Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Sood, Srijan"'
Autor:
Kawawa-Beaudan, Maxime, Sood, Srijan, Palande, Soham, Mani, Ganapathy, Balch, Tucker, Veloso, Manuela
We present a lightweight approach to sequence classification using Ensemble Methods for Hidden Markov Models (HMMs). HMMs offer significant advantages in scenarios with imbalanced or smaller datasets due to their simplicity, interpretability, and eff
Externí odkaz:
http://arxiv.org/abs/2409.07619
Autor:
Potluru, Vamsi K., Borrajo, Daniel, Coletta, Andrea, Dalmasso, Niccolò, El-Laham, Yousef, Fons, Elizabeth, Ghassemi, Mohsen, Gopalakrishnan, Sriram, Gosai, Vikesh, Kreačić, Eleonora, Mani, Ganapathy, Obitayo, Saheed, Paramanand, Deepak, Raman, Natraj, Solonin, Mikhail, Sood, Srijan, Vyetrenko, Svitlana, Zhu, Haibei, Veloso, Manuela, Balch, Tucker
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality. We present a broad overview of prototypical applications of synthetic data in the financial sector and in particular provide
Externí odkaz:
http://arxiv.org/abs/2401.00081
In this work, we address time-series forecasting as a computer vision task. We capture input data as an image and train a model to produce the subsequent image. This approach results in predicting distributions as opposed to pointwise values. To asse
Externí odkaz:
http://arxiv.org/abs/2107.01273
Time series forecasting is essential for agents to make decisions. Traditional approaches rely on statistical methods to forecast given past numeric values. In practice, end-users often rely on visualizations such as charts and plots to reason about
Externí odkaz:
http://arxiv.org/abs/2011.09052
Autor:
Agrawal, Ashrya, Pfisterer, Florian, Bischl, Bernd, Buet-Golfouse, Francois, Sood, Srijan, Chen, Jiahao, Shah, Sameena, Vollmer, Sebastian
We present an empirical study of debiasing methods for classifiers, showing that debiasers often fail in practice to generalize out-of-sample, and can in fact make fairness worse rather than better. A rigorous evaluation of the debiasing treatment ef
Externí odkaz:
http://arxiv.org/abs/2011.02407
In reinforcement learning, we often define goals by specifying rewards within desirable states. One problem with this approach is that we typically need to redefine the rewards each time the goal changes, which often requires some understanding of th
Externí odkaz:
http://arxiv.org/abs/1705.09045