleys
This paper proposes a new optimization algorithm called EntropySGD for ...
read it
Pratik Chaudhari
is this you? claim profile