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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...
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01/24/2023
Off-Policy Evaluation for Action-Dependent Non-Stationary Environments
Methods for sequential decision-making are often built upon a foundation...
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11/21/2022
Implicit Training of Energy Model for Structure Prediction
Most deep learning research has focused on developing new model and trai...
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11/03/2022
Privacy Aware Experiments without Cookies
Consider two brands that want to jointly test alternate web experiences ...
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09/01/2022
Progressive Fusion for Multimodal Integration
Integration of multimodal information from various sources has been show...
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09/28/2021
Neural Dependency Coding inspired Multimodal Fusion
Information integration from different modalities is an active area of r...
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08/30/2021
Adversarial Stein Training for Graph Energy Models
Learning distributions over graph-structured data is a challenging task ...
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07/03/2021
Sibling Regression for Generalized Linear Models
Field observations form the basis of many scientific studies, especially...
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01/25/2021
High-Confidence Off-Policy (or Counterfactual) Variance Estimation
Many sequential decision-making systems leverage data collected using pr...
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12/31/2020
Bosonic Random Walk Networks for Graph Learning
The development of Graph Neural Networks (GNNs) has led to great progres...
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12/31/2020
Three-quarter Sibling Regression for Denoising Observational Data
Many ecological studies and conservation policies are based on field obs...
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07/09/2020
Untapped Potential of Data Augmentation: A Domain Generalization Viewpoint
Data augmentation is a popular pre-processing trick to improve generaliz...
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05/17/2020
Optimizing for the Future in Non-Stationary MDPs
Most reinforcement learning methods are based upon the key assumption th...
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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...
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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...
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04/28/2018
Generalizing Across Domains via Cross-Gradient Training
We present CROSSGRAD, a method to use multi-domain training data to lear...
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07/05/2017