no code implementations • 4 Dec 2023 • Isabel Leal, Krzysztof Choromanski, Deepali Jain, Avinava Dubey, Jake Varley, Michael Ryoo, Yao Lu, Frederick Liu, Vikas Sindhwani, Quan Vuong, Tamas Sarlos, Ken Oslund, Karol Hausman, Kanishka Rao
We present Self-Adaptive Robust Attention for Robotics Transformers (SARA-RT): a new paradigm for addressing the emerging challenge of scaling up Robotics Transformers (RT) for on-robot deployment.
1 code implementation • 20 Oct 2023 • Arijit Sehanobish, Krzysztof Choromanski, Yunfan Zhao, Avinava Dubey, Valerii Likhosherstov
We introduce the concept of scalable neural network kernels (SNNKs), the replacements of regular feedforward layers (FFLs), capable of approximating the latter, but with favorable computational properties.
no code implementations • 7 Oct 2023 • Isaac Reid, Eli Berger, Krzysztof Choromanski, Adrian Weller
We present a novel quasi-Monte Carlo mechanism to improve graph-based sampling, coined repelling random walks.
no code implementations • 7 Oct 2023 • Isaac Reid, Krzysztof Choromanski, Eli Berger, Adrian Weller
This includes many of the most popular examples of kernels defined on the nodes of a graph.
no code implementations • 15 Sep 2023 • Haozhe Shan, Albert Gu, Zhong Meng, Weiran Wang, Krzysztof Choromanski, Tara Sainath
Online speech recognition, where the model only accesses context to the left, is an important and challenging use case for ASR systems.
no code implementations • 6 Sep 2023 • David B. D'Ambrosio, Jonathan Abelian, Saminda Abeyruwan, Michael Ahn, Alex Bewley, Justin Boyd, Krzysztof Choromanski, Omar Cortes, Erwin Coumans, Tianli Ding, Wenbo Gao, Laura Graesser, Atil Iscen, Navdeep Jaitly, Deepali Jain, Juhana Kangaspunta, Satoshi Kataoka, Gus Kouretas, Yuheng Kuang, Nevena Lazic, Corey Lynch, Reza Mahjourian, Sherry Q. Moore, Thinh Nguyen, Ken Oslund, Barney J Reed, Krista Reymann, Pannag R. Sanketi, Anish Shankar, Pierre Sermanet, Vikas Sindhwani, Avi Singh, Vincent Vanhoucke, Grace Vesom, Peng Xu
We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets.
1 code implementation • 28 Jul 2023 • Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, Pete Florence, Chuyuan Fu, Montse Gonzalez Arenas, Keerthana Gopalakrishnan, Kehang Han, Karol Hausman, Alexander Herzog, Jasmine Hsu, Brian Ichter, Alex Irpan, Nikhil Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Isabel Leal, Lisa Lee, Tsang-Wei Edward Lee, Sergey Levine, Yao Lu, Henryk Michalewski, Igor Mordatch, Karl Pertsch, Kanishka Rao, Krista Reymann, Michael Ryoo, Grecia Salazar, Pannag Sanketi, Pierre Sermanet, Jaspiar Singh, Anikait Singh, Radu Soricut, Huong Tran, Vincent Vanhoucke, Quan Vuong, Ayzaan Wahid, Stefan Welker, Paul Wohlhart, Jialin Wu, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Tianhe Yu, Brianna Zitkovich
Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web.
no code implementations • 29 Apr 2023 • Krzysztof Choromanski
We also introduce a (still unbiased) quasi Monte Carlo variant of GRFs, q-GRFs, relying on the so-called reinforced random walks, that might be used to optimize the variance of GRFs.
no code implementations • 31 Mar 2023 • Rami Botros, Anmol Gulati, Tara N. Sainath, Krzysztof Choromanski, Ruoming Pang, Trevor Strohman, Weiran Wang, Jiahui Yu
Conformer models maintain a large number of internal states, the vast majority of which are associated with self-attention layers.
