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2020 – today
- 2024
[i111]Dhananjay Ashok, Barnabás Póczos:
Controllable Text Generation in the Instruction-Tuning Era. CoRR abs/2405.01490 (2024)- [i110]Amira Alakhdar, Barnabás Póczos, Newell Washburn:
Diffusion Models in De Novo Drug Design. CoRR abs/2406.08511 (2024) - [i109]Tongzhou Liao, Barnabás Póczos:
Graph Attention with Random Rewiring. CoRR abs/2407.05649 (2024) - [i108]Yuchen Shen, Barnabás Póczos:
GraphBPE: Molecular Graphs Meet Byte-Pair Encoding. CoRR abs/2407.19039 (2024) - 2023
- [c121]Dhananjay Ashok, Atharva Kulkarni, Hai Pham, Barnabás Póczos
:
The student becomes the master: Outperforming GPT3 on Scientific Factual Error Correction. EMNLP (Findings) 2023: 6762-6778 - [i107]Dhananjay Ashok, Atharva Kulkarni, Hai Pham, Barnabás Póczos:
The student becomes the master: Matching GPT3 on Scientific Factual Error Correction. CoRR abs/2305.14707 (2023) - [i106]Chenghui Zhou, Barnabás Póczos:
Objective-Agnostic Enhancement of Molecule Properties via Multi-Stage VAE. CoRR abs/2308.13066 (2023) - [i105]Hai Pham, Young Jin Kim, Subhabrata Mukherjee, David P. Woodruff, Barnabás Póczos, Hany Hassan Awadalla:
Task-Based MoE for Multitask Multilingual Machine Translation. CoRR abs/2308.15772 (2023) - 2022
- [i104]Han Nguyen, Hai Pham, Sashank J. Reddi, Barnabás Póczos:
On the Algorithmic Stability and Generalization of Adaptive Optimization Methods. CoRR abs/2211.03970 (2022) - [i103]Chenghui Zhou, Barnabás Póczos:
Improving Molecule Properties Through 2-Stage VAE. CoRR abs/2212.02750 (2022) - 2021
- [c120]George Stoica, Emmanouil Antonios Platanios, Barnabás Póczos:
Re-TACRED: Addressing Shortcomings of the TACRED Dataset. AAAI 2021: 13843-13850 - [c119]Yiwei Lyu, Paul Pu Liang, Hai Pham, Eduard H. Hovy, Barnabás Póczos, Ruslan Salakhutdinov, Louis-Philippe Morency:
StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer. NAACL-HLT 2021: 2116-2138 - [c118]Chenghui Zhou, Chun-Liang Li, Barnabás Póczos:
Unsupervised program synthesis for images by sampling without replacement. UAI 2021: 408-418 - [i102]Yiwei Lyu, Paul Pu Liang, Hai Pham, Eduard H. Hovy, Barnabás Póczos, Ruslan Salakhutdinov, Louis-Philippe Morency:
StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer. CoRR abs/2104.05196 (2021) - [i101]George Stoica, Emmanouil Antonios Platanios, Barnabás Póczos:
Re-TACRED: Addressing Shortcomings of the TACRED Dataset. CoRR abs/2104.08398 (2021) - [i100]Otilia Stretcu, Emmanouil Antonios Platanios, Tom M. Mitchell, Barnabás Póczos:
Coarse-to-Fine Curriculum Learning. CoRR abs/2106.04072 (2021) - 2020
- [j15]Michael Andrews, Manfred Paulini, Sergei Gleyzer, Barnabás Póczos:
End-to-End Physics Event Classification with CMS Open Data: Applying Image-Based Deep Learning to Detector Data for the Direct Classification of Collision Events at the LHC. Comput. Softw. Big Sci. 4(1) (2020) - [j14]Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabás Póczos, Eric P. Xing:
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly. J. Mach. Learn. Res. 21: 81:1-81:27 (2020) - [c117]George Stoica, Otilia Stretcu, Emmanouil Antonios Platanios, Tom M. Mitchell, Barnabás Póczos:
