Shiv Shankar | DeepAI

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∙ 02/06/2023

Optimization using Parallel Gradient Evaluations on Multiple Parameters

We propose a first-order method for convex optimization, where instead o...

0 Yash Chandak, et al. ∙

∙ 01/24/2023

Off-Policy Evaluation for Action-Dependent Non-Stationary Environments

Methods for sequential decision-making are often built upon a foundation...

6 Yash Chandak, et al. ∙

∙ 11/21/2022

Implicit Training of Energy Model for Structure Prediction

Most deep learning research has focused on developing new model and trai...

0 Shiv Shankar, et al. ∙

∙ 11/03/2022

Privacy Aware Experiments without Cookies

Consider two brands that want to jointly test alternate web experiences ...

0 Shiv Shankar, et al. ∙

∙ 09/01/2022

Progressive Fusion for Multimodal Integration

Integration of multimodal information from various sources has been show...

2 Shiv Shankar, et al. ∙

∙ 09/28/2021

Neural Dependency Coding inspired Multimodal Fusion

Information integration from different modalities is an active area of r...

5 Shiv Shankar, et al. ∙

∙ 08/30/2021

Adversarial Stein Training for Graph Energy Models

Learning distributions over graph-structured data is a challenging task ...

19 Shiv Shankar, et al. ∙

∙ 07/03/2021

Sibling Regression for Generalized Linear Models

Field observations form the basis of many scientific studies, especially...

7 Shiv Shankar, et al. ∙

∙ 01/25/2021

High-Confidence Off-Policy (or Counterfactual) Variance Estimation

Many sequential decision-making systems leverage data collected using pr...

12 Yash Chandak, et al. ∙

∙ 12/31/2020

Bosonic Random Walk Networks for Graph Learning

The development of Graph Neural Networks (GNNs) has led to great progres...

3 Shiv Shankar, et al. ∙

∙ 12/31/2020

Three-quarter Sibling Regression for Denoising Observational Data

Many ecological studies and conservation policies are based on field obs...

1 Shiv Shankar, et al. ∙

∙ 07/09/2020

Untapped Potential of Data Augmentation: A Domain Generalization Viewpoint

Data augmentation is a popular pre-processing trick to improve generaliz...

0 Vihari Piratla, et al. ∙

∙ 05/17/2020

Optimizing for the Future in Non-Stationary MDPs

Most reinforcement learning methods are based upon the key assumption th...

2 Yash Chandak, et al. ∙

∙ 09/06/2019

Differential Equation Units: Learning Functional Forms of Activation Functions from Data

Most deep neural networks use simple, fixed activation functions, such a...

0 Ali Torkamani, et al. ∙

∙ 05/19/2019

Learning Compact Neural Networks Using Ordinary Differential Equations as Activation Functions

Most deep neural networks use simple, fixed activation functions, such a...

0 Ali Torkamani, et al. ∙

∙ 04/28/2018

Generalizing Across Domains via Cross-Gradient Training

We present CROSSGRAD, a method to use multi-domain training data to lear...

0 Shiv Shankar, et al. ∙

∙ 07/05/2017

Labeled Memory Networks for Online Model Adaptation

Augmenting a neural network with memory that can grow without growing th...

0 Shiv Shankar, et al. ∙

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