Joseph Gonzalez

According to our database1, Joseph Gonzalez authored at least 118 papers between 2009 and 2020.

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Bibliography

2020
Safety Augmented Value Estimation From Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Tasks.
IEEE Robotics Autom. Lett., 2020

RILaaS: Robot Inference and Learning as a Service.
IEEE Robotics Autom. Lett., 2020

Cloudburst: Stateful Functions-as-a-Service.
Proc. VLDB Endow., 2020

Towards Scalable Dataframe Systems.
Proc. VLDB Endow., 2020

CoVista: A Unified View on Privacy Sensitive Mobile Contact Tracing.
IEEE Data Eng. Bull., 2020

Letter from the Special Issue Editor.
IEEE Data Eng. Bull., 2020

Intermittent Visual Servoing: Efficiently Learning Policies Robust to Instrument Changes for High-precision Surgical Manipulation.
CoRR, 2020

Untangling Dense Knots by Learning Task-Relevant Keypoints.
CoRR, 2020

Resource Allocation in Multi-armed Bandit Exploration: Overcoming Nonlinear Scaling with Adaptive Parallelism.
CoRR, 2020

Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones.
CoRR, 2020

A Statistical Framework for Low-bitwidth Training of Deep Neural Networks.
CoRR, 2020

Multi-Agent Collaboration via Reward Attribution Decomposition.
CoRR, 2020

MMGSD: Multi-Modal Gaussian Shape Descriptors for Correspondence Matching in 1D and 2D Deformable Objects.
CoRR, 2020

Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting.
CoRR, 2020

A Review of Single-Source Deep Unsupervised Visual Domain Adaptation.
CoRR, 2020

Benchmarking Semi-supervised Federated Learning.
CoRR, 2020

FetchSGD: Communication-Efficient Federated Learning with Sketching.
CoRR, 2020

Optimizing Prediction Serving on Low-Latency Serverless Dataflow.
CoRR, 2020

Boundary thickness and robustness in learning models.
CoRR, 2020

Contrastive Code Representation Learning.
CoRR, 2020

BEV-Seg: Bird's Eye View Semantic Segmentation Using Geometry and Semantic Point Cloud.
CoRR, 2020

Hindsight Logging for Model Training.
CoRR, 2020

SegNBDT: Visual Decision Rules for Segmentation.
CoRR, 2020

Ansor : Generating High-Performance Tensor Programs for Deep Learning.
CoRR, 2020

FBNetV3: Joint Architecture-Recipe Search using Neural Acquisition Function.
CoRR, 2020

CoVista: A Unified View on Privacy Sensitive Mobile Contact Tracing Effort.
CoRR, 2020

Mechanism Design with Bandit Feedback.
CoRR, 2020

NBDT: Neural-Backed Decision Trees.
CoRR, 2020

Learning to Smooth and Fold Real Fabric Using Dense Object Descriptors Trained on Synthetic Color Images.
CoRR, 2020

Frustratingly Simple Few-Shot Object Detection.
CoRR, 2020

ABC-LMPC: Safe Sample-Based Learning MPC for Stochastic Nonlinear Dynamical Systems with Adjustable Boundary Conditions.
CoRR, 2020

Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers.
CoRR, 2020

SqueezeWave: Extremely Lightweight Vocoders for On-device Speech Synthesis.
CoRR, 2020

Cloudburst: Stateful Functions-as-a-Service.
CoRR, 2020

Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization.
Proceedings of Machine Learning and Systems 2020, 2020

Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data.
Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020

Fog Robotics Algorithms for Distributed Motion Planning Using Lambda Serverless Computing.
Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020

Serverless Boom or Bust? An Analysis of Economic Incentives.
Proceedings of the 12th USENIX Workshop on Hot Topics in Cloud Computing, 2020

A fault-tolerance shim for serverless computing.
Proceedings of the EuroSys '20: Fifteenth EuroSys Conference 2020, 2020

Oblivious coopetitive analytics using hardware enclaves.
Proceedings of the EuroSys '20: Fifteenth EuroSys Conference 2020, 2020

FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

InferLine: latency-aware provisioning and scaling for prediction serving pipelines.
Proceedings of the SoCC '20: ACM Symposium on Cloud Computing, 2020

Thompson Sampling for Linearly Constrained Bandits.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Domain-Aware Dynamic Networks.
CoRR, 2019

Deep Reinforcement Learning in System Optimization.
CoRR, 2019

Task-Aware Deep Sampling for Feature Generation.
CoRR, 2019

ANODEV2: A Coupled Neural ODE Evolution Framework.
CoRR, 2019

Extending Deep Model Predictive Control with Safety Augmented Value Estimation from Demonstrations.
CoRR, 2019

SysML: The New Frontier of Machine Learning Systems.
CoRR, 2019

Constrained Thompson Sampling for Wireless Link Optimization.
CoRR, 2019

Cloud Programming Simplified: A Berkeley View on Serverless Computing.
CoRR, 2019

The OoO VLIW JIT Compiler for GPU Inference.
CoRR, 2019

Dynamic Space-Time Scheduling for GPU Inference.
CoRR, 2019

Scaling Video Analytics Systems to Large Camera Deployments.
Proceedings of the 20th International Workshop on Mobile Computing Systems and Applications, 2019

Deep Mixture of Experts via Shallow Embedding.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Helen: Maliciously Secure Coopetitive Learning for Linear Models.
Proceedings of the 2019 IEEE Symposium on Security and Privacy, 2019

Contextual Multi-Armed Bandits for Link Adaptation in Cellular Networks.
Proceedings of the 2019 Workshop on Network Meets AI & ML, 2019

ANODEV2: A Coupled Neural ODE Framework.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering.
Proceedings of the International Conference on Robotics and Automation, 2019

