Research Area
Year Published

669 Results

November 30, 2018

A Block Coordinate Ascent Algorithm for Mean-Variance Optimization

Neural Information Processing Systems (NeurIPS)

Risk management in dynamic decision problems is a primary concern in many fields, including financial investment, autonomous driving, and healthcare. The mean-variance function is one of the most widely used objective functions in risk management due to its simplicity and interpretability. Existing algorithms for mean-variance optimization are based on multi-time-scale stochastic approximation, whose learning rate schedules are often hard to tune, and have only asymptotic convergence proof. In this paper, we develop a model-free policy search framework for mean-variance optimization with finite-sample error bound analysis (to local optima).

By: Tengyang Xie, Bo Liu, Yangyang Xu, Mohammad Ghavamzadeh, Yinlam Chow, Daoming Lyu, Daesub Yoon

November 30, 2018

A Lyapunov-based Approach to Safe Reinforcement Learning

Neural Information Processing Systems (NeurIPS)

To incorporate safety in RL, we derive algorithms under the framework of constrained Markov decision processes (CMDPs), an extension of the standard Markov decision processes (MDPs) augmented with constraints on expected cumulative costs. Our approach hinges on a novel Lyapunov method.

By: Yinlam Chow, Ofir Nachum, Mohammad Ghavamzadeh, Edgar Duenez-Guzman

November 27, 2018

AnoGen: Deep Anomaly Generator

Outlier Detection De-constructed (ODD) Workshop

Motivated by the continued success of Variational Auto-Encoders (VAE) and Generative Adversarial Networks (GANs) to produce realistic-looking data we provide a platform to generate a realistic time-series with anomalies called AnoGen.

By: Nikolay Laptev

November 27, 2018

Deep Incremental Learning for Efficient High-Fidelity Face Tracking

ACM SIGGRAPH ASIA 2018

In this paper, we present an incremental learning framework for efficient and accurate facial performance tracking. Our approach is to alternate the modeling step, which takes tracked meshes and texture maps to train our deep learning-based statistical model, and the tracking step, which takes predictions of geometry and texture our model infers from measured images and optimize the predicted geometry by minimizing image, geometry and facial landmark errors.

By: Chenglei Wu, Takaaki Shiratori, Yaser Sheikh

November 27, 2018

Machine Learning for Reliable mmWave Systems: Blockage Prediction and Proactive Handoff

IEEE Global Conference on Signal and Information Processing (GlobalSIP)

The sensitivity of millimeter wave (mmWave) signals to blockages is a fundamental challenge for mobile mmWave communication systems. The sudden blockage of the line-of-sight (LOS) link between the base station and the mobile user normally leads to disconnecting the communication session, which highly impacts the system reliability. Further, reconnecting the user to another LOS base station incurs high beam training overhead and critical latency problem. In this paper, we leverage machine learning tools and propose a novel solution for these reliability and latency challenges in mmWave MIMO systems

By: Ahmed Alkhateeb, Iz Beltagy, Sam Alex

November 24, 2018

Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications

ArXiv

The application of deep learning techniques resulted in remarkable improvement of machine learning models. In this paper we provide detailed characterizations of deep learning models used in many Facebook social network services.

By: Jongsoo Park, Maxim Naumov, Protonu Basu, Summer Deng, Aravind Kalaiah, Daya Khudia, James Law, Parth Malani, Andrey Malevich, Satish Nadathur, Juan Pino, Martin Schatz, Alexander Sidorov, Viswanath Sivakumar, Andrew Tulloch, Xiaodong Wang, Yiming Wu, Hector Yuen, Utku Diril, Dmytro Dzhulgakov, Kim Hazelwood, Bill Jia, Yangqing Jia, Lin Qiao, Vijay Rao, Nadav Rotem, Sungjoo Yoo, Mikhail Smelyanskiy

November 24, 2018

On Periodic Functions as Regularizers for Quantization of Neural Networks

ArXiv

Deep learning models have been successfully used in computer vision and many other fields. We propose an unorthodox algorithm for performing quantization of the model parameters.

By: Maxim Naumov, Utku Diril, Jongsoo Park, Benjamin Ray, Jedrzej Jablonski, Andrew Tulloch

November 7, 2018

Social Connectedness: Measurement, Determinants, and Effects

Journal of Economic Perspectives

Social networks can shape many aspects of social and economic activity: migration and trade, job-seeking, innovation, consumer preferences and sentiment, public health, social mobility, and more. In turn, social networks themselves are associated with geographic proximity, historical ties, political boundaries, and other factors.

By: Michael Bailey, Rachel Cao, Theresa Kuchler, Johannes Stroebel, Arlene Wong

November 3, 2018

The Effect of Computer-Generated Descriptions on Photo-Sharing Experiences of People with Visual Impairments

Conference on Computer-Supported Cooperative Work and Social Computing (CSCW)

Like sighted people, visually impaired people want to share photographs on social networking services, but find it difficult to identify and select photos from their albums. We aimed to address this problem by incorporating state-of-the-art computer-generated descriptions into Facebook’s photo-sharing feature.

By: Yuhang Zhao, Shaomei Wu, Lindsay Reynolds, Shiri Azenkot

November 3, 2018

How Social Ties Influence Hurricane Evacuation Behavior

Conference on Computer-Supported Cooperative Work and Social Computing (CSCW)

This work is the first of its kind, examining these phenomena across three major disasters in the United States—Hurricane Harvey, Hurricane Irma, and Hurricane Maria—using aggregated, de-identified data from over 1.5 million Facebook users.

By: Danaë Metaxa-Kakavouli, Paige Maas, Daniel P. Aldrich