b'Saurabh Garg'

research

∙ 06/01/2023

(Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy

We derive an (almost) guaranteed upper bound on the error of deep neural...

0 Elan Rosenfeld, et al. ∙

research

∙ 05/31/2023

Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms

This paper focuses on supervised and unsupervised online label shift, wh...

0 Dheeraj Baby, et al. ∙

research

∙ 03/01/2023

Fog Device-as-a-Service (FDaaS): A Framework for Service Deployment in Public Fog Environments

Meeting the requirements of future services with time sensitivity and ha...

0 Sudheer Kumar Battula, et al. ∙

research

∙ 02/06/2023

RLSbench: Domain Adaptation Under Relaxed Label Shift

Despite the emergence of principled methods for domain adaptation under ...

0 Saurabh Garg, et al. ∙

research

∙ 02/06/2023

CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets

Open vocabulary models (e.g. CLIP) have shown strong performance on zero...

0 Zachary Novack, et al. ∙

research

∙ 11/29/2022

Disentangling the Mechanisms Behind Implicit Regularization in SGD

A number of competing hypotheses have been proposed to explain why small...

0 Zachary Novack, et al. ∙

research

∙ 10/26/2022

Characterizing Datapoints via Second-Split Forgetting

Researchers investigating example hardness have increasingly focused on ...

0 Pratyush Maini, et al. ∙

research

∙ 09/28/2022

Downstream Datasets Make Surprisingly Good Pretraining Corpora

For most natural language processing tasks, the dominant practice is to ...

0 Kundan Krishna, et al. ∙

research

∙ 07/26/2022

Unsupervised Learning under Latent Label Shift

What sorts of structure might enable a learner to discover classes from ...

0 Manley Roberts, et al. ∙

research

∙ 07/26/2022

Domain Adaptation under Open Set Label Shift

We introduce the problem of domain adaptation under Open Set Label Shift...

0 Saurabh Garg, et al. ∙

research

∙ 07/06/2022

A Comprehensive Review on Deep Supervision: Theories and Applications

Deep supervision, or known as 'intermediate supervision' or 'auxiliary s...

0 Renjie Li, et al. ∙

research

∙ 02/20/2022

Deconstructing Distributions: A Pointwise Framework of Learning

In machine learning, we traditionally evaluate the performance of a sing...

0 Gal Kaplun, et al. ∙

research

∙ 01/11/2022

Leveraging Unlabeled Data to Predict Out-of-Distribution Performance

Real-world machine learning deployments are characterized by mismatches ...

71 Saurabh Garg, et al. ∙

research

∙ 12/19/2021

Parallel Multi-Scale Networks with Deep Supervision for Hand Keypoint Detection

Keypoint detection plays an important role in a wide range of applicatio...

7 Renjie Li, et al. ∙

research

∙ 11/01/2021

Mixture Proportion Estimation and PU Learning: A Modern Approach

Given only positive examples and unlabeled examples (from both positive ...

0 Saurabh Garg, et al. ∙

research

∙ 10/29/2021

SDP: Scalable Real-time Dynamic Graph Partitioner

Time-evolving large graph has received attention due to their participat...

0 Md Anwarul kaium Patwary, et al. ∙

research

∙ 08/23/2021

Towards a Formal Modelling, Analysis, and Verification of a Clone Node Attack Detection Scheme in the Internet of Things

In a clone node attack, an attacker attempted to physically capture the ...

0 Khizar Hameed, et al. ∙

research

∙ 06/30/2021

A Context-Aware Information-Based Clone Node Attack Detection Scheme in Internet of Things

The rapidly expanding nature of the Internet of Things (IoT) networks is...

0 Khizar Hameed, et al. ∙

research

∙ 05/25/2021

A Taxonomy Study on Securing Blockchain-based Industrial Applications: An Overview, Application Perspectives, Requirements, Attacks, Countermeasures, and Open Issues

Blockchain technology has taken on a leading role in today's industrial ...

0 Khizar Hameed, et al. ∙

research

∙ 05/01/2021

RATT: Leveraging Unlabeled Data to Guarantee Generalization

To assess generalization, machine learning scientists typically either (...