1 code implementation • 2 Feb 2023 • Krzysztof Choromanski, Arijit Sehanobish, Han Lin, Yunfan Zhao, Eli Berger, Tetiana Parshakova, Alvin Pan, David Watkins, Tianyi Zhang, Valerii Likhosherstov, Somnath Basu Roy Chowdhury, Avinava Dubey, Deepali Jain, Tamas Sarlos, Snigdha Chaturvedi, Adrian Weller
We present two new classes of algorithms for efficient field integration on graphs encoding point clouds.
no code implementations • 1 Feb 2023 • Valerii Likhosherstov, Krzysztof Choromanski, Avinava Dubey, Frederick Liu, Tamas Sarlos, Adrian Weller
The problem of efficient approximation of a linear operator induced by the Gaussian or softmax kernel is often addressed using random features (RFs) which yield an unbiased approximation of the operator's result.
1 code implementation • 31 Jan 2023 • Isaac Reid, Krzysztof Choromanski, Valerii Likhosherstov, Adrian Weller
We present Simplex Random Features (SimRFs), a new random feature (RF) mechanism for unbiased approximation of the softmax and Gaussian kernels by geometrical correlation of random projection vectors.
no code implementations • 20 Nov 2022 • Zahra Shamsi, Drew Bryant, Jacob Wilson, Xiaoyu Qu, Avinava Dubey, Konik Kothari, Mostafa Dehghani, Mariya Chavarha, Valerii Likhosherstov, Brian Williams, Michael Frumkin, Fred Appelbaum, Krzysztof Choromanski, Ali Bashir, Min Fang
These individual chromosomes were used to train and assess deep learning models for classifying the 24 human chromosomes and identifying chromosomal aberrations.
no code implementations • 22 Sep 2022 • Xuesu Xiao, Tingnan Zhang, Krzysztof Choromanski, Edward Lee, Anthony Francis, Jake Varley, Stephen Tu, Sumeet Singh, Peng Xu, Fei Xia, Sven Mikael Persson, Dmitry Kalashnikov, Leila Takayama, Roy Frostig, Jie Tan, Carolina Parada, Vikas Sindhwani
Despite decades of research, existing navigation systems still face real-world challenges when deployed in the wild, e. g., in cluttered home environments or in human-occupied public spaces.
no code implementations • 13 Sep 2022 • David Watkins-Valls, Peter Allen, Krzysztof Choromanski, Jacob Varley, Nicholas Waytowich
We propose the Multiple View Performer (MVP) - a new architecture for 3D shape completion from a series of temporally sequential views.
no code implementations • 2 Aug 2022 • Yunfan Zhao, Qingkai Pan, Krzysztof Choromanski, Deepali Jain, Vikas Sindhwani
We present a new class of structured reinforcement learning policy-architectures, Implicit Two-Tower (ITT) policies, where the actions are chosen based on the attention scores of their learnable latent representations with those of the input states.
1 code implementation • 30 May 2022 • Valerii Likhosherstov, Krzysztof Choromanski, Avinava Dubey, Frederick Liu, Tamas Sarlos, Adrian Weller
We introduce chefs' random tables (CRTs), a new class of non-trigonometric random features (RFs) to approximate Gaussian and softmax kernels.
1 code implementation • 1 Apr 2022 • Andy Zeng, Maria Attarian, Brian Ichter, Krzysztof Choromanski, Adrian Wong, Stefan Welker, Federico Tombari, Aveek Purohit, Michael Ryoo, Vikas Sindhwani, Johnny Lee, Vincent Vanhoucke, Pete Florence
Large pretrained (e. g., "foundation") models exhibit distinct capabilities depending on the domain of data they are trained on.
Ranked #21 on Video Retrieval on MSR-VTT-1kA (video-to-text R@1 metric)
no code implementations • 25 Nov 2021 • Valerii Likhosherstov, Anurag Arnab, Krzysztof Choromanski, Mario Lucic, Yi Tay, Adrian Weller, Mostafa Dehghani
Can we train a single transformer model capable of processing multiple modalities and datasets, whilst sharing almost all of its learnable parameters?
