Tamas Sarlos
20 papers · 2013–2024 · 6 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π Renaissance Researcher (5) π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (11) π£ Hot Topic Early Bird
π§
Keyword Pioneer
π£
Hot Topic Early Bird
π
Academic Marathon
(11)
π€
Dynamic Duo
(10)
π
Grand Slam
π
Keyword Champion
(2)
π₯
Unstoppable
(8)
π
Conference Pioneer
π
Trend Setter
ποΈ
Keyword Collector
(100)
π
Century Club
(20)
Conferences
ICML (6)
NIPS (6)
AISTATS (5)
AAAI (1)
COLT (1)
ICLR (1)
Top co-authors
Keywords
kernel approximation
(5)
dimensionality reduction
(4)
transformer architecture
(3)
spectral analysis
(2)
tensor sketching
(2)
random projection
(2)
structured matrice
(2)
randomized algorithm
(2)
random feature
(2)
graph theory
(2)
differential privacy
(2)
wasserstein distance
(2)
reinforcement learning
(2)
gaussian kernel
(2)
heavy hitter
(2)
softmax kernel
(2)
point cloud
(1)
language modeling
(1)
attention mechanism
(1)
optimal transport
(1)
Papers
Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries
COLT 2024
Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers
NIPS 2024
Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers
AISTATS 2024
Tricking the Hashing Trick: A Tight Lower Bound on the Robustness of CountSketch to Adaptive Inputs
AAAI 2023
Hardness of Low Rank Approximation of Entrywise Transformed Matrix Products
NIPS 2023
Dense-Exponential Random Features: Sharp Positive Estimators of the Gaussian Kernel
NIPS 2023
Efficient Graph Field Integrators Meet Point Clouds
ICML 2023
On the Robustness of CountSketch to Adaptive Inputs
ICML 2022
From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers
ICML 2022
Chefs' Random Tables: Non-Trigonometric Random Features
NIPS 2022
Differentially Private Weighted Sampling
AISTATS 2021
Rethinking Attention with Performers
ICLR 2021
Stochastic Flows and Geometric Optimization on the Orthogonal Group
ICML 2020
Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels
NIPS 2019
Matrix-Free Preconditioning in Online Learning
ICML 2019
Orthogonal Estimation of Wasserstein Distances
AISTATS 2019
The Geometry of Random Features
AISTATS 2018
Geometrically Coupled Monte Carlo Sampling
NIPS 2018
Structured adaptive and random spinners for fast machine learning computations
AISTATS 2017
Fastfood - Computing Hilbert Space Expansions in loglinear time
ICML 2013