Zobrazeno 1 - 10
of 1 216
pro vyhledávání: '"BANERJEE, ARINDAM"'
Conservative Contextual Bandits (CCBs) address safety in sequential decision making by requiring that an agent's policy, along with minimizing regret, also satisfies a safety constraint: the performance is not worse than a baseline policy (e.g., the
Externí odkaz:
http://arxiv.org/abs/2412.06165
Combining gradient compression methods (e.g., CountSketch, quantization) and adaptive optimizers (e.g., Adam, AMSGrad) is a desirable goal in federated learning (FL), with potential benefits on both fewer communication rounds and less per-round commu
Externí odkaz:
http://arxiv.org/abs/2411.06770
Autor:
Chang, Yi-Chia, Stewart, Adam J., Bastani, Favyen, Wolters, Piper, Kannan, Shreya, Huber, George R., Wang, Jingtong, Banerjee, Arindam
Foundation models pre-trained using self-supervised and weakly-supervised learning have shown powerful transfer learning capabilities on various downstream tasks, including language understanding, text generation, and image recognition. Recently, the
Externí odkaz:
http://arxiv.org/abs/2409.09451
Weight normalization (WeightNorm) is widely used in practice for the training of deep neural networks and modern deep learning libraries have built-in implementations of it. In this paper, we provide the first theoretical characterizations of both op
Externí odkaz:
http://arxiv.org/abs/2409.08935
Autor:
Banerjee, Arindam, Basu, Saugata
Let $\mathrm{R}$ be a real closed field, $S \subset \mathrm{R}^n$ a closed and bounded semi-algebraic set and $\mathbf{f} = (f_1,\ldots,f_p):S \rightarrow \mathrm{R}^p$ a continuous semi-algebraic map. We study the poset module structure in homology
Externí odkaz:
http://arxiv.org/abs/2407.13586
Generalization and optimization guarantees on the population loss in machine learning often rely on uniform convergence based analysis, typically based on the Rademacher complexity of the predictors. The rich representation power of modern models has
Externí odkaz:
http://arxiv.org/abs/2406.07712
Recent works have shown a reduction from contextual bandits to online regression under a realizability assumption [Foster and Rakhlin, 2020, Foster and Krishnamurthy, 2021]. In this work, we investigate the use of neural networks for such online regr
Externí odkaz:
http://arxiv.org/abs/2312.07145
Generative models have gained popularity for their potential applications in imaging science, such as image reconstruction, posterior sampling and data sharing. Flow-based generative models are particularly attractive due to their ability to tractabl
Externí odkaz:
http://arxiv.org/abs/2309.04856