Babak Hassibi
24 papers · 2014–2024 · 6 conferences · across top CS/AI conferences
Achievements
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🏃 Academic Marathon (10) 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🌍 Conference Polyglot (6) 🐝 Cross-Pollinator (11)
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Keyword Pioneer
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Conference Polyglot
(6)
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Keyword Champion
(2)
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Unstoppable
(11)
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Century Club
(24)
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Trend Setter
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Keyword Collector
(114)
Conferences
NIPS (10)
L4DC (5)
COLT (3)
ICML (3)
AISTATS (2)
ICLR (1)
Top co-authors
Research topics
Keywords
linear dynamical system
(5)
convex optimization
(4)
adaptive control
(4)
regret bound
(4)
asymptotic analysis
(3)
signal recovery
(3)
system identification
(3)
graph clustering
(2)
compressed sensing
(2)
regret optimization
(2)
compressive sensing
(2)
logistic regression
(2)
sparse recovery
(2)
gaussian min-max theorem
(2)
phase retrieval
(2)
kalman filter
(2)
optimal control
(2)
sample complexity
(1)
stochastic gradient descent
(1)
wasserstein distance
(1)
Papers
Universality in Transfer Learning for Linear Models
NIPS 2024
Wasserstein distributionally robust regret-optimal control over infinite-horizon
L4DC 2024
Infinite-Horizon Distributionally Robust Regret-Optimal Control
ICML 2024
Precise Asymptotic Analysis of Deep Random Feature Models
COLT 2023
Thompson Sampling Achieves $\tilde{O}(\sqrt{T})$ Regret in Linear Quadratic Control
COLT 2022
Online Estimation and Control with Optimal Pathlength Regret
L4DC 2022
Reinforcement Learning with Fast Stabilization in Linear Dynamical Systems
AISTATS 2022
Finite-time System Identification and Adaptive Control in Autoregressive Exogenous Systems
L4DC 2021
Regret-Optimal Filtering
AISTATS 2021
Stability and Identification of Random Asynchronous Linear Time-Invariant Systems
L4DC 2021
Regret-optimal measurement-feedback control
L4DC 2021
The Performance Analysis of Generalized Margin Maximizers on Separable Data
ICML 2020
Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems
NIPS 2020
The Impact of Regularization on High-dimensional Logistic Regression
NIPS 2019
Universality in Learning from Linear Measurements
NIPS 2019
Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization
ICLR 2019
Low-Rank Riemannian Optimization on Positive Semidefinite Stochastic Matrices with Applications to Graph Clustering
ICML 2018
Learning without the Phase: Regularized PhaseMax Achieves Optimal Sample Complexity
NIPS 2018
A Universal Analysis of Large-Scale Regularized Least Squares Solutions
NIPS 2017
Crowdsourced Clustering: Querying Edges vs Triangles
NIPS 2016
Fundamental Limits of Budget-Fidelity Trade-off in Label Crowdsourcing
NIPS 2016
Regularized Linear Regression: A Precise Analysis of the Estimation Error
COLT 2015
LASSO with Non-linear Measurements is Equivalent to One With Linear Measurements
NIPS 2015
Graph Clustering With Missing Data: Convex Algorithms and Analysis
NIPS 2014