no code implementations • 11 Nov 2024 • Kai Kim, Howard Tsai, Rajat Sen, Abhimanyu Das, ZiHao Zhou, Abhishek Tanpure, Mathew Luo, Rose Yu

Our data is a significant contribution to the rare selection of available multimodal datasets.

no code implementations • 31 Oct 2024 • Abhimanyu Das, Matthew Faw, Rajat Sen, Yichen Zhou

Our foundation model is specifically trained to utilize examples from multiple related time-series in its context window (in addition to the history of the target time-series) to help it adapt to the specific distribution of the target domain at inference time.

no code implementations • 28 Nov 2023 • Weihao Kong, Mingda Qiao, Rajat Sen

We study the problem of recovering Gaussian data under adversarial corruptions when the noises are low-rank and the corruptions are on the coordinate level.

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.

1 code implementation • 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.

Ranked #29 on Time Series Forecasting on ETTh1 (336) Multivariate (MAE metric)

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.

4 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 #4 on Time Series Forecasting on ETTh2 (96) 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 • 26 May 2022 • Avishek Ghosh, Arya Mazumdar, Soumyabrata Pal, Rajat Sen

In this paper we show that a version of the popular alternating minimization (AM) algorithm finds the best fit lines in a dataset even when a realizable model is not assumed, under some regularity conditions on the dataset and the initial points, and thereby provides a solution for the ERM.

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 • 26 Oct 2021 • Reese Pathak, Rajat Sen, Nikhil Rao, N. Benjamin Erichson, Michael I. Jordan, Inderjit S. Dhillon

Our framework -- which we refer to as "cluster-and-conquer" -- is highly general, allowing for any time-series forecasting and clustering method to be used in each step.

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.

1 code implementation • 15 Feb 2021 • Rajat Sen, Alexander Rakhlin, Lexing Ying, Rahul Kidambi, Dean Foster, Daniel Hill, Inderjit Dhillon

We show that our algorithm has a regret guarantee of $O(k\sqrt{(A-k+1)T \log (|\mathcal{F}|T)})$, where $A$ is the total number of arms and $\mathcal{F}$ is the class containing the regression function, while only requiring $\tilde{O}(A)$ computation per time step.

Computational Efficiency
Extreme Multi-Label Classification
**+2**

1 code implementation • 9 Dec 2020 • Nishant Yadav, Rajat Sen, Daniel N. Hill, Arya Mazumdar, Inderjit S. Dhillon

Previous queries in the user session can provide useful context for the user's intent and can be leveraged to suggest auto-completions that are more relevant while adhering to the user's prefix.

no code implementations • NeurIPS 2019 • Soumya Basu, Rajat Sen, Sujay Sanghavi, Sanjay Shakkottai

We show that with prior knowledge of the rewards and delays of all the arms, the problem of optimizing cumulative reward does not admit any pseudo-polynomial time algorithm (in the number of arms) unless randomized exponential time hypothesis is false, by mapping to the PINWHEEL scheduling problem.

1 code implementation • NeurIPS 2020 • Matthew Faw, Rajat Sen, Karthikeyan Shanmugam, Constantine Caramanis, Sanjay Shakkottai

We consider a covariate shift problem where one has access to several different training datasets for the same learning problem and a small validation set which possibly differs from all the individual training distributions.

1 code implementation • NeurIPS 2019 • Rajat Sen, Hsiang-Fu Yu, Inderjit Dhillon

Forecasting high-dimensional time series plays a crucial role in many applications such as demand forecasting and financial predictions.

1 code implementation • 24 Oct 2018 • Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai

We study the problem of black-box optimization of a noisy function in the presence of low-cost approximations or fidelities, which is motivated by problems like hyper-parameter tuning.

no code implementations • ICML 2018 • Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai

Motivated by settings such as hyper-parameter tuning and physical simulations, we consider the problem of black-box optimization of a function.

1 code implementation • 25 Jun 2018 • Rajat Sen, Karthikeyan Shanmugam, Himanshu Asnani, Arman Rahimzamani, Sreeram Kannan

Given independent samples generated from the joint distribution $p(\mathbf{x},\mathbf{y},\mathbf{z})$, we study the problem of Conditional Independence (CI-Testing), i. e., whether the joint equals the CI distribution $p^{CI}(\mathbf{x},\mathbf{y},\mathbf{z})= p(\mathbf{z}) p(\mathbf{y}|\mathbf{z})p(\mathbf{x}|\mathbf{z})$ or not.

no code implementations • 7 Jun 2018 • Maurice Diesendruck, Ethan R. Elenberg, Rajat Sen, Guy W. Cole, Sanjay Shakkottai, Sinead A. Williamson

Deep generative networks can simulate from a complex target distribution, by minimizing a loss with respect to samples from that distribution.

1 code implementation • 23 Feb 2018 • Rajat Sen, Karthikeyan Shanmugam, Nihal Sharma, Sanjay Shakkottai

We consider the problem of contextual bandits with stochastic experts, which is a variation of the traditional stochastic contextual bandit with experts problem.

1 code implementation • NeurIPS 2017 • Rajat Sen, Ananda Theertha Suresh, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai

We consider the problem of non-parametric Conditional Independence testing (CI testing) for continuous random variables.

no code implementations • ICML 2017 • Rajat Sen, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai

Motivated by applications in computational advertising and systems biology, we consider the problem of identifying the best out of several possible soft interventions at a source node $V$ in an acyclic causal directed graph, to maximize the expected value of a target node $Y$ (located downstream of $V$).

no code implementations • NeurIPS 2016 • Subhashini Krishnasamy, Rajat Sen, Ramesh Johari, Sanjay Shakkottai

A naive view of this problem would suggest that queue-regret should grow logarithmically: since queue-regret cannot be larger than classical regret, results for the standard MAB problem give algorithms that ensure queue-regret increases no more than logarithmically in time.

no code implementations • 1 Jun 2016 • Rajat Sen, Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G. Dimakis, Sanjay Shakkottai

Our algorithm achieves a regret of $\mathcal{O}\left(L\mathrm{poly}(m, \log K) \log T \right)$ at time $T$, as compared to $\mathcal{O}(LK\log T)$ for conventional contextual bandits, assuming a constant gap between the best arm and the rest for each context.

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