1 code implementation • ICLR 2022 • Krzysztof Choromanski, Haoxian Chen, Han Lin, Yuanzhe Ma, Arijit Sehanobish, Deepali Jain, Michael S Ryoo, Jake Varley, Andy Zeng, Valerii Likhosherstov, Dmitry Kalashnikov, Vikas Sindhwani, Adrian Weller
We propose a new class of random feature methods for linearizing softmax and Gaussian kernels called hybrid random features (HRFs) that automatically adapt the quality of kernel estimation to provide most accurate approximation in the defined regions of interest.
1 code implementation • 16 Jul 2021 • Krzysztof Choromanski, Han Lin, Haoxian Chen, Tianyi Zhang, Arijit Sehanobish, Valerii Likhosherstov, Jack Parker-Holder, Tamas Sarlos, Adrian Weller, Thomas Weingarten
In this paper we provide, to the best of our knowledge, the first comprehensive approach for incorporating various masking mechanisms into Transformers architectures in a scalable way.
no code implementations • 7 Jun 2021 • Valerii Likhosherstov, Krzysztof Choromanski, Adrian Weller
Our proof is constructive, enabling us to propose an algorithm for finding adaptive inputs and fixed self-attention parameters in order to approximate a given matrix.
1 code implementation • 4 Jun 2021 • Valerii Likhosherstov, Xingyou Song, Krzysztof Choromanski, Jared Davis, Adrian Weller
Approximate bi-level optimization (ABLO) consists of (outer-level) optimization problems, involving numerical (inner-level) optimization loops.
2 code implementations • 19 Jan 2021 • Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder, Yunhao Tang, Qiuyi Zhang, Daiyi Peng, Deepali Jain, Wenbo Gao, Aldo Pacchiano, Tamas Sarlos, Yuxiang Yang
In this paper, we approach the problem of optimizing blackbox functions over large hybrid search spaces consisting of both combinatorial and continuous parameters.
1 code implementation • 13 Jan 2021 • Mircea Trofin, Yundi Qian, Eugene Brevdo, Zinan Lin, Krzysztof Choromanski, David Li
Leveraging machine-learning (ML) techniques for compiler optimizations has been widely studied and explored in academia.
2 code implementations • NeurIPS 2021 • Valerii Likhosherstov, Krzysztof Choromanski, Jared Davis, Xingyou Song, Adrian Weller
Recent works proposed various linear self-attention mechanisms, scaling only as $O(L)$ for serial computation.
12 code implementations • ICLR 2021 • Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, David Belanger, Lucy Colwell, Adrian Weller
We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear (as opposed to quadratic) space and time complexity, without relying on any priors such as sparsity or low-rankness.
Ranked #7 on Offline RL on D4RL
no code implementations • 21 Jun 2020 • Aldo Pacchiano, Philip J. Ball, Jack Parker-Holder, Krzysztof Choromanski, Stephen Roberts
The principle of optimism in the face of uncertainty is prevalent throughout sequential decision making problems such as multi-armed bandits and reinforcement learning (RL).
no code implementations • NeurIPS 2020 • Krzysztof Choromanski, Jared Quincy Davis, Valerii Likhosherstov, Xingyou Song, Jean-Jacques Slotine, Jacob Varley, Honglak Lee, Adrian Weller, Vikas Sindhwani
We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the orthogonal group O(d).
no code implementations • 13 Jun 2020 • Yunhao Tang, Krzysztof Choromanski
Off-policy learning algorithms have been known to be sensitive to the choice of hyper-parameters.
no code implementations • 5 Jun 2020 • Valerii Likhosherstov, Xingyou Song, Krzysztof Choromanski, Jared Davis, Adrian Weller
Bilevel optimization (BLO) is a popular approach with many applications including hyperparameter optimization, neural architecture search, adversarial robustness and model-agnostic meta-learning.
