Zobrazeno 1 - 10
of 144
pro vyhledávání: '"Shevade, Shirish"'
Autor:
Sahu, Surya Prakash, Mandal, Madhurima, Bharadwaj, Shikhar, Kanade, Aditya, Maniatis, Petros, Shevade, Shirish
Developers often have questions about semantic aspects of code they are working on, e.g., "Is there a class whose parent classes declare a conflicting attribute?". Answering them requires understanding code semantics such as attributes and inheritanc
Externí odkaz:
http://arxiv.org/abs/2209.08372
The personalized list continuation (PLC) task is to curate the next items to user-generated lists (ordered sequence of items) in a personalized way. The main challenge in this task is understanding the ternary relationships among the interacting enti
Externí odkaz:
http://arxiv.org/abs/2110.01467
Machine-Learning-as-a-Service providers expose machine learning (ML) models through application programming interfaces (APIs) to developers. Recent work has shown that attackers can exploit these APIs to extract good approximations of such ML models,
Externí odkaz:
http://arxiv.org/abs/2107.05166
Cross-Domain Collaborative Filtering (CDCF) provides a way to alleviate data sparsity and cold-start problems present in recommendation systems by exploiting the knowledge from related domains. Existing CDCF models are either based on matrix factoriz
Externí odkaz:
http://arxiv.org/abs/1907.08440
Providing feedback is an integral part of teaching. Most open online courses on programming make use of automated grading systems to support programming assignments and give real-time feedback. These systems usually rely on test results to quantify t
Externí odkaz:
http://arxiv.org/abs/1905.12454
Machine learning models trained on confidential datasets are increasingly being deployed for profit. Machine Learning as a Service (MLaaS) has made such models easily accessible to end-users. Prior work has developed model extraction attacks, in whic
Externí odkaz:
http://arxiv.org/abs/1905.09165
In this work, we propose an automated method to identify semantic bugs in student programs, called ATAS, which builds upon the recent advances in both symbolic execution and active learning. Symbolic execution is a program analysis technique which ca
Externí odkaz:
http://arxiv.org/abs/1804.05655
Novice programmers often struggle with the formal syntax of programming languages. To assist them, we design a novel programming language correction framework amenable to reinforcement learning. The framework allows an agent to mimic human actions fo
Externí odkaz:
http://arxiv.org/abs/1801.10467
Stochastic multi-armed bandit (MAB) mechanisms are widely used in sponsored search auctions, crowdsourcing, online procurement, etc. Existing stochastic MAB mechanisms with a deterministic payment rule, proposed in the literature, necessarily suffer
Externí odkaz:
http://arxiv.org/abs/1703.00632
AUC (Area under the ROC curve) is an important performance measure for applications where the data is highly imbalanced. Learning to maximize AUC performance is thus an important research problem. Using a max-margin based surrogate loss function, AUC
Externí odkaz:
http://arxiv.org/abs/1612.08633