Arnab Bhattacharyya
29 papers · 2018–2025 · 9 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
Jump to papers ↓+8 more ↓ Show less ↑
πΊοΈ Taxonomy Completionist (35) π Conference Polyglot Β· Silver π Academic Marathon Β· Sprint π Interdisciplinary Bridge Β· Gold π£ Hot Topic Early Bird
π
Keyword Champion
(2)
π
Grand Slam
π¬
Deep Specialist
(11)
π
Conference Pioneer
π₯
Unstoppable Β· Bronze
β‘
Prolific Year Β· Productive
π
Century Club Β· Bronze
ποΈ
Keyword Collector
(115)
Top co-authors
Research topics
Machine Learning
(24)
Optimization & Theory
(20)
Artificial Intelligence
(16)
Mathematics & Optimization
(14)
Core AI
(12)
Mathematics
(10)
Bayesian & Probabilistic Β· Machine Learning
(8)
Core Methods
(8)
Optimization
(8)
Learning Types
(5)
Knowledge & Reasoning
(4)
Bayesian & Probabilistic Β· Artificial Intelligence
(4)
Reasoning
(4)
Computer Science
(3)
Foundations
(3)
Keywords
sample complexity
(9)
total variation distance
(6)
causal inference
(5)
bayesian network
(5)
product distribution
(4)
graphical model
(4)
causal discovery
(3)
interventional distribution
(3)
gaussian graphical model
(3)
causal effect
(3)
approximation algorithm
(3)
proper learning
(2)
property testing
(2)
causal model
(2)
causal graph
(2)
multivariate gaussian
(2)
causal structure learning
(2)
distribution learning
(2)
structure learning
(2)
ising model
(2)
Papers
Approximating the Total Variation Distance between Gaussians
AISTATS 2025
Computational Explorations of Total Variation Distance
ICLR 2025
Learnability of Parameter-Bounded Bayes Nets
AAAI 2025
Learning High-dimensional Gaussians from Censored Data
AISTATS 2025
Learning multivariate Gaussians with imperfect advice
ICML 2025
Probably approximately correct high-dimensional causal effect estimation given a valid adjustment set
CLeaR 2025
Learning bounded-degree polytrees with known skeleton
ALT 2024
Online bipartite matching with imperfect advice
ICML 2024
Optimal estimation of Gaussian (poly)trees
AISTATS 2024
Total Variation Distance Meets Probabilistic Inference
ICML 2024
Active causal structure learning with advice
ICML 2023
Constraint Optimization over Semirings
AAAI 2023
On Approximating Total Variation Distance
IJCAI 2023
On the Interventional Kullback-Leibler Divergence
CLeaR 2023
Sample Complexity of Distinguishing Cause from Effect
AISTATS 2023
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects
NeurIPS 2022
Efficient interventional distribution learning in the PAC framework
AISTATS 2022
Identifiability of Linear AMP Chain Graph Models
AAAI 2022
Independence Testing for Bounded Degree Bayesian Networks
NeurIPS 2022
Learning Sparse Fixed-Structure Gaussian Bayesian Networks
AISTATS 2022
Verification and search algorithms for causal DAGs
NeurIPS 2022
Efficient Statistics for Sparse Graphical Models from Truncated Samples
AISTATS 2021
Testing Product Distributions: A Closer Look
ALT 2021
Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning
NeurIPS 2020
Learning and Sampling of Atomic Interventions from Observations
ICML 2020
Minimum Intervention Cover of a Causal Graph
AAAI 2019
Hardness of Learning Noisy Halfspaces using Polynomial Thresholds
COLT 2018
Learning and Testing Causal Models with Interventions
NeurIPS 2018
Testing Sparsity over Known and Unknown Bases
ICML 2018