1 code implementation • 5 Jun 2020 • Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, David Belanger, Lucy Colwell, Adrian Weller
In response, solutions that exploit the structure and sparsity of the learned attention matrix have blossomed.
no code implementations • NeurIPS 2020 • Han Lin, Haoxian Chen, Tianyi Zhang, Clement Laroche, Krzysztof Choromanski
Orthogonal Monte Carlo (OMC) is a very effective sampling algorithm imposing structural geometric conditions (orthogonality) on samples for variance reduction.
no code implementations • ICLR Workshop DeepDiffEq 2019 • Jared Quincy Davis, Krzysztof Choromanski, Jake Varley, Honglak Lee, Jean-Jacques Slotine, Valerii Likhosterov, Adrian Weller, Ameesh Makadia, Vikas Sindhwani
Neural Ordinary Differential Equations (ODEs) are elegant reinterpretations of deep networks where continuous time can replace the discrete notion of depth, ODE solvers perform forward propagation, and the adjoint method enables efficient, constant memory backpropagation.
no code implementations • 18 Apr 2020 • Valerii Likhosherstov, Jared Davis, Krzysztof Choromanski, Adrian Weller
We introduce an efficient approach for optimization over orthogonal groups on highly parallel computation units such as GPUs or TPUs.
no code implementations • 31 Mar 2020 • Wenbo Gao, Laura Graesser, Krzysztof Choromanski, Xingyou Song, Nevena Lazic, Pannag Sanketi, Vikas Sindhwani, Navdeep Jaitly
We propose a model-free algorithm for learning efficient policies capable of returning table tennis balls by controlling robot joints at a rate of 100Hz.
no code implementations • ICML 2020 • Krzysztof Choromanski, David Cheikhi, Jared Davis, Valerii Likhosherstov, Achille Nazaret, Achraf Bahamou, Xingyou Song, Mrugank Akarte, Jack Parker-Holder, Jacob Bergquist, Yuan Gao, Aldo Pacchiano, Tamas Sarlos, Adrian Weller, Vikas Sindhwani
We present a new class of stochastic, geometrically-driven optimization algorithms on the orthogonal group $O(d)$ and naturally reductive homogeneous manifolds obtained from the action of the rotation group $SO(d)$.
no code implementations • 2 Mar 2020 • Xingyou Song, Yuxiang Yang, Krzysztof Choromanski, Ken Caluwaerts, Wenbo Gao, Chelsea Finn, Jie Tan
Learning adaptable policies is crucial for robots to operate autonomously in our complex and quickly changing world.
no code implementations • ICML 2020 • Philip Ball, Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts
Model-Based Reinforcement Learning (MBRL) offers a promising direction for sample efficient learning, often achieving state of the art results for continuous control tasks.
2 code implementations • NeurIPS 2020 • Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts
Exploration is a key problem in reinforcement learning, since agents can only learn from data they acquire in the environment.
no code implementations • 25 Sep 2019 • Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Anna Choromanska, Krzysztof Choromanski, Michael I. Jordan
We introduce a new approach for comparing reinforcement learning policies, using Wasserstein distances (WDs) in a newly defined latent behavioral space.
no code implementations • 25 Sep 2019 • Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder, Yunhao Tang, Wenbo Gao, Aldo Pacchiano, Tamas Sarlos, Deepali Jain, Yuxiang Yang
We present a neural architecture search algorithm to construct compact reinforcement learning (RL) policies, by combining ENAS and ES in a highly scalable and intuitive way.
1 code implementation • ICLR 2020 • Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, Yunhao Tang
We introduce ES-MAML, a new framework for solving the model agnostic meta learning (MAML) problem based on Evolution Strategies (ES).
no code implementations • 10 Jul 2019 • Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder, Yunhao Tang, Wenbo Gao, Aldo Pacchiano, Tamas Sarlos, Deepali Jain, Yuxiang Yang
We present a neural architecture search algorithm to construct compact reinforcement learning (RL) policies, by combining ENAS and ES in a highly scalable and intuitive way.
