Search Results for author: Taposh Banerjee

Found 8 papers, 1 papers with code

Large Deviation Analysis of Score-based Hypothesis Testing

no code implementations27 Jan 2024 Enmao Diao, Taposh Banerjee, Vahid Tarokh

We analyze the performance of this score-based hypothesis testing procedure and derive upper bounds on the probabilities of its Type I and II errors.

Reinforcement Learning with an Abrupt Model Change

no code implementations22 Apr 2023 Wuxia Chen, Taposh Banerjee, Jemin George, Carl Busart

The proposed algorithm exploits a fundamental reward-detection trade-off present in these problems and uses a quickest change detection algorithm to detect the model change.

Change Detection reinforcement-learning

Modeling and Quickest Detection of a Rapidly Approaching Object

no code implementations4 Mar 2023 Tim Brucks, Taposh Banerjee, Rahul Mishra

This change in distribution could be due to an event or a sudden arrival of an enemy object.

Quickest Change Detection for Unnormalized Statistical Models

no code implementations1 Feb 2023 Suya Wu, Enmao Diao, Taposh Banerjee, Jie Ding, Vahid Tarokh

This paper develops a new variant of the classical Cumulative Sum (CUSUM) algorithm for the quickest change detection.

Change Detection

Cross-subject Decoding of Eye Movement Goals from Local Field Potentials

no code implementations8 Nov 2019 Marko Angjelichinoski, John Choi, Taposh Banerjee, Bijan Pesaran, Vahid Tarokh

We propose an efficient data-driven estimation approach for linear transfer functions that uses the first and second order moments of the class-conditional distributions.

Transfer Learning

Minimax-optimal decoding of movement goals from local field potentials using complex spectral features

no code implementations29 Jan 2019 Marko Angjelichinoski, Taposh Banerjee, John Choi, Bijan Pesaran, Vahid Tarokh

We consider the problem of predicting eye movement goals from local field potentials (LFP) recorded through a multielectrode array in the macaque prefrontal cortex.

Kiefer Wolfowitz Algorithm is Asymptotically Optimal for a Class of Non-Stationary Bandit Problems

no code implementations26 Feb 2017 Rahul Singh, Taposh Banerjee

We consider the problem of designing an allocation rule or an "online learning algorithm" for a class of bandit problems in which the set of control actions available at each time $s$ is a convex, compact subset of $\mathbb{R}^d$.

Quickest Change Detection

1 code implementation19 Oct 2012 Venugopal V. Veeravalli, Taposh Banerjee

The problem of detecting changes in the statistical properties of a stochastic system and time series arises in various branches of science and engineering.

Statistics Theory Information Theory Information Theory Optimization and Control Probability Applications Statistics Theory

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