University of Pennsylvania

∙ Philadelphia , United States ∙ 0 followers

The University of Pennsylvania is a private Ivy League research university located in the University City neighborhood of Philadelphia, Pennsylvania.

Featured Researchers

Yu Chen
69 publications
Alejandro Ribeiro
55 publications
Hao Wu
53 publications
Dan Roth
53 publications
Sham M. Kakade
44 publications
Ufuk Topcu
43 publications
Xiao Zhang
43 publications
Jiaming Xu
42 publications
Alexander Rakhlin
41 publications
Kostas Daniilidis
41 publications
Ke Sun
41 publications

Sample Complexity of Kalman Filtering for Unknown Systems

In this paper, we consider the task of designing a Kalman Filter (KF) fo...
12/27/2019 ∙ by Anastasios Tsiamis, et al. ∙ 40 ∙ share

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SGP-DT: Semantic Genetic Programming Based on Dynamic Targets

Semantic GP is a promising approach that introduces semantic awareness d...
01/30/2020 ∙ by Stefano Ruberto, et al. ∙ 31 ∙ share

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EdgeNets:Edge Varying Graph Neural Networks

Driven by the outstanding performance of neural networks in the structur...
01/21/2020 ∙ by Elvin Isufi, et al. ∙ 29 ∙ share

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Finite Sample Analysis of Stochastic System Identification

In this paper, we analyze the finite sample complexity of stochastic sys...
03/21/2019 ∙ by Anastasios Tsiamis, et al. ∙ 26 ∙ share

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Monocular 3D Pose Recovery via Nonconvex Sparsity with Theoretical Analysis

For recovering 3D object poses from 2D images, a prevalent method is to ...
12/29/2018 ∙ by Jianqiao Wangni, et al. ∙ 24 ∙ share

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Optimal Algorithms for Submodular Maximization with Distributed Constraints

We consider a class of discrete optimization problems that aim to maximi...
09/30/2019 ∙ by Alexander Robey, et al. ∙ 21 ∙ share

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Continual Learning for Sentence Representations Using Conceptors

Distributed representations of sentences have become ubiquitous in natur...
04/18/2019 ∙ by Tianlin Liu, et al. ∙ 20 ∙ share

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Minibatch Processing in Spiking Neural Networks

Spiking neural networks (SNNs) are a promising candidate for biologicall...
09/05/2019 ∙ by Daniel J. Saunders, et al. ∙ 17 ∙ share

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Optimal Structured Principal Subspace Estimation: Metric Entropy and Minimax Rates

Driven by a wide range of applications, many principal subspace estimati...
02/18/2020 ∙ by T. Tony Cai, et al. ∙ 17 ∙ share

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EventGAN: Leveraging Large Scale Image Datasets for Event Cameras

Event cameras provide a number of benefits over traditional cameras, suc...
12/03/2019 ∙ by Alex Zihao Zhu, et al. ∙ 16 ∙ share

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Safe Predictors for Enforcing Input-Output Specifications

We present an approach for designing correct-by-construction neural netw...
01/29/2020 ∙ by Stephen Mell, et al. ∙ 16 ∙ share

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Cross-Domain 3D Equivariant Image Embeddings

Spherical convolutional networks have been introduced recently as tools ...
12/06/2018 ∙ by Carlos Esteves, et al. ∙ 14 ∙ share

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Equivariant Multi-View Networks

Several approaches to 3D vision tasks process multiple views of the inpu...
04/01/2019 ∙ by Carlos Esteves, et al. ∙ 14 ∙ share

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An Exploration of Embodied Visual Exploration

Embodied computer vision considers perception for robots in general, uns...
01/07/2020 ∙ by Santhosh K. Ramakrishnan, et al. ∙ 14 ∙ share

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On the Capabilities and Limitations of Reasoning for Natural Language Understanding

Recent systems for natural language understanding are strong at overcomi...
01/08/2019 ∙ by Daniel Khashabi, et al. ∙ 12 ∙ share

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Encoding High-Level Visual Attributes in Capsules for Explainable Medical Diagnoses

Deep neural networks are often called black-boxes due to their difficult...
09/12/2019 ∙ by Rodney LaLonde, et al. ∙ 12 ∙ share

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Learning Predictive Models From Observation and Interaction

Learning predictive models from interaction with the world allows an age...
12/30/2019 ∙ by Karl Schmeckpeper, et al. ∙ 12 ∙ share

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Depth Completion via Deep Basis Fitting

In this paper we consider the task of image-guided depth completion wher...
12/21/2019 ∙ by Chao Qu, et al. ∙ 11 ∙ share

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Active Learning in Video Tracking

Active learning methods, like uncertainty sampling, combined with probab...
12/29/2019 ∙ by Sima Behpour, et al. ∙ 11 ∙ share

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Predicting with Proxies

Predictive analytics is increasingly used to guide decision-making in ma...
12/28/2018 ∙ by Hamsa Bastani, et al. ∙ 10 ∙ share

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Semantic variation operators for multidimensional genetic programming

Multidimensional genetic programming represents candidate solutions as s...
04/18/2019 ∙ by William La Cava, et al. ∙ 10 ∙ share

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Cross-Lingual Ability of Multilingual BERT: An Empirical Study

Recent work has exhibited the surprising cross-lingual abilities of mult...
12/17/2019 ∙ by Karthikeyan K, et al. ∙ 10 ∙ share

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A Baseline for Few-Shot Image Classification

Fine-tuning a deep network trained with the standard cross-entropy loss ...
09/06/2019 ∙ by Guneet S. Dhillon, et al. ∙ 9 ∙ share

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All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks

Image feature matching is a fundamental part of many geometric computer ...
01/07/2019 ∙ by Stephen Phillips, et al. ∙ 8 ∙ share

