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08/17/2023
Regularizing Adversarial Imitation Learning Using Causal Invariance
Imitation learning methods are used to infer a policy in a Markov decisi...
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06/15/2023
Improving Explainability of Disentangled Representations using Multipath-Attribution Mappings
Explainable AI aims to render model behavior understandable by humans, w...
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06/14/2023
Explore In-Context Learning for 3D Point Cloud Understanding
With the rise of large-scale models trained on broad data, in-context le...
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10/07/2022
How to Enable Uncertainty Estimation in Proximal Policy Optimization
While deep reinforcement learning (RL) agents have showcased strong resu...
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09/26/2022
Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs
In principle, applying variational autoencoders (VAEs) to sequential dat...
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08/22/2022
BARReL: Bottleneck Attention for Adversarial Robustness in Vision-Based Reinforcement Learning
Robustness to adversarial perturbations has been explored in many areas ...
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06/24/2022
Gated Domain Units for Multi-source Domain Generalization
Distribution shift (DS) is a common problem that deteriorates the perfor...
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03/22/2021
Spatially Dependent U-Nets: Highly Accurate Architectures for Medical Imaging Segmentation
In clinical practice, regions of interest in medical imaging often need ...
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03/16/2021
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling
How to improve generative modeling by better exploiting spatial regulari...
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01/25/2021
On maximum-likelihood estimation in the all-or-nothing regime
We study the problem of estimating a rank-1 additive deformation of a Ga...
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11/23/2020
Statistical and computational thresholds for the planted k-densest sub-hypergraph problem
Recovery a planted signal perturbed by noise is a fundamental problem in...
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08/13/2020
Neural collaborative filtering for unsupervised mitral valve segmentation in echocardiography
The segmentation of the mitral valve annulus and leaflets specifies a cr...
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06/24/2020
Continuous Submodular Function Maximization
Continuous submodular functions are a category of generally non-convex/n...
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06/01/2020
From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models
Submodular functions have been studied extensively in machine learning a...
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06/14/2019
Variational Federated Multi-Task Learning
In classical federated learning a central server coordinates the trainin...
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06/07/2019
Disentangled State Space Representations
Sequential data often originates from diverse domains across which stati...
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02/03/2019
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves
Estimating what would be an individual's potential response to varying l...
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01/21/2019
Exact Recovery for a Family of Community-Detection Generative Models
Generative models for networks with communities have been studied extens...
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05/19/2018
Optimal DR-Submodular Maximization and Applications to Provable Mean Field Inference
Mean field inference in probabilistic models is generally a highly nonco...
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04/12/2018
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs
Parameter identification and comparison of dynamical systems is a challe...
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11/04/2017
Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms
DR-submodular continuous functions are important objectives with wide re...
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05/19/2017
Scalable Variational Inference for Dynamical Systems
Gradient matching is a promising tool for learning parameters and state ...
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11/21/2016
Scalable Adaptive Stochastic Optimization Using Random Projections
Adaptive stochastic gradient methods such as AdaGrad have gained popular...
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10/21/2016
Mean-Field Variational Inference for Gradient Matching with Gaussian Processes
Gradient matching with Gaussian processes is a promising tool for learni...
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10/04/2016
Model Selection for Gaussian Process Regression by Approximation Set Coding
Gaussian processes are powerful, yet analytically tractable models for s...
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06/02/2016
Multi-Organ Cancer Classification and Survival Analysis
Accurate and robust cell nuclei classification is the cornerstone for a ...
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04/21/2016
TI-POOLING: transformation-invariant pooling for feature learning in Convolutional Neural Networks
In this paper we present a deep neural network topology that incorporate...
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12/31/2015
Computational Pathology: Challenges and Promises for Tissue Analysis
The histological assessment of human tissue has emerged as the key chall...
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06/24/2013
Correlated random features for fast semi-supervised learning
This paper presents Correlated Nystrom Views (XNV), a fast semi-supervis...
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12/19/2012
Role Mining with Probabilistic Models
Role mining tackles the problem of finding a role-based access control (...
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05/28/2012
Learning Dictionaries with Bounded Self-Coherence
Sparse coding in learned dictionaries has been established as a successf...
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02/15/2011