0 Saurabh Garg, et al. ∙

research

∙ 04/29/2021

Applications of Artificial Intelligence to aid detection of dementia: a narrative review on current capabilities and future directions

With populations ageing, the number of people with dementia worldwide is...

0 Renjie Li, et al. ∙

research

∙ 03/10/2021

Multiple Linear Regression-Based Energy-Aware Resource Allocation in the Fog Computing Environment

Fog computing is a promising computing paradigm for time-sensitive Inter...

0 Ranesh Kumar Naha, et al. ∙

research

∙ 03/10/2021

Fuzzy Logic-based Robust Failure Handling Mechanism for Fog Computing

Fog computing is an emerging computing paradigm which is mainly suitable...

0 Ranesh Kumar Naha, et al. ∙

research

∙ 02/20/2021

On Proximal Policy Optimization's Heavy-tailed Gradients

Modern policy gradient algorithms, notably Proximal Policy Optimization ...

0 Saurabh Garg, et al. ∙

research

∙ 03/17/2020

A Unified View of Label Shift Estimation

Label shift describes the setting where although the label distribution ...

3 Saurabh Garg, et al. ∙

research

∙ 03/01/2020

Authentication, Access Control, Privacy, Threats and Trust Management Towards Securing Fog Computing Environments: A Review

Fog computing is an emerging computing paradigm that has come into consi...

0 Abdullah Al-Noman Patwary, et al. ∙

research

∙ 12/18/2019

Adaptive Scheduling for Efficient Execution of Dynamic Stream Workflows

Stream workflow application such as online anomaly detection or online t...

0 Mutaz Barika, et al. ∙

research

∙ 12/18/2019

Scheduling Algorithms for Efficient Execution of Stream Workflow Applications in Multicloud Environments

Big data processing applications are becoming more and more complex. The...

0 Mutaz Barika, et al. ∙

research

∙ 12/17/2019

Multi-Criteria-based Dynamic User Behaviour Aware Resource Allocation in Fog Computing

Fog computing is a promising computing paradigm in which IoT data can be...

0 Ranesh Kumar Naha, et al. ∙

research

∙ 10/10/2019

BigDataSDNSim: A Simulator for Analyzing Big Data Applications in Software-Defined Cloud Data Centers

Emerging paradigms of big data and Software-Defined Networking (SDN) in ...

0 Khaled Alwasel, et al. ∙

research

∙ 10/07/2019

IoTSim-Edge: A Simulation Framework for Modeling the Behaviour of IoT and Edge Computing Environments

This paper proposes a novel simulator IoTSim-Edge, which captures the be...

0 Devki Nandan Jha, et al. ∙

research

∙ 02/05/2019

Window-based Streaming Graph Partitioning Algorithm

In the recent years, the scale of graph datasets has increased to such a...

0 Md Anwarul kaium Patwary, et al. ∙

research

∙ 11/22/2018

Fog Computing Architecture: Survey and Challenges

Emerging technologies that generate a huge amount of data such as the In...

0 Ranesh Kumar Naha, et al. ∙

research

∙ 09/06/2018

Code-switched Language Models Using Dual RNNs and Same-Source Pretraining

This work focuses on building language models (LMs) for code-switched te...

0 Saurabh Garg, et al. ∙

research

∙ 07/03/2018

Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions

Emerging technologies like the Internet of Things (IoT) require latency-...

0 Ranesh Kumar Naha, et al. ∙

research

∙ 06/20/2018

User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

Recommender systems have become an integral part of many social networks...

0 Erfan Aghasian, et al. ∙

research

∙ 04/16/2018

Automatic Rain and Cicada Chorus Filtering of Bird Acoustic Data

Recording and analysing environmental audio recordings has become a comm...

0 Alexander Brown, et al. ∙

research

∙ 02/02/2018

Scalable Preprocessing of High Volume Bird Acoustic Data

In this work, we examine the problem of efficiently preprocessing high v...

0 Alexander Brown, et al. ∙

research

∙ 11/03/2017

Dual Language Models for Code Mixed Speech Recognition

In this work, we present a new approach to language modeling for bilingu...

0 Saurabh Garg, et al. ∙

Saurabh Garg

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