Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Mukherjee, Debarghya"'
Solving partial differential equations (PDEs) and their inverse problems using Physics-informed neural networks (PINNs) is a rapidly growing approach in the physics and machine learning community. Although several architectures exist for PINNs that w
Externí odkaz:
http://arxiv.org/abs/2406.14808
Autor:
Chen, Jiachen, Huang, Danyang, Wang, Liyuan, Lunetta, Kathryn L., Mukherjee, Debarghya, Cheng, Huimin
Node classification is a fundamental task, but obtaining node classification labels can be challenging and expensive in many real-world scenarios. Transfer learning has emerged as a promising solution to address this challenge by leveraging knowledge
Externí odkaz:
http://arxiv.org/abs/2405.16672
As machine learning models are increasingly deployed in dynamic environments, it becomes paramount to assess and quantify uncertainties associated with distribution shifts. A distribution shift occurs when the underlying data-generating process chang
Externí odkaz:
http://arxiv.org/abs/2405.10302
Autor:
Durot, Cecile, Mukherjee, Debarghya
Shuffled regression and unlinked regression represent intriguing challenges that have garnered considerable attention in many fields, including but not limited to ecological regression, multi-target tracking problems, image denoising, etc. However, a
Externí odkaz:
http://arxiv.org/abs/2404.09306
Uncertainty quantification in prediction presents a compelling challenge with vast applications across various domains, including biomedical science, economics, and weather forecasting. There exists a wide array of methods for constructing prediction
Externí odkaz:
http://arxiv.org/abs/2306.16549
Deep neural networks have achieved tremendous success due to their representation power and adaptation to low-dimensional structures. Their potential for estimating structured regression functions has been recently established in the literature. Howe
Externí odkaz:
http://arxiv.org/abs/2302.05851
Time-varying stochastic optimization problems frequently arise in machine learning practice (e.g. gradual domain shift, object tracking, strategic classification). Although most problems are solved in discrete time, the underlying process is often co
Externí odkaz:
http://arxiv.org/abs/2205.13575
Many instances of algorithmic bias are caused by distributional shifts. For example, machine learning (ML) models often perform worse on demographic groups that are underrepresented in the training data. In this paper, we leverage this connection bet
Externí odkaz:
http://arxiv.org/abs/2205.00504
Post-processing in algorithmic fairness is a versatile approach for correcting bias in ML systems that are already used in production. The main appeal of post-processing is that it avoids expensive retraining. In this work, we propose general post-pr
Externí odkaz:
http://arxiv.org/abs/2110.13796
This paper presents a number of new findings about the canonical change point estimation problem. The first part studies the estimation of a change point on the real line in a simple stump model using the robust Huber estimating function which interp
Externí odkaz:
http://arxiv.org/abs/2105.11591