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Not All Claims are Created Equal: Choosing the Right Approach to Assess Your Hypotheses

Empirical research in Natural Language Processing (NLP) has adopted a na...
11/10/2019 ∙ by Erfan Sadeqi Azer, et al. ∙ 8 ∙ share

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Distributed Attack-Robust Submodular Maximization for Multi-Robot Planning

We aim to guard swarm-robotics applications against denial-of-service (D...
10/02/2019 ∙ by Lifeng Zhou, et al. ∙ 7 ∙ share

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Labeling Panoramas with Spherical Hourglass Networks

With the recent proliferation of consumer-grade 360 cameras, it is worth...
09/06/2018 ∙ by Carlos Esteves, et al. ∙ 6 ∙ share

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Robustness Meets Deep Learning: An End-to-End Hybrid Pipeline for Unsupervised Learning of Egomotion

In this work, we propose a method that combines unsupervised deep learni...
12/20/2018 ∙ by Alex Zihao Zhu, et al. ∙ 6 ∙ share

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Black Box Submodular Maximization: Discrete and Continuous Settings

In this paper, we consider the problem of black box continuous submodula...
01/28/2019 ∙ by Lin Chen, et al. ∙ 6 ∙ share

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Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification

CNNs, RNNs, GCNs, and CapsNets have shown significant insights in repres...
06/09/2019 ∙ by Hao Peng, et al. ∙ 6 ∙ share

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Equal Opportunity in Online Classification with Partial Feedback

We study an online classification problem with partial feedback in which...
02/06/2019 ∙ by Yahav Bechavod, et al. ∙ 4 ∙ share

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Question Answering as Global Reasoning over Semantic Abstractions

We propose a novel method for exploiting the semantic structure of text ...
06/09/2019 ∙ by Daniel Khashabi, et al. ∙ 3 ∙ share

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Joint Reasoning for Temporal and Causal Relations

Understanding temporal and causal relations between events is a fundamen...
06/12/2019 ∙ by Qiang Ning, et al. ∙ 3 ∙ share

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Stochastic optimization approaches to learning concise representations

We propose and study a method for learning interpretable features via st...
07/03/2018 ∙ by William La Cava, et al. ∙ 2 ∙ share

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EBIC: an open source software for high-dimensional and big data biclustering analyses

Motivation: In this paper we present the latest release of EBIC, a next-...
07/26/2018 ∙ by Patryk Orzechowski, et al. ∙ 2 ∙ share

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Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes

Multi-output Gaussian processes (GPs) are a flexible Bayesian nonparamet...
06/01/2019 ∙ by Li-Fang Cheng, et al. ∙ 2 ∙ share

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Declarative Learning-Based Programming as an Interface to AI Systems

Data-driven approaches are becoming more common as problem-solving techn...
06/18/2019 ∙ by Parisa Kordjamshidi, et al. ∙ 2 ∙ share

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Neural Embedding for Physical Manipulations

In common real-world robotic operations, action and state spaces can be ...
07/13/2019 ∙ by Lingzhi Zhang, et al. ∙ 2 ∙ share

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Estimating heterogeneous treatment effects with right-censored data via causal survival forests

There is fast-growing literature on estimating heterogeneous treatment e...
01/27/2020 ∙ by Yifan Cui, et al. ∙ 1 ∙ share

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Counting Roots of Polynomials over Z/p^2Z

Until recently, the only known method of finding the roots of polynomial...
08/15/2017 ∙ by Trajan Hammonds, et al. ∙ 0 ∙ share

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Bayesian Q-learning with Assumed Density Filtering

While off-policy temporal difference methods have been broadly used in r...
12/09/2017 ∙ by Heejin Jeong, et al. ∙ 0 ∙ share

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ε-Lexicase selection: a probabilistic and multi-objective analysis of lexicase selection in continuous domains

Lexicase selection is a parent selection method that considers training ...
09/15/2017 ∙ by William La Cava, et al. ∙ 0 ∙ share

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Relief-Based Feature Selection: Introduction and Review

Feature selection plays a critical role in data mining, driven by increa...
11/22/2017 ∙ by Ryan J. Urbanowicz, et al. ∙ 0 ∙ share

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Tree-Structured Boosting: Connections Between Gradient Boosted Stumps and Full Decision Trees

Additive models, such as produced by gradient boosting, and full interac...
11/18/2017 ∙ by José Marcio Luna, et al. ∙ 0 ∙ share

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Stochastic Submodular Maximization: The Case of Coverage Functions

Stochastic optimization of continuous objectives is at the heart of mode...
11/05/2017 ∙ by Mohammad Reza Karimi, et al. ∙ 0 ∙ share

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Ensemble representation learning: an analysis of fitness and survival for wrapper-based genetic programming methods

Recently we proposed a general, ensemble-based feature engineering wrapp...
03/20/2017 ∙ by William La Cava, et al. ∙ 0 ∙ share

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Toward the automated analysis of complex diseases in genome-wide association studies using genetic programming

Machine learning has been gaining traction in recent years to meet the d...
02/06/2017 ∙ by Andrew Sohn, et al. ∙ 0 ∙ share

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Decentralized Online Learning with Kernels

We consider multi-agent stochastic optimization problems over reproducin...
10/11/2017 ∙ by Alec Koppel, et al. ∙ 0 ∙ share

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Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer

Knowledge transfer between tasks can improve the performance of learned ...
10/10/2017 ∙ by David Isele, et al. ∙ 0 ∙ share

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Unsupervised learning of image motion by recomposing sequences

We propose a new method for learning a representation of image motion in...
12/01/2016 ∙ by Andrew Jaegle, et al. ∙ 0 ∙ share

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