Search Results for author:
Found 26 papers, 9 papers with code
A Combinatorial Approach to Robust PCA
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.
Transformers can optimally learn regression mixture models
In this work, we investigate the hypothesis that transformers can learn an optimal predictor for mixtures of regressions.
A decoder-only foundation model for time-series forecasting
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.
Linear Regression using Heterogeneous Data Batches
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
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
Efficient List-Decodable Regression using Batches
We begin the study of list-decodable linear regression using batches.
Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models
We study the problem of learning generalized linear models under adversarial corruptions.
On Learning Mixture of Linear Regressions in the Non-Realizable Setting
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
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.
Cluster-and-Conquer: A Framework For Time-Series Forecasting
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.
On the benefits of maximum likelihood estimation for Regression and Forecasting
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.
Hierarchically Regularized Deep Forecasting
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.
Top-$k$ eXtreme Contextual Bandits with Arm Hierarchy
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
Session-Aware Query Auto-completion using Extreme Multi-label Ranking
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
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.
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions
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.
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 applications such as demand forecasting and financial predictions.
Noisy Blackbox Optimization with Multi-Fidelity Queries: A Tree Search Approach
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
Motivated by settings such as hyper-parameter tuning and physical simulations, we consider the problem of black-box optimization of a function.
Mimic and Classify : A meta-algorithm for Conditional Independence Testing
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
Deep generative networks can simulate from a complex target distribution, by minimizing a loss with respect to samples from that distribution.
Contextual Bandits with Stochastic Experts
We consider the problem of contextual bandits with stochastic experts, which is a variation of the traditional stochastic contextual bandit with experts problem.
Model-Powered Conditional Independence Test
We consider the problem of non-parametric Conditional Independence testing (CI testing) for continuous random variables.
Identifying Best Interventions through Online Importance Sampling
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
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
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.