Contextual Parameter Generation for Knowledge Graph Link Prediction. AAAI 2020: 3000-3008 - [c116]Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabás Póczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W. Black, Shrimai Prabhumoye:
Politeness Transfer: A Tag and Generate Approach. ACL 2020: 1869-1881 - [c115]Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing:
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. AISTATS 2020: 3393-3403 - [c114]Zirui Wang, Sanket Vaibhav Mehta, Barnabás Póczos, Jaime G. Carbonell:
Efficient Meta Lifelong-Learning with Limited Memory. EMNLP (1) 2020: 535-548 - [c113]Hai Pham, Amrith Setlur, Saket Dingliwal, Tzu-Hsiang Lin, Barnabás Póczos, Kang Huang, Zhuo Li, Jae Lim, Collin McCormack, Tam Vu:
Robust Handwriting Recognition with Limited and Noisy Data. ICFHR 2020: 301-306 - [c112]Biswajit Paria, Chih-Kuan Yeh, Ian En-Hsu Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos:
Minimizing FLOPs to Learn Efficient Sparse Representations. ICLR 2020 - [c111]Zoltán Ádám Milacski, Barnabás Póczos, András Lörincz:
VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video Processing. ICML 2020: 6893-6904 - [c110]Amrith Setlur, Barnabás Póczos, Alan W. Black:
Nonlinear ISA with Auxiliary Variables for Learning Speech Representations. INTERSPEECH 2020: 180-184 - [c109]Mariya Toneva, Otilia Stretcu, Barnabás Póczos, Leila Wehbe, Tom M. Mitchell:
Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction. NeurIPS 2020 - [c108]Ananya Uppal, Shashank Singh, Barnabás Póczos:
Robust Density Estimation under Besov IPM Losses. NeurIPS 2020 - [i99]Adarsh Dave, Jared Mitchell, Kirthevasan Kandasamy, Sven Burke, Biswajit Paria, Barnabás Póczos, Jay Whitacre, Venkatasubramanian Viswanathan:
Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning. CoRR abs/2001.09938 (2020) - [i98]Chenghui Zhou, Chun-Liang Li, Barnabás Póczos:
Unsupervised Program Synthesis for Images using Tree-Structured LSTM. CoRR abs/2001.10119 (2020) - [i97]Ilqar Ramazanli, Barnabás Póczos:
Optimal Adaptive Matrix Completion. CoRR abs/2002.02431 (2020) - [i96]Ilqar Ramazanli, Han Nguyen, Hai Pham, Sashank J. Reddi, Barnabás Póczos:
Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets. CoRR abs/2002.08528 (2020) - [i95]Biswajit Paria, Chih-Kuan Yeh, Ian En-Hsu Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos:
Minimizing FLOPs to Learn Efficient Sparse Representations. CoRR abs/2004.05665 (2020) - [i94]Ananya Uppal, Shashank Singh, Barnabás Póczos:
Robust Density Estimation under Besov IPM Losses. CoRR abs/2004.08597 (2020) - [i93]Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabás Póczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W. Black, Shrimai Prabhumoye:
Politeness Transfer: A Tag and Generate Approach. CoRR abs/2004.14257 (2020) - [i92]Amrith Setlur, Saket Dingliwal, Barnabás Póczos:
Covariate Distribution Aware Meta-learning. CoRR abs/2007.02523 (2020) - [i91]Amrith Setlur, Barnabás Póczos, Alan W. Black:
Nonlinear ISA with Auxiliary Variables for Learning Speech Representations. CoRR abs/2007.12948 (2020) - [i90]Hai Pham, Amrith Setlur, Saket Dingliwal, Tzu-Hsiang Lin, Barnabás Póczos, Kang Huang, Zhuo Li, Jae Lim, Collin McCormack, Tam Vu:
Robust Handwriting Recognition with Limited and Noisy Data. CoRR abs/2008.08148 (2020) - [i89]Mariya Toneva, Otilia Stretcu, Barnabás Póczos, Leila Wehbe, Tom M. Mitchell:
Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction. CoRR abs/2009.08424 (2020) - [i88]Zirui Wang, Sanket Vaibhav Mehta, Barnabás Póczos, Jaime G. Carbonell:
Efficient Meta Lifelong-Learning with Limited Memory. CoRR abs/2010.02500 (2020) - [i87]George Stoica, Emmanouil Antonios Platanios, Barnabás Póczos:
Improving Relation Extraction by Leveraging Knowledge Graph Link Prediction. CoRR abs/2012.04812 (2020)
2010 – 2019
- 2019
- [j13]Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos:
Multi-fidelity Gaussian Process Bandit Optimisation. J. Artif. Intell. Res. 66: 151-196 (2019) - [j12]Shashank Singh, Yang Yang, Barnabás Póczos, Jian Ma:
Predicting enhancer-promoter interaction from genomic sequence with deep neural networks. Quant. Biol. 7(2): 122-137 (2019) - [c107]Hai Pham, Paul Pu Liang, Thomas Manzini, Louis-Philippe Morency, Barnabás Póczos:
Found in Translation: Learning Robust Joint Representations by Cyclic Translations between Modalities. AAAI 2019: 6892-6899 - [c106]Yifan Wu, Barnabás Póczos, Aarti Singh:
Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent. AISTATS 2019: 1070-1078 - [c105]Chun-Liang Li, Wei-Cheng Chang, Youssef Mroueh, Yiming Yang, Barnabás Póczos:
Implicit Kernel Learning. AISTATS 2019: 2007-2016 - [c104]Zoltán Ádám Milacski, Barnabás Póczos, András Lorincz:
Differentiable Unrolled Alternating Direction Method of Multipliers for OneNet. BMVC 2019: 140 - [c103]Zirui Wang, Zihang Dai, Barnabás Póczos, Jaime G. Carbonell:
Characterizing and Avoiding Negative Transfer. CVPR 2019: 11293-11302 - [c102]Chun-Liang Li, Tomas Simon, Jason M. Saragih, Barnabás Póczos, Yaser Sheikh:
LBS Autoencoder: Self-Supervised Fitting of Articulated Meshes to Point Clouds. CVPR 2019: 11967-11976 - [c101]Wei-Cheng Chang, Chun-Liang Li, Yiming Yang, Barnabás Póczos:
Kernel Change-point Detection with Auxiliary Deep Generative Models. ICLR (Poster) 2019 - [c100]Simon S. Du, Xiyu Zhai, Barnabás Póczos, Aarti Singh:
Gradient Descent Provably Optimizes Over-parameterized Neural Networks. ICLR (Poster) 2019 - [c99]Chun-Liang Li, Manzil Zaheer, Yang Zhang, Barnabás Póczos, Ruslan Salakhutdinov:
Point Cloud GAN. DGS@ICLR 2019 - [c98]Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos:
Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments. ICML 2019: 3222-3232 - [c97]Zoltán Ádám Milacski, Barnabás Póczos, András Lörincz:
Group k-Sparse Temporal Convolutional Neural Networks: Unsupervised Pretraining for Video Classification. IJCNN 2019: 1-10 - [c96]Emmanouil Antonios Platanios, Otilia Stretcu, Graham Neubig, Barnabás Póczos, Tom M. Mitchell:
Competence-based Curriculum Learning for Neural Machine Translation. NAACL-HLT (1) 2019: 1162-1172 - [c95]Simon S. Du, Kangcheng Hou, Ruslan Salakhutdinov, Barnabás Póczos, Ruosong Wang, Keyulu Xu:
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels. NeurIPS 2019: 5724-5734 - [c94]Emre Yolcu, Barnabás Póczos:
Learning Local Search Heuristics for Boolean Satisfiability. NeurIPS 2019: 7990-8001 - [c93]Ananya Uppal, Shashank Singh, Barnabás Póczos:
Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses. NeurIPS 2019: 9086-9097 - [c92]Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos:
A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations. UAI 2019: 766-776 - [i86]Wei-Cheng Chang, Chun-Liang Li, Yiming Yang, Barnabás Póczos:
Kernel Change-point Detection with Auxiliary Deep Generative Models. CoRR abs/1901.06077 (2019) - [i85]Willie Neiswanger, Kirthevasan Kandasamy, Barnabás Póczos, Jeff Schneider, Eric P. Xing:
ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization. CoRR abs/1901.11515 (2019) - [i84]Ananya Uppal, Shashank Singh, Barnabás Póczos:
Nonparametric Density Estimation under Besov IPM Losses. CoRR abs/1902.03511 (2019) - [i83]Michael Andrews, John Alison, Sitong An, Patrick Bryant, Bjorn Burkle, Sergei Gleyzer, Meenakshi Narain, Manfred Paulini, Barnabás Póczos, Emanuele Usai:
End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data. CoRR abs/1902.08276 (2019) - [i82]Chun-Liang Li, Wei-Cheng Chang, Youssef Mroueh, Yiming Yang, Barnabás Póczos:
Implicit Kernel Learning. CoRR abs/1902.10214 (2019) - [i81]Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabás Póczos, Eric P. Xing:
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly. CoRR abs/1903.06694 (2019) - [i80]Emmanouil Antonios Platanios, Otilia Stretcu, Graham Neubig, Barnabás Póczos, Tom M. Mitchell:
Competence-based Curriculum Learning for Neural Machine Translation. CoRR abs/1903.09848 (2019) - [i79]Chun-Liang Li, Tomas Simon, Jason M. Saragih, Barnabás Póczos, Yaser Sheikh:
LBS Autoencoder: Self-supervised Fitting of Articulated Meshes to Point Clouds. CoRR abs/1904.10037 (2019) - [i78]Simon S. Du, Kangcheng Hou, Barnabás Póczos, Ruslan Salakhutdinov, Ruosong Wang, Keyulu Xu:
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels. CoRR abs/1905.13192 (2019) - [i77]Haiguang Liao, Wentai Zhang, Xuliang Dong, Barnabás Póczos, Kenji Shimada, Levent Burak Kara:
A Deep Reinforcement Learning Approach for Global Routing. CoRR abs/1906.08809 (2019) - [i76]Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing:
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. CoRR abs/1908.01425 (2019) - [i75]Songwei Ge, Austin Dill, Eunsu Kang, Chun-Liang Li, Lingyao Zhang, Manzil Zaheer, Barnabás Póczos:
Developing Creative AI to Generate Sculptural Objects. CoRR abs/1908.07587 (2019) - [i74]Amrith Setlur, Barnabás Póczos:
Better Approximate Inference for Partial Likelihood Models with a Latent Structure. CoRR abs/1910.10211 (2019) - [i73]Kai Hu, Barnabás Póczos:
RotationOut as a Regularization Method for Neural Network. CoRR abs/1911.07427 (2019) - [i72]Joel Ruben Antony Moniz, Eunsu Kang, Barnabás Póczos:
LucidDream: Controlled Temporally-Consistent DeepDream on Videos. CoRR abs/1911.11960 (2019) - [i71]Austin Dill, Songwei Ge, Eunsu Kang, Chun-Liang Li, Barnabás Póczos:
Learned Interpolation for 3D Generation. CoRR abs/1912.10787 (2019) - 2018
- [c91]Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos:
Parallelised Bayesian Optimisation via Thompson Sampling. AISTATS 2018: 133-142 - [c90]Sashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabás Póczos, Francis R. Bach, Ruslan Salakhutdinov, Alexander J. Smola:
A Generic Approach for Escaping Saddle points. AISTATS 2018: 1233-1242 - [c89]Shashank Singh, Barnabás Póczos, Jian Ma:
Minimax Reconstruction Risk of Convolutional Sparse Dictionary Learning. AISTATS 2018: 1327-1336 - [c88]Yusha Liu, Chun-Liang Li, Barnabás Póczos:
Classifier Two Sample Test for Video Anomaly Detections. BMVC 2018: 71 - [c87]Simon S. Du, Jason D. Lee, Yuandong Tian, Aarti Singh, Barnabás Póczos:
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima. ICML 2018: 1338-1347 - [c86]Junier B. Oliva, Avinava Dubey, Manzil Zaheer, Barnabás Póczos, Ruslan Salakhutdinov, Eric P. Xing, Jeff Schneider:
Transformation Autoregressive Networks. ICML 2018: 3895-3904 - [c85]Paloma Sodhi, Hanqi Sun, Barnabás Póczos, David Wettergreen:
Robust Plant Phenotyping via Model-Based Optimization. IROS 2018: 7689-7696 - [c84]Sumedha Singla, Mingming Gong, Siamak Ravanbakhsh, Frank C. Sciurba, Barnabás Póczos, Kayhan N. Batmanghelich:
Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector. MICCAI (1) 2018: 502-510 - [c83]Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing:
Neural Architecture Search with Bayesian Optimisation and Optimal Transport. NeurIPS 2018: 2020-2029 - [c82]Shashank Singh, Ananya Uppal, Boyue Li, Chun-Liang Li, Manzil Zaheer, Barnabás Póczos:
Nonparametric Density Estimation under Adversarial Losses. NeurIPS 2018: 10246-10257 - [i70]Yifan Wu, Barnabás Póczos, Aarti Singh:
Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent. CoRR abs/1802.04420 (2018) - [i69]Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing:
Neural Architecture Search with Bayesian Optimisation and Optimal Transport. CoRR abs/1802.07191 (2018) - [i68]Shashank Singh, Barnabás Póczos:
Minimax Distribution Estimation in Wasserstein Distance. CoRR abs/1802.08855 (2018) - [i67]Shashank Singh, Bharath K. Sriperumbudur, Barnabás Póczos:
Minimax Estimation of Quadratic Fourier Functionals. CoRR abs/1803.11451 (2018) - [i66]Shashank Singh, Ananya Uppal, Boyue Li, Chun-Liang Li, Manzil Zaheer, Barnabás Póczos:
Nonparametric Density Estimation under Adversarial Losses. CoRR abs/1805.08836 (2018) - [i65]Yotam Hechtlinger, Barnabás Póczos, Larry A. Wasserman:
Cautious Deep Learning. CoRR abs/1805.09460 (2018) - [i64]Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos:
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming. CoRR abs/1805.09964 (2018) - [i63]Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos:
A Flexible Multi-Objective Bayesian Optimization Approach using Random Scalarizations. CoRR abs/1805.12168 (2018) - [i62]Sumedha Singla, Mingming Gong, Siamak Ravanbakhsh, Frank C. Sciurba, Barnabás Póczos, Kayhan N. Batmanghelich:
Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector. CoRR abs/1806.11217 (2018) - [i61]Hai Pham, Thomas Manzini, Paul Pu Liang, Barnabás Póczos:
Seq2Seq2Sentiment: Multimodal Sequence to Sequence Models for Sentiment Analysis. CoRR abs/1807.03915 (2018) - [i60]Michael Andrews, Manfred Paulini, Sergei Gleyzer, Barnabás Póczos:
End-to-End Physics Event Classification with the CMS Open Data: Applying Image-based Deep Learning on Detector Data to Directly Classify Collision Events at the LHC. CoRR abs/1807.11916 (2018) - [i59]Simon S. Du, Xiyu Zhai, Barnabás Póczos, Aarti Singh:
Gradient Descent Provably Optimizes Over-parameterized Neural Networks. CoRR abs/1810.02054 (2018) - [i58]Chun-Liang Li, Manzil Zaheer, Yang Zhang, Barnabás Póczos, Ruslan Salakhutdinov:
Point Cloud GAN. CoRR abs/1810.05795 (2018) - [i57]Chun-Liang Li, Eunsu Kang, Songwei Ge, Lingyao Zhang, Austin Dill, Manzil Zaheer, Barnabás Póczos:
Hallucinating Point Cloud into 3D Sculptural Object. CoRR abs/1811.05389 (2018) - [i56]Siyu He, Yin Li, Yu Feng, Shirley Ho, Siamak Ravanbakhsh, Wei Chen, Barnabás Póczos:
Learning to Predict the Cosmological Structure Formation. CoRR abs/1811.06533 (2018) - [i55]Zirui Wang, Zihang Dai, Barnabás Póczos, Jaime G. Carbonell:
Characterizing and Avoiding Negative Transfer. CoRR abs/1811.09751 (2018) - [i54]Hai Pham, Paul Pu Liang, Thomas Manzini, Louis-Philippe Morency, Barnabás Póczos:
Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities. CoRR abs/1812.07809 (2018) - 2017
- [j11]Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos:
Query efficient posterior estimation in scientific experiments via Bayesian active learning. Artif. Intell. 243: 45-56 (2017) - [c81]Siamak Ravanbakhsh, François Lanusse, Rachel Mandelbaum, Jeff G. Schneider, Barnabás Póczos:
Enabling Dark Energy Science with Deep Generative Models of Galaxy Images. AAAI 2017: 1488-1494 - [c80]Srinivasan Vijayarangan, Paloma Sodhi, Prathamesh Kini, James Bourne, Simon S. Du, Hanqi Sun, Barnabás Póczos, Dimitrios Apostolopoulos, David Wettergreen:
High-Throughput Robotic Phenotyping of Energy Sorghum Crops. FSR 2017: 99-113 - [c79]Jen-Hao Rick Chang, Chun-Liang Li, Barnabás Póczos, B. V. K. Vijaya Kumar:
One Network to Solve Them All - Solving Linear Inverse Problems Using Deep Projection Models. ICCV 2017: 5889-5898 - [c78]Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos:
Deep Learning with Sets and Point Clouds. ICLR (Workshop) 2017 - [c77]Kirthevasan Kandasamy, Gautam Dasarathy, Jeff G. Schneider, Barnabás Póczos:
Multi-fidelity Bayesian Optimisation with Continuous Approximations. ICML 2017: 1799-1808 - [c76]Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider:
The Statistical Recurrent Unit. ICML 2017: 2671-2680 - [c75]Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos:
Equivariance Through Parameter-Sharing. ICML 2017: 2892-2901 - [c74]Shashank Singh, Barnabás Póczos:
Nonparanormal Information Estimation. ICML 2017: 3210-3219 - [c73]Wei-Cheng Chang, Chun-Liang Li, Yiming Yang, Barnabás Póczos:
Data-driven Random Fourier Features using Stein Effect. IJCAI 2017: 1497-1503 - [c72]Simon S. Du, Jayanth Koushik, Aarti Singh, Barnabás Póczos:
Hypothesis Transfer Learning via Transformation Functions. NIPS 2017: 574-584 - [c71]Simon S. Du, Chi Jin, Jason D. Lee, Michael I. Jordan, Aarti Singh, Barnabás Póczos:
Gradient Descent Can Take Exponential Time to Escape Saddle Points. NIPS 2017: 1067-1077 - [c70]Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, Barnabás Póczos:
MMD GAN: Towards Deeper Understanding of Moment Matching Network. NIPS 2017: 2203-2213 - [c69]Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, Alexander J. Smola:
Deep Sets. NIPS 2017: 3391-3401 - [c68]Xiao Fu, Kejun Huang, Otilia Stretcu, Hyun Ah Song, Evangelos E. Papalexakis, Partha P. Talukdar, Tom M. Mitchell, Nicholas D. Sidiropoulos, Christos Faloutsos, Barnabás Póczos:
BrainZoom: High Resolution Reconstruction from Multi-modal Brain Signals. SDM 2017: 216-227 - [c67]Pengtao Xie, Barnabás Póczos, Eric P. Xing:
Near-Orthogonality Regularization in Kernel Methods. UAI 2017 - [i53]Shashank Singh, Barnabás Póczos:
Nonparanormal Information Estimation. CoRR abs/1702.07803 (2017) - [i52]Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos:
Equivariance Through Parameter-Sharing. CoRR abs/1702.08389 (2017) - [i51]Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider:
The Statistical Recurrent Unit. CoRR abs/1703.00381 (2017) - [i50]Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, Alexander J. Smola:
Deep Sets. CoRR abs/1703.06114 (2017) - [i49]Jen-Hao Rick Chang, Chun-Liang Li, Barnabás Póczos, B. V. K. Vijaya Kumar, Aswin C. Sankaranarayanan:
One Network to Solve Them All - Solving Linear Inverse Problems using Deep Projection Models. CoRR abs/1703.09912 (2017) - [i48]Wei-Cheng Chang, Chun-Liang Li, Yiming Yang, Barnabás Póczos:
Data-driven Random Fourier Features using Stein Effect. CoRR abs/1705.08525 (2017) - [i47]Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, Barnabás Póczos:
MMD GAN: Towards Deeper Understanding of Moment Matching Network. CoRR abs/1705.08584 (2017) - [i46]Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff G. Schneider, Barnabás Póczos:
Asynchronous Parallel Bayesian Optimisation via Thompson Sampling. CoRR abs/1705.09236 (2017) - [i45]Simon S. Du, Chi Jin, Jason D. Lee, Michael I. Jordan, Barnabás Póczos, Aarti Singh:
Gradient Descent Can Take Exponential Time to Escape Saddle Points. CoRR abs/1705.10412 (2017) - [i44]Junier B. Oliva, Kumar Avinava Dubey, Barnabás Póczos, Eric P. Xing, Jeff G. Schneider:
Recurrent Estimation of Distributions. CoRR abs/1705.10750 (2017) - [i43]Shashank Singh, Barnabás Póczos, Jian Ma:
On the Reconstruction Risk of Convolutional Sparse Dictionary Learning. CoRR abs/1708.08587 (2017) - [i42]Sashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabás Póczos, Francis R. Bach, Ruslan Salakhutdinov, Alexander J. Smola:
A Generic Approach for Escaping Saddle points. CoRR abs/1709.01434 (2017) - [i41]Siamak Ravanbakhsh, Junier B. Oliva, Sebastian Fromenteau, Layne C. Price, Shirley Ho, Jeff G. Schneider, Barnabás Póczos:
Estimating Cosmological Parameters from the Dark Matter Distribution. CoRR abs/1711.02033 (2017) - [i40]Simon S. Du, Jason D. Lee, Yuandong Tian, Barnabás Póczos, Aarti Singh:
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima. CoRR abs/1712.00779 (2017) - 2016
- [j10]Zoltán Szabó, Bharath K. Sriperumbudur, Barnabás Póczos, Arthur Gretton:
Learning Theory for Distribution Regression. J. Mach. Learn. Res. 17: 152:1-152:40 (2016) - [j9]Fang-Cheng Yeh, Jean M. Vettel, Aarti Singh, Barnabás Póczos, Scott T. Grafton, Kirk I. Erickson, Wen-Yih Isaac Tseng, Timothy D. Verstynen:
Quantifying Differences and Similarities in Whole