Arnab Bhattacharyya
29 papers · 2018–2025 · 9 conferences · across top CS/AI conferences
Achievements
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🏃 Academic Marathon (7) 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (9) 🐝 Cross-Pollinator (12)
🐝
Cross-Pollinator
(12)
🐣
Hot Topic Early Bird
🌍
Conference Polyglot
(9)
🏆
Keyword Champion
🏆
Grand Slam
🔬
Deep Specialist
(11)
🔥
Unstoppable
(8)
🚀
Conference Pioneer
💎
Century Club
(29)
⚡
Prolific Year
(5)
📈
Trend Setter
🗃️
Keyword Collector
(91)
Conferences
AISTATS (7)
ICML (6)
NIPS (5)
AAAI (4)
ALT (2)
CLEAR (2)
COLT (1)
ICLR (1)
IJCAI (1)
Top co-authors
Keywords
sample complexity
(7)
bayesian network
(5)
causal inference
(5)
graphical model
(4)
causal discovery
(3)
product distribution
(3)
total variation distance
(3)
interventional distribution
(3)
gaussian graphical model
(3)
causal effect
(3)
proper learning
(2)
property testing
(2)
causal model
(2)
structure learning
(2)
causal structure learning
(2)
kullback-leibler divergence
(2)
approximation algorithm
(2)
causal graph
(2)
high-dimensional inference
(1)
pac learning
(1)
Papers
Learning High-dimensional Gaussians from Censored Data
AISTATS 2025
Probably approximately correct high-dimensional causal effect estimation given a valid adjustment set
CLEAR 2025
Learning multivariate Gaussians with imperfect advice
ICML 2025
Learnability of Parameter-Bounded Bayes Nets
AAAI 2025
Approximating the Total Variation Distance between Gaussians
AISTATS 2025
Computational Explorations of Total Variation Distance
ICLR 2025
Optimal estimation of Gaussian (poly)trees
AISTATS 2024
Online bipartite matching with imperfect advice
ICML 2024
Total Variation Distance Meets Probabilistic Inference
ICML 2024
Learning bounded-degree polytrees with known skeleton
ALT 2024
On Approximating Total Variation Distance
IJCAI 2023
Constraint Optimization over Semirings
AAAI 2023
Sample Complexity of Distinguishing Cause from Effect
AISTATS 2023
On the Interventional Kullback-Leibler Divergence
CLEAR 2023
Active causal structure learning with advice
ICML 2023
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects
NIPS 2022
Independence Testing for Bounded Degree Bayesian Networks
NIPS 2022
Identifiability of Linear AMP Chain Graph Models
AAAI 2022
Efficient interventional distribution learning in the PAC framework
AISTATS 2022
Learning Sparse Fixed-Structure Gaussian Bayesian Networks
AISTATS 2022
Verification and search algorithms for causal DAGs
NIPS 2022
Efficient Statistics for Sparse Graphical Models from Truncated Samples
AISTATS 2021
Testing Product Distributions: A Closer Look
ALT 2021
Learning and Sampling of Atomic Interventions from Observations
ICML 2020
Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning
NIPS 2020
Minimum Intervention Cover of a Causal Graph
AAAI 2019
Learning and Testing Causal Models with Interventions
NIPS 2018
Testing Sparsity over Known and Unknown Bases
ICML 2018
Hardness of Learning Noisy Halfspaces using Polynomial Thresholds
COLT 2018