Search Results for author: Rajat Sen

Found 23 papers, 9 papers with code

Linear Regression using Heterogeneous Data Batches

no code implementations5 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.


Long-term Forecasting with TiDE: Time-series Dense Encoder

2 code implementations17 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.

Time Series Time Series Forecasting

Efficient List-Decodable Regression using Batches

no code implementations23 Nov 2022 Abhimanyu Das, Ayush Jain, Weihao Kong, Rajat Sen

We begin the study of list-decodable linear regression using batches.


On Learning Mixture of Linear Regressions in the Non-Realizable Setting

no code implementations26 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.

Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting

no code implementations21 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.

STS Time Series +1

Cluster-and-Conquer: A Framework For Time-Series Forecasting

no code implementations26 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.

Time Series Time Series Forecasting

On the benefits of maximum likelihood estimation for Regression and Forecasting

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.

regression Time Series +1

Hierarchically Regularized Deep Forecasting

no code implementations14 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.

Time Series Time Series Analysis

Top-$k$ eXtreme Contextual Bandits with Arm Hierarchy

1 code implementation15 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.

Extreme Multi-Label Classification Multi-Armed Bandits +1

Session-Aware Query Auto-completion using Extreme Multi-label Ranking

1 code implementation9 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.

Blocking Bandits

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.

Blocking Product Recommendation +1

Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions

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.

Noisy Blackbox Optimization with Multi-Fidelity Queries: A Tree Search Approach

1 code implementation24 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.

Multi-Fidelity Black-Box Optimization with Hierarchical Partitions

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.

Physical Simulations

Mimic and Classify : A meta-algorithm for Conditional Independence Testing

1 code implementation25 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.

Importance Weighted Generative Networks

no code implementations7 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.

Selection bias

Contextual Bandits with Stochastic Experts

1 code implementation23 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.

Multi-Armed Bandits

Identifying Best Interventions through Online Importance Sampling

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 $V$).

Regret of Queueing Bandits

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.

Contextual Bandits with Latent Confounders: An NMF Approach

no code implementations1 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.

Matrix Completion Multi-Armed Bandits

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