1 code implementation • ICML 2020 • Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Anna Choromanska, Krzysztof Choromanski, Michael. I. Jordan
We introduce a new approach for comparing reinforcement learning policies, using Wasserstein distances (WDs) in a newly defined latent behavioral space.
no code implementations • 29 May 2019 • Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang
We propose a new class of structured methods for Monte Carlo (MC) sampling, called DPPMC, designed for high-dimensional nonisotropic distributions where samples are correlated to reduce the variance of the estimator via determinantal point processes.
no code implementations • 29 May 2019 • Yunhao Tang, Krzysztof Choromanski, Alp Kucukelbir
Evolution Strategies (ES) are a powerful class of blackbox optimization techniques that recently became a competitive alternative to state-of-the-art policy gradient (PG) algorithms for reinforcement learning (RL).
no code implementations • 29 May 2019 • Albert S. Berahas, Liyuan Cao, Krzysztof Choromanski, Katya Scheinberg
We then demonstrate via rigorous analysis of the variance and by numerical comparisons on reinforcement learning tasks that the Gaussian sampling method used in [Salimans et al. 2016] is significantly inferior to the orthogonal sampling used in [Choromaski et al. 2018] as well as more general interpolation methods.
no code implementations • 3 May 2019 • Albert S. Berahas, Liyuan Cao, Krzysztof Choromanski, Katya Scheinberg
To this end, we use the results in [Berahas et al., 2019] and show how each method can satisfy the sufficient conditions, possibly only with some sufficiently large probability at each iteration, as happens to be the case with Gaussian smoothing and smoothing on a sphere.
Optimization and Control
no code implementations • 9 Mar 2019 • Mark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamas Sarlos, Adrian Weller
Wasserstein distances are increasingly used in a wide variety of applications in machine learning.
no code implementations • 7 Mar 2019 • Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Deepali Jain, Yuxiang Yang, Atil Iscen, Jasmine Hsu, Vikas Sindhwani
Interest in derivative-free optimization (DFO) and "evolutionary strategies" (ES) has recently surged in the Reinforcement Learning (RL) community, with growing evidence that they can match state of the art methods for policy optimization problems in Robotics.
1 code implementation • NeurIPS 2019 • Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang
ASEBO adapts to the geometry of the function and learns optimal sets of sensing directions, which are used to probe it, on-the-fly.
no code implementations • ICML 2018 • Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller
We present a new method of blackbox optimization via gradient approximation with the use of structured random orthogonal matrices, providing more accurate estimators than baselines and with provable theoretical guarantees.
no code implementations • 12 Feb 2018 • Anne Morvan, Antoine Souloumiac, Krzysztof Choromanski, Cédric Gouy-Pailler, Jamal Atif
The aim of this paper is to endow the well-known family of hypercubic quantization hashing methods with theoretical guarantees.
no code implementations • ICLR 2018 • Krzysztof Choromanski, Carlton Downey, Byron Boots
In this paper, we extend the theory of ORFs to Kernel Ridge Regression and show that ORFs can be used to obtain Orthogonal PSRNNs (OPSRNNs), which are smaller and faster than PSRNNs.
no code implementations • 15 Oct 2017 • Xinyan Yan, Krzysztof Choromanski, Byron Boots, Vikas Sindhwani
Policy evaluation or value function or Q-function approximation is a key procedure in reinforcement learning (RL).
8 code implementations • 25 Apr 2017 • Mariusz Bojarski, Philip Yeres, Anna Choromanska, Krzysztof Choromanski, Bernhard Firner, Lawrence Jackel, Urs Muller
This eliminates the need for human engineers to anticipate what is important in an image and foresee all the necessary rules for safe driving.
1 code implementation • 7 Mar 2017 • Anne Morvan, Krzysztof Choromanski, Cédric Gouy-Pailler, Jamal Atif
In this paper, we address the problem of recovering arbitrary-shaped data clusters from datasets while facing \emph{high space constraints}, as this is for instance the case in many real-world applications when analysis algorithms are directly deployed on resources-limited mobile devices collecting the data.
2 code implementations • NeurIPS 2017 • Krzysztof Choromanski, Mark Rowland, Adrian Weller
We examine a class of embeddings based on structured random matrices with orthogonal rows which can be applied in many machine learning applications including dimensionality reduction and kernel approximation.
4 code implementations • 16 Nov 2016 • Mariusz Bojarski, Anna Choromanska, Krzysztof Choromanski, Bernhard Firner, Larry Jackel, Urs Muller, Karol Zieba
We furthermore justify our approach with theoretical arguments and theoretically confirm that the proposed method identifies sets of input pixels, rather than individual pixels, that collaboratively contribute to the prediction.
no code implementations • NeurIPS 2016 • Felix X. Yu, Ananda Theertha Suresh, Krzysztof Choromanski, Daniel Holtmann-Rice, Sanjiv Kumar
We present an intriguing discovery related to Random Fourier Features: in Gaussian kernel approximation, replacing the random Gaussian matrix by a properly scaled random orthogonal matrix significantly decreases kernel approximation error.
no code implementations • 19 Oct 2016 • Mariusz Bojarski, Anna Choromanska, Krzysztof Choromanski, Francois Fagan, Cedric Gouy-Pailler, Anne Morvan, Nourhan Sakr, Tamas Sarlos, Jamal Atif
We consider an efficient computational framework for speeding up several machine learning algorithms with almost no loss of accuracy.
no code implementations • 29 May 2016 • Krzysztof Choromanski, Vikas Sindhwani
We propose a scheme for recycling Gaussian random vectors into structured matrices to approximate various kernel functions in sublinear time via random embeddings.
no code implementations • 29 May 2016 • Krzysztof Choromanski, Francois Fagan, Cedric Gouy-Pailler, Anne Morvan, Tamas Sarlos, Jamal Atif
In particular, as a byproduct of the presented techniques and by using relatively new Berry-Esseen-type CLT for random vectors, we give the first theoretical guarantees for one of the most efficient existing LSH algorithms based on the $\textbf{HD}_{3}\textbf{HD}_{2}\textbf{HD}_{1}$ structured matrix ("Practical and Optimal LSH for Angular Distance").
no code implementations • 17 May 2016 • Anna Choromanska, Krzysztof Choromanski, Mariusz Bojarski
We analyze the performance of the top-down multiclass classification algorithm for decision tree learning called LOMtree, recently proposed in the literature Choromanska and Langford (2014) for solving efficiently classification problems with very large number of classes.
no code implementations • 25 Apr 2016 • Krzysztof Choromanski, Francois Fagan
Our framework covers as special cases already known structured approaches such as the Fast Johnson-Lindenstrauss Transform, but is much more general since it can be applied also to highly nonlinear embeddings.
no code implementations • 16 Nov 2015 • Anna Choromanska, Krzysztof Choromanski, Mariusz Bojarski, Tony Jebara, Sanjiv Kumar, Yann Lecun
We prove several theoretical results showing that projections via various structured matrices followed by nonlinear mappings accurately preserve the angular distance between input high-dimensional vectors.
no code implementations • 4 Sep 2015 • Ruiqi Guo, Sanjiv Kumar, Krzysztof Choromanski, David Simcha
We propose a quantization based approach for fast approximate Maximum Inner Product Search (MIPS).
no code implementations • 10 Jun 2015 • Krzysztof Choromanski, Sanjiv Kumar, Xiaofeng Liu
To achieve fast clustering, we propose to represent each cluster by a skeleton set which is updated continuously as new data is seen.
no code implementations • 26 Oct 2014 • Mariusz Bojarski, Anna Choromanska, Krzysztof Choromanski, Yann Lecun
We consider supervised learning with random decision trees, where the tree construction is completely random.
no code implementations • 26 Oct 2014 • Apoorv Agarwal, Anna Choromanska, Krzysztof Choromanski
In this paper, we compare three initialization schemes for the KMEANS clustering algorithm: 1) random initialization (KMEANSRAND), 2) KMEANS++, and 3) KMEANSD++.
1 code implementation • 24 Feb 2014 • Felix X. Yu, Krzysztof Choromanski, Sanjiv Kumar, Tony Jebara, Shih-Fu Chang
Learning from Label Proportions (LLP) is a learning setting, where the training data is provided in groups, or "bags", and only the proportion of each class in each bag is known.