Krzysztof Choromanski
28 papers · 2016–2026 · 9 venues · across top CS/AI venues
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Research topics
Machine Learning
(23)
Optimization & Theory
(14)
Reinforcement Learning
(12)
Deep Learning
(10)
Mathematics & Optimization
(10)
Architectures
(9)
Core Methods
(9)
Methods
(8)
Optimization
(8)
Artificial Intelligence
(6)
Applications
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Learning Types
(4)
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(4)
Keywords
reinforcement learning
(5)
kernel approximation
(4)
evolution strategy
(4)
policy gradient
(3)
transformer architecture
(3)
evolution strategies
(3)
black-box optimization
(3)
policy optimization
(3)
random projection
(2)
neural network compression
(2)
wasserstein distance
(2)
variance reduction
(2)
stochastic gradient descent
(2)
derivative-free optimization
(2)
monte carlo sampling
(2)
continuous control
(2)
matrix factorization
(2)
gradient estimation
(2)
model-based reinforcement learning
(2)
kernel ridge regression
(2)
Papers
TIPSv2: Advancing Vision-Language Pretraining with Enhanced Patch-Text Alignment
CVPR 2026
Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers
NeurIPS 2024
Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers
AISTATS 2024
Structured Unrestricted-Rank Matrices for Parameter Efficient Finetuning
NeurIPS 2024
On the Expressive Flexibility of Self-Attention Matrices
AAAI 2023
Robotic Table Tennis: A Case Study into a High Speed Learning System
RSS 2023
From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers
ICML 2022
Catformer: Designing Stable Transformers via Sensitivity Analysis
ICML 2021
CWY Parametrization: a Solution for Parallelized Optimization of Orthogonal and Stiefel Matrices
AISTATS 2021
Debiasing a First-order Heuristic for Approximate Bi-level Optimization
ICML 2021
Towards tractable optimism in model-based reinforcement learning
UAI 2021
ES-MAML: Simple Hessian-Free Meta Learning
ICLR 2020
Learning to Score Behaviors for Guided Policy Optimization
ICML 2020
Practical Nonisotropic Monte Carlo Sampling in High Dimensions via Determinantal Point Processes
AISTATS 2020
Ready Policy One: World Building Through Active Learning
ICML 2020
Stochastic Flows and Geometric Optimization on the Orthogonal Group
ICML 2020
Variance Reduction for Evolution Strategies via Structured Control Variates
AISTATS 2020
KAMA-NNs: Low-dimensional Rotation Based Neural Networks
AISTATS 2019
Orthogonal Estimation of Wasserstein Distances
AISTATS 2019
Provably Robust Blackbox Optimization for Reinforcement Learning
CoRL 2019
Unifying Orthogonal Monte Carlo Methods
ICML 2019
Initialization matters: Orthogonal Predictive State Recurrent Neural Networks
ICLR 2018
Structured Evolution with Compact Architectures for Scalable Policy Optimization
ICML 2018
The Geometry of Random Features
AISTATS 2018
Structured adaptive and random spinners for fast machine learning computations
AISTATS 2017
Binary embeddings with structured hashed projections
ICML 2016
Quantization based Fast Inner Product Search
AISTATS 2016
Recycling Randomness with Structure for Sublinear time Kernel Expansions
ICML 2016