ACE: Adapting to Changing Environments for Semantic Segmentation.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

Accel: A Corrective Fusion Network for Efficient Semantic Segmentation on Video.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

On-Policy Robot Imitation Learning from a Converging Supervisor.
Proceedings of the 3rd Annual Conference on Robot Learning, 2019

HyperSched: Dynamic Resource Reallocation for Model Development on a Deadline.
Proceedings of the ACM Symposium on Cloud Computing, SoCC 2019, 2019

Serverless Computing: One Step Forward, Two Steps Back.
Proceedings of the CIDR 2019, 2019

2018
InferLine: ML Inference Pipeline Composition Framework.
CoRR, 2018

Using Multitask Learning to Improve 12-Lead Electrocardiogram Classification.
CoRR, 2018

On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent.
CoRR, 2018

ReXCam: Resource-Efficient, Cross-Camera Video Analytics at Enterprise Scale.
CoRR, 2018

Inter-BMV: Interpolation with Block Motion Vectors for Fast Semantic Segmentation on Video.
CoRR, 2018

Tune: A Research Platform for Distributed Model Selection and Training.
CoRR, 2018

Deep Mixture of Experts via Shallow Embedding.
CoRR, 2018

Unsupervised Domain Adaptation: from Simulation Engine to the RealWorld.
CoRR, 2018

Fast Semantic Segmentation on Video Using Motion Vector-Based Feature Interpolation.
CoRR, 2018

Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning.
CoRR, 2018

Research for practice: prediction-serving systems.
Commun. ACM, 2018

IDK Cascades: Fast Deep Learning by Learning not to Overthink.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

RLlib: Abstractions for Distributed Reinforcement Learning.
Proceedings of the 35th International Conference on Machine Learning, 2018

SkipNet: Learning Dynamic Routing in Convolutional Networks.
Proceedings of the Computer Vision - ECCV 2018, 2018

Fast Semantic Segmentation on Video Using Block Motion-Based Feature Interpolation.
Proceedings of the Computer Vision - ECCV 2018 Workshops, 2018

Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2017
Ray RLLib: A Composable and Scalable Reinforcement Learning Library.
CoRR, 2017

A Berkeley View of Systems Challenges for AI.
CoRR, 2017

SkipNet: Learning Dynamic Routing in Convolutional Networks.
CoRR, 2017

Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning.
CoRR, 2017

IDK Cascades: Fast Deep Learning by Learning not to Overthink.
CoRR, 2017

Hemingway: Modeling Distributed Optimization Algorithms.
CoRR, 2017

Opaque: An Oblivious and Encrypted Distributed Analytics Platform.
Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation, 2017

Clipper: A Low-Latency Online Prediction Serving System.
Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation, 2017

Selecting the <i>best</i> VM across multiple public clouds: a data-driven performance modeling approach.
Proceedings of the 2017 Symposium on Cloud Computing, SoCC 2017, Santa Clara, CA, USA, 2017


Random projection design for scalable implicit smoothing of randomly observed stochastic processes.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016
Multi-Task Learning for Straggler Avoiding Predictive Job Scheduling.
J. Mach. Learn. Res., 2016

Scalable Linear Causal Inference for Irregularly Sampled Time Series with Long Range Dependencies.
CoRR, 2016

Apache Spark: a unified engine for big data processing.
Commun. ACM, 2016

GraphFrames: an integrated API for mixing graph and relational queries.
Proceedings of the Fourth International Workshop on Graph Data Management Experiences and Systems, Redwood Shores, CA, USA, June 24, 2016

2015
Asynchronous Complex Analytics in a Distributed Dataflow Architecture.
CoRR, 2015

Faster Jobs in Distributed Data Processing using Multi-Task Learning.
Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, BC, Canada, April 30, 2015

The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox.
Proceedings of the CIDR 2015, 2015

Efficient data reduction for large-scale genetic mapping.
Proceedings of the 6th ACM Conference on Bioinformatics, 2015

2014
GraphX: Unifying Data-Parallel and Graph-Parallel Analytics.
CoRR, 2014

From graphs to tables the design of scalable systems for graph analytics.
Proceedings of the 23rd International World Wide Web Conference, 2014

GraphX: Graph Processing in a Distributed Dataflow Framework.
Proceedings of the 11th USENIX Symposium on Operating Systems Design and Implementation, 2014

Parallel Double Greedy Submodular Maximization.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

GABB Introduction.
Proceedings of the 2014 IEEE International Parallel & Distributed Processing Symposium Workshops, 2014

Efficient and accurate clustering for large-scale genetic mapping.
Proceedings of the 2014 IEEE International Conference on Bioinformatics and Biomedicine, 2014

2013
GraphX: a resilient distributed graph system on Spark.
Proceedings of the First International Workshop on Graph Data Management Experiences and Systems, 2013

Optimistic Concurrency Control for Distributed Unsupervised Learning.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

MLI: An API for Distributed Machine Learning.
Proceedings of the 2013 IEEE 13th International Conference on Data Mining, 2013


2012
Distributed GraphLab: A Framework for Machine Learning in the Cloud.
Proc. VLDB Endow., 2012

Scalable inference in latent variable models.
Proceedings of the Fifth International Conference on Web Search and Web Data Mining, 2012

PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs.
Proceedings of the 10th USENIX Symposium on Operating Systems Design and Implementation, 2012

2011
Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees.
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

GraphLab: A Distributed Framework for Machine Learning in the Cloud
CoRR, 2011

2010
GraphLab: A New Framework For Parallel Machine Learning.
Proceedings of the UAI 2010, 2010

2009
Residual Splash for Optimally Parallelizing Belief Propagation.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Distributed Parallel Inference on Large Factor Graphs.
Proceedings of the UAI 2009, 2009


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