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04/17/2023
Long-term Forecasting with TiDE: Time-series Dense Encoder
Recent work has shown that simple linear models can outperform several T...
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06/09/2022
Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models
We study the problem of learning generalized linear models under adversa...
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05/26/2022
On Learning Mixture of Linear Regressions in the Non-Realizable Setting
While mixture of linear regressions (MLR) is a well-studied topic, prior...
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04/21/2022
A Top-Down Approach to Hierarchically Coherent Probabilistic Forecasting
Hierarchical forecasting is a key problem in many practical multivariate...
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06/18/2021
On the benefits of maximum likelihood estimation for Regression and Forecasting
We advocate for a practical Maximum Likelihood Estimation (MLE) approach...
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06/14/2021
Hierarchically Regularized Deep Forecasting
Hierarchical forecasting is a key problem in many practical multivariate...
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02/15/2021
Top-k eXtreme Contextual Bandits with Arm Hierarchy
Motivated by modern applications, such as online advertisement and recom...
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12/09/2020
Session-Aware Query Auto-completion using Extreme Multi-label Ranking
Query auto-completion is a fundamental feature in search engines where t...
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07/27/2019
Blocking Bandits
We consider a novel stochastic multi-armed bandit setting, where playing...
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07/23/2019
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions
We consider a co-variate shift problem where one has access to several m...
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05/09/2019
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting
Forecasting high-dimensional time series plays a crucial role in many ap...
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10/24/2018
Noisy Blackbox Optimization with Multi-Fidelity Queries: A Tree Search Approach
We study the problem of black-box optimization of a noisy function in th...
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06/25/2018
Mimic and Classify : A meta-algorithm for Conditional Independence Testing
Given independent samples generated from the joint distribution p(x,y,z)...
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06/07/2018
Importance weighted generative networks
Deep generative networks can simulate from a complex target distribution...
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02/23/2018
Contextual Bandits with Stochastic Experts
We consider the problem of contextual bandits with stochastic experts, w...
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09/18/2017
Model-Powered Conditional Independence Test
We consider the problem of non-parametric Conditional Independence testi...
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01/10/2017
Identifying Best Interventions through Online Importance Sampling
Motivated by applications in computational advertising and systems biolo...
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06/01/2016
