Aditya Bhaskara
28 papers · 2014–2025 · 7 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (7) π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (14) π§ Keyword Pioneer π Academic Marathon (11)
π
Interdisciplinary Bridge
π
Conference Polyglot
(7)
π
Academic Marathon
(11)
π§¬
Topic Evolution
π
Keyword Champion
(2)
ποΈ
Keyword Collector
(112)
β‘
Prolific Year
(5)
π
Century Club
(28)
π
Trend Setter
β
The Questioner
Conferences
NIPS (10)
ICML (7)
AISTATS (5)
COLT (3)
ALT (1)
ICLR (1)
JMLR (1)
Top co-authors
Keywords
regret bound
(6)
approximation algorithm
(5)
online linear optimization
(4)
online learning
(4)
k-means clustering
(4)
greedy algorithm
(3)
approximation guarantee
(2)
mixture model
(2)
linear optimization
(2)
online algorithm
(2)
logarithmic regret
(2)
matrix factorization
(2)
distributed algorithm
(2)
k-center clustering
(2)
community detection
(2)
distributed clustering
(2)
outlier detection
(2)
matrix approximation
(2)
stochastic block model
(2)
optimistic regret
(2)
Papers
Descent with Misaligned Gradients and Applications to Hidden Convexity
ICLR 2025
Convergence Guarantees for the DeepWalk Embedding on Block Models
ICML 2024
On the Robustness of Spectral Algorithms for Semirandom Stochastic Block Models
NIPS 2024
Structure of Nonlinear Node Embeddings in Stochastic Block Models
AISTATS 2023
Tight Bounds for Volumetric Spanners and Applications
NIPS 2023
Competing against Adaptive Strategies in Online Learning via Hints
AISTATS 2023
Bandit Online Linear Optimization with Hints and Queries
ICML 2023
Additive Error Guarantees for Weighted Low Rank Approximation
ICML 2021
Power of Hints for Online Learning with Movement Costs
AISTATS 2021
Principal Component Regression with Semirandom Observations via Matrix Completion
AISTATS 2021
Logarithmic Regret from Sublinear Hints
NIPS 2021
Robust Algorithms for Online $k$-means Clustering
ALT 2020
Online Learning with Imperfect Hints
ICML 2020
Adaptive Probing Policies for Shortest Path Routing
NIPS 2020
Online Linear Optimization with Many Hints
NIPS 2020
Online MAP Inference of Determinantal Point Processes
NIPS 2020
Approximate Guarantees for Dictionary Learning
COLT 2019
On Distributed Averaging for Stochastic k-PCA
NIPS 2019
Greedy Sampling for Approximate Clustering in the Presence of Outliers
NIPS 2019
On Binary Embedding using Circulant Matrices
JMLR 2018
Distributed Clustering via LSH Based Data Partitioning
ICML 2018
Linear Relaxations for Finding Diverse Elements in Metric Spaces
NIPS 2016
Greedy Column Subset Selection: New Bounds and Distributed Algorithms
ICML 2016
Sparse Solutions to Nonnegative Linear Systems and Applications
AISTATS 2015
Provable Bounds for Learning Some Deep Representations
ICML 2014
Open Problem: Tensor Decompositions: Algorithms up to the Uniqueness Threshold?
COLT 2014
Distributed Balanced Clustering via Mapping Coresets
NIPS 2014
Uniqueness of Tensor Decompositions with Applications to Polynomial Identifiability
COLT 2014