no code implementations • 14 Nov 2023 • Reese Pathak, Rajat Sen, Weihao Kong, Abhimanyu Das
In this work, we investigate the hypothesis that transformers can learn an optimal predictor for mixtures of regressions.
no code implementations • 14 Oct 2023 • Abhimanyu Das, Weihao Kong, Rajat Sen, Yichen Zhou
Motivated by recent advances in large language models for Natural Language Processing (NLP), we design a time-series foundation model for forecasting whose out-of-the-box zero-shot performance on a variety of public datasets comes close to the accuracy of state-of-the-art supervised forecasting models for each individual dataset.
no code implementations • 5 Sep 2023 • Ayush Jain, Rajat Sen, Weihao Kong, Abhimanyu Das, Alon Orlitsky
A common approach assumes that the sources fall in one of several unknown subgroups, each with an unknown input distribution and input-output relationship.
2 code implementations • 17 Apr 2023 • Abhimanyu Das, Weihao Kong, Andrew Leach, Shaan Mathur, Rajat Sen, Rose Yu
Recent work has shown that simple linear models can outperform several Transformer based approaches in long term time-series forecasting.
Ranked #3 on Time Series Forecasting on ETTh2 (192) Multivariate
no code implementations • 23 Nov 2022 • Abhimanyu Das, Ayush Jain, Weihao Kong, Rajat Sen
We begin the study of list-decodable linear regression using batches.
no code implementations • 9 Jun 2022 • Pranjal Awasthi, Abhimanyu Das, Weihao Kong, Rajat Sen
We study the problem of learning generalized linear models under adversarial corruptions.
no code implementations • 21 Apr 2022 • Abhimanyu Das, Weihao Kong, Biswajit Paria, Rajat Sen
Probabilistic, hierarchically coherent forecasting is a key problem in many practical forecasting applications -- the goal is to obtain coherent probabilistic predictions for a large number of time series arranged in a pre-specified tree hierarchy.
no code implementations • 8 Mar 2022 • Ashok Cutkosky, Chris Dann, Abhimanyu Das, Qiuyi, Zhang
We study the setting of optimizing with bandit feedback with additional prior knowledge provided to the learner in the form of an initial hint of the optimal action.
no code implementations • NeurIPS 2021 • Pranjal Awasthi, Abhimanyu Das, Sreenivas Gollapudi
Graph Neural Networks~(GNNs) are a powerful class of architectures for solving learning problems on graphs.
no code implementations • ICLR 2022 • Pranjal Awasthi, Abhimanyu Das, Rajat Sen, Ananda Theertha Suresh
We also demonstrate empirically that our method instantiated with a well-designed general purpose mixture likelihood family can obtain superior performance for a variety of tasks across time-series forecasting and regression datasets with different data distributions.
no code implementations • 14 Jun 2021 • Biswajit Paria, Rajat Sen, Amr Ahmed, Abhimanyu Das
Hierarchical forecasting is a key problem in many practical multivariate forecasting applications - the goal is to simultaneously predict a large number of correlated time series that are arranged in a pre-specified aggregation hierarchy.
no code implementations • ICLR 2021 • Atish Agarwala, Abhimanyu Das, Brendan Juba, Rina Panigrahy, Vatsal Sharan, Xin Wang, Qiuyi Zhang
Can deep learning solve multiple tasks simultaneously, even when they are unrelated and very different?
no code implementations • 1 Jan 2021 • Pranjal Awasthi, Abhimanyu Das, Sreenivas Gollapudi
Finally, we empirically demonstrate the effectiveness of our proposed architecture for a variety of graph problems.
no code implementations • 24 Dec 2020 • Ashok Cutkosky, Abhimanyu Das, Manish Purohit
We provide a simple method to combine stochastic bandit algorithms.
no code implementations • 15 May 2020 • Atish Agarwala, Abhimanyu Das, Rina Panigrahy, Qiuyi Zhang
We present experimental evidence that the many-body gravitational force function is easier to learn with ReLU networks as compared to networks with exponential activations.
no code implementations • 8 Apr 2019 • Abhimanyu Das, Sreenivas Gollapudi, Ravi Kumar, Rina Panigrahy
In this paper we study the learnability of deep random networks from both theoretical and practical points of view.
no code implementations • NeurIPS 2012 • Abhimanyu Das, Anirban Dasgupta, Ravi Kumar
We compare our algorithms to traditional greedy and $\ell_1$-regularization schemes and show that we obtain a more diverse set of features that result in the regression problem being stable under perturbations.