Search Results for author: Koulik Khamaru

Found 17 papers, 2 papers with code

Informativeness of Weighted Conformal Prediction

no code implementations10 May 2024 Mufang Ying, Wenge Guo, Koulik Khamaru, Ying Hung

Weighted conformal prediction (WCP), a recently proposed framework, provides uncertainty quantification with the flexibility to accommodate different covariate distributions between training and test data.

Conformal Prediction Informativeness +2

Statistical Limits of Adaptive Linear Models: Low-Dimensional Estimation and Inference

1 code implementation NeurIPS 2023 Licong Lin, Mufang Ying, Suvrojit Ghosh, Koulik Khamaru, Cun-Hui Zhang

Even in linear models, the Ordinary Least Squares (OLS) estimator may fail to exhibit asymptotic normality for single coordinate estimation and have inflated error.

Adaptive Linear Estimating Equations

1 code implementation NeurIPS 2023 Mufang Ying, Koulik Khamaru, Cun-Hui Zhang

Sequential data collection has emerged as a widely adopted technique for enhancing the efficiency of data gathering processes.

Multi-Armed Bandits

Semi-parametric inference based on adaptively collected data

no code implementations5 Mar 2023 Licong Lin, Koulik Khamaru, Martin J. Wainwright

Many standard estimators, when applied to adaptively collected data, fail to be asymptotically normal, thereby complicating the construction of confidence intervals.

Optimal variance-reduced stochastic approximation in Banach spaces

no code implementations21 Jan 2022 Wenlong Mou, Koulik Khamaru, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan

We study the problem of estimating the fixed point of a contractive operator defined on a separable Banach space.


Near-optimal inference in adaptive linear regression

no code implementations5 Jul 2021 Koulik Khamaru, Yash Deshpande, Tor Lattimore, Lester Mackey, Martin J. Wainwright

We propose a family of online debiasing estimators to correct these distributional anomalies in least squares estimation.

Active Learning regression +2

Instance-optimality in optimal value estimation: Adaptivity via variance-reduced Q-learning

no code implementations28 Jun 2021 Koulik Khamaru, Eric Xia, Martin J. Wainwright, Michael I. Jordan

Various algorithms in reinforcement learning exhibit dramatic variability in their convergence rates and ultimate accuracy as a function of the problem structure.


Instability, Computational Efficiency and Statistical Accuracy

no code implementations22 May 2020 Nhat Ho, Koulik Khamaru, Raaz Dwivedi, Martin J. Wainwright, Michael. I. Jordan, Bin Yu

Many statistical estimators are defined as the fixed point of a data-dependent operator, with estimators based on minimizing a cost function being an important special case.

Computational Efficiency

Is Temporal Difference Learning Optimal? An Instance-Dependent Analysis

no code implementations16 Mar 2020 Koulik Khamaru, Ashwin Pananjady, Feng Ruan, Martin J. Wainwright, Michael. I. Jordan

We address the problem of policy evaluation in discounted Markov decision processes, and provide instance-dependent guarantees on the $\ell_\infty$-error under a generative model.

Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models

no code implementations1 Feb 2019 Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Martin J. Wainwright, Michael. I. Jordan, Bin Yu

We study a class of weakly identifiable location-scale mixture models for which the maximum likelihood estimates based on $n$ i. i. d.

Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems

no code implementations20 Dec 2018 Dhruv Malik, Ashwin Pananjady, Kush Bhatia, Koulik Khamaru, Peter L. Bartlett, Martin J. Wainwright

We focus on characterizing the convergence rate of these methods when applied to linear-quadratic systems, and study various settings of driving noise and reward feedback.

Theoretical guarantees for EM under misspecified Gaussian mixture models

no code implementations NeurIPS 2018 Raaz Dwivedi, Nhật Hồ, Koulik Khamaru, Martin J. Wainwright, Michael. I. Jordan

We provide two classes of theoretical guarantees: first, we characterize the bias introduced due to the misspecification; and second, we prove that population EM converges at a geometric rate to the model projection under a suitable initialization condition.

Singularity, Misspecification, and the Convergence Rate of EM

no code implementations1 Oct 2018 Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Michael. I. Jordan, Martin J. Wainwright, Bin Yu

A line of recent work has analyzed the behavior of the Expectation-Maximization (EM) algorithm in the well-specified setting, in which the population likelihood is locally strongly concave around its maximizing argument.

Convergence guarantees for a class of non-convex and non-smooth optimization problems

no code implementations ICML 2018 Koulik Khamaru, Martin J. Wainwright

We also show that our algorithms can escape strict saddle points for a class of non-smooth functions, thereby generalizing known results for smooth functions.

Density Estimation

Computation of the Maximum Likelihood estimator in low-rank Factor Analysis

no code implementations18 Jan 2018 Koulik Khamaru, Rahul Mazumder

Factor analysis, a classical multivariate statistical technique is popularly used as a fundamental tool for dimensionality reduction in statistics, econometrics and data science.

Dimensionality Reduction Econometrics

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