17 citations · 30 across the 23 of their papers we have counts for
29 papers
Executing Causal Structure Learning with Linear-Attention Transformers
Amartya Roy, Sayar Karmakar
Transformers can execute algorithms on data given in their input. We ask whether they can do the same for causal discovery. We study a standard continuous method that repeatedly up…
A Characterization of the -tests Under Nearly Integrated Nearly White Noise
Aidan Wardak, Sayar Karmakar
We derive the limiting distributions of the -test family of unit root statistics in the nearly integrated nearly white noise (NINW) framework introduced by Nabeya and Perron (19…
Joint Estimation in Potts Model
Somabha Mukherjee, Sumit Mukherjee, Sayar Karmakar
In this paper, we study estimation of parameters in a two-parameter Potts model with colors and coupling matrix . We characterize concrete sufficient conditions for existe…
Nonparametric regression of spatio-temporal data using infinite-dimensional covariates
Subhrajyoty Roy, Soudeep Deb, Sayar Karmakar +1
In spatio-temporal analysis, we often record data at specific time intervals but with varying spatial locations between these timepoints. We propose a conditional model to analyze…
Fast localization of anomalous patches in spatial data under dependence
Soham Bonnerjee, Sayar Karmakar, George Michailidis
We propose a scalable, provably accurate method for localizing an unknown number of multiple axis-aligned anomalous patches in spatial data under a general class of spatial depende…
The Game of Graph Nim on graphs with four edges
Sayar Karmakar, Moumanti Podder, Souvik Roy +1
This work is concerned with the study of the Game of Graph Nim -- a class of two-player combinatorial games -- on graphs with edges. To each edge of such a graph is assigned a…
Fast segmentation of watermarked texts from large language models through an epidemic change-point framework
Soham Bonnerjee, Subhrajyoty Roy, Sayar Karmakar
With the growing use of large language models, concerns over content authenticity have spurred a variety of watermarking schemes. These schemes use secret keys to detect machine-ge…
Sharp Gaussian approximations for Decentralized Federated Learning
Soham Bonnerjee, Sayar Karmakar, Wei Biao Wu
Federated Learning has gained traction in privacy-sensitive collaborative environments, with local SGD emerging as a key optimization method in decentralized settings. While its co…
Gaussian Approximation For Non-stationary Time Series with Optimal Rate and Explicit Construction
Soham Bonnerjee, Sayar Karmakar, Wei Biao Wu
Statistical inference for time series such as curve estimation for time-varying models or testing for existence of change-point have garnered significant attention. However, these…
Percolation games on rooted, edge-weighted random trees
Sayar Karmakar, Moumanti Podder, Souvik Roy +1
Consider a rooted Galton-Watson tree , to each of whose edges we assign, independently, a weight that equals with probability , with probability and …
Generalized percolation games on the -dimensional square lattice, and ergodicity of associated probabilistic cellular automata
Dhruv Bhasin, Sayar Karmakar, Moumanti Podder +1
Each vertex of the infinite -dimensional square lattice graph is assigned, independently, a label that reads trap with probability , target with probability , and open wit…
The spread of an epidemic: a game-theoretic approach
Sayar Karmakar, Moumanti Podder, Souvik Roy +1
We introduce and study a model stemming from game theory for the spread of an epidemic throughout a given population. Each agent is allowed to choose an action whose value dictates…
Phase transition in percolation games on rooted Galton-Watson trees
Sayar Karmakar, Moumanti Podder, Souvik Roy +1
We study the bond percolation game and the site percolation game on the rooted Galton-Watson tree with offspring distribution . We obtain the probabilities of win, loss an…
Ergodicity of a generalized probabilistic cellular automaton with parity-based neighbourhoods
Dhruv Bhasin, Sayar Karmakar, Moumanti Podder +1
We study a one-dimensional generalized probabilistic cellular automaton with universe , alphabet , parameters and such that $0…
On a class of probabilistic cellular automata with size- neighbourhood and their applications in percolation games
Dhruv Bhasin, Sayar Karmakar, Moumanti Podder +1
Different versions of percolation games on , with parameters and that indicate, respectively, the probability with which a site in is label…
Towards Size-Independent Generalization Bounds for Deep Operator Nets
Pulkit Gopalani, Sayar Karmakar, Dibyakanti Kumar +1
In recent times machine learning methods have made significant advances in becoming a useful tool for analyzing physical systems. A particularly active area in this theme has been…
An Empirical Study of the Occurrence of Heavy-Tails in Training a ReLU Gate
Sayar Karmakar, Anirbit Mukherjee
A particular direction of recent advance about stochastic deep-learning algorithms has been about uncovering a rather mysterious heavy-tailed nature of the stationary distribution…
Long-term prediction intervals with many covariates
Sayar Karmakar, Marek Chudy, Wei Biao Wu
Accurate forecasting is one of the fundamental focus in the literature of econometric time-series. Often practitioners and policy makers want to predict outcomes of an entire time…
Simultaneous inference for time-varying models
Sayar Karmakar, Stefan Richter, Wei Biao Wu
A general class of time-varying regression models is considered in this paper. We estimate the regression coefficients by using local linear M-estimation. For these estimators, wea…
Time-varying auto-regressive models for count time-series
Arkaprava Roy, Sayar Karmakar
Count-valued time series data are routinely collected in many application areas. We are particularly motivated to study the count time series of daily new cases, arising from COVID…
Bayesian modelling of time-varying conditional heteroscedasticity
Sayar Karmakar, Arkaprava Roy
Conditional heteroscedastic (CH) models are routinely used to analyze financial datasets. The classical models such as ARCH-GARCH with time-invariant coefficients are often inadequ…
Evaluating the Impact of COVID-19 on Cyberbullying through Bayesian Trend Analysis
Sayar Karmakar, Sanchari Das
COVID-19's impact has surpassed from personal and global health to our social life. In terms of digital presence, it is speculated that during pandemic, there has been a significan…
Change-Point Analysis of Cyberbullying-Related Twitter Discussions During COVID-19
Sanchari Das, Andrew Kim, Sayar Karmakar
Due to the outbreak of COVID-19, users are increasingly turning to online services. An increase in social media usage has also been observed, leading to the suspicion that this has…
Provable Training of a ReLU Gate with an Iterative Non-Gradient Algorithm
Sayar Karmakar, Anirbit Mukherjee
In this work, we demonstrate provable guarantees on the training of a single ReLU gate in hitherto unexplored regimes. We give a simple iterative stochastic algorithm that can trai…