163 citations · 397 across the 26 of their papers we have counts for
31 papers
TabFM-Auto: Self-Evolving Pipelines for Tabular Foundation Models
Deqing Fu, Huangyuan Su, Rajat Sen +4
Tabular foundation models achieve strong zero-shot accuracy on structured data by pretraining on synthetic tables, but they ignore the column names, task descriptions, and auxiliar…
TabFM: A Zero-Shot Foundation Model for Tabular Data
Weihao Kong, Erez Louidor Ilan, Shuxin Nie +6
Tabular machine learning typically relies on per-dataset workflows, fitting tree ensembles or running AutoML searches from scratch for every task. We present TabFM, a 400M-paramete…
Multi-Modal Forecaster: Jointly Predicting Time Series and Textual Data
Kai Kim, Howard Tsai, Rajat Sen +5
Current forecasting approaches are largely unimodal and ignore the rich textual data that often accompany the time series due to lack of well-curated multimodal benchmark dataset.…
In-Context Fine-Tuning for Time-Series Foundation Models
Abhimanyu Das, Matthew Faw, Rajat Sen +1
Motivated by the recent success of time-series foundation models for zero-shot forecasting, we present a methodology for of a time-series foundati…
A Combinatorial Approach to Robust PCA
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. Concretely, we assume t…
Transformers can optimally learn regression mixture models
Reese Pathak, Rajat Sen, Weihao Kong +1
Mixture models arise in many regression problems, but most methods have seen limited adoption partly due to these algorithms' highly-tailored and model-specific nature. On the othe…
A decoder-only foundation model for time-series forecasting
Abhimanyu Das, Weihao Kong, Rajat Sen +1
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…
Linear Regression using Heterogeneous Data Batches
Ayush Jain, Rajat Sen, Weihao Kong +2
In many learning applications, data are collected from multiple sources, each providing a \emph{batch} of samples that by itself is insufficient to learn its input-output relations…
Long-term Forecasting with TiDE: Time-series Dense Encoder
Abhimanyu Das, Weihao Kong, Andrew Leach +3
Recent work has shown that simple linear models can outperform several Transformer based approaches in long term time-series forecasting. Motivated by this, we propose a Multi-laye…
Efficient List-Decodable Regression using Batches
Abhimanyu Das, Ayush Jain, Weihao Kong +1
We begin the study of list-decodable linear regression using batches. In this setting only an fraction of the batches are genuine. Each genuine batch contains …
Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models
Pranjal Awasthi, Abhimanyu Das, Weihao Kong +1
We study the problem of learning generalized linear models under adversarial corruptions. We analyze a classical heuristic called the iterative trimmed maximum likelihood estimator…
On Learning Mixture of Linear Regressions in the Non-Realizable Setting
Avishek Ghosh, Arya Mazumdar, Soumyabrata Pal +1
While mixture of linear regressions (MLR) is a well-studied topic, prior works usually do not analyze such models for prediction error. In fact, {\em prediction} and {\em loss} are…
Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting
Abhimanyu Das, Weihao Kong, Biswajit Paria +1
Probabilistic, hierarchically coherent forecasting is a key problem in many practical forecasting applications -- the goal is to obtain coherent probabilistic predictions for a lar…
Cluster-and-Conquer: A Framework For Time-Series Forecasting
Reese Pathak, Rajat Sen, Nikhil Rao +3
We propose a three-stage framework for forecasting high-dimensional time-series data. Our method first estimates parameters for each univariate time series. Next, we use these para…
Bandits with Stochastic Experts: Constant Regret, Empirical Experts and Episodes
Nihal Sharma, Rajat Sen, Soumya Basu +2
We study a variant of the contextual bandit problem where an agent can intervene through a set of stochastic expert policies. Given a fixed context, each expert samples actions fro…
On the benefits of maximum likelihood estimation for Regression and Forecasting
Pranjal Awasthi, Abhimanyu Das, Rajat Sen +1
We advocate for a practical Maximum Likelihood Estimation (MLE) approach towards designing loss functions for regression and forecasting, as an alternative to the typical approach…
Hierarchically Regularized Deep Forecasting
Biswajit Paria, Rajat Sen, Amr Ahmed +1
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 t…
Top- eXtreme Contextual Bandits with Arm Hierarchy
Rajat Sen, Alexander Rakhlin, Lexing Ying +4
Motivated by modern applications, such as online advertisement and recommender systems, we study the top- extreme contextual bandits problem, where the total number of arms can…
Session-Aware Query Auto-completion using Extreme Multi-label Ranking
Nishant Yadav, Rajat Sen, Daniel N. Hill +2
Query auto-completion (QAC) is a fundamental feature in search engines where the task is to suggest plausible completions of a prefix typed in the search bar. Previous queries in t…
Blocking Bandits
Soumya Basu, Rajat Sen, Sujay Sanghavi +1
We consider a novel stochastic multi-armed bandit setting, where playing an arm makes it unavailable for a fixed number of time slots thereafter. This models situations where reusi…
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions
Matthew Faw, Rajat Sen, Karthikeyan Shanmugam +2
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 fr…
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting
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. Modern datasets can have millions of correl…
Noisy Blackbox Optimization with Multi-Fidelity Queries: A Tree Search Approach
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 tun…
Mimic and Classify : A meta-algorithm for Conditional Independence Testing
Rajat Sen, Karthikeyan Shanmugam, Himanshu Asnani +2
Given independent samples generated from the joint distribution , we study the problem of Conditional Independence (CI-Testing), i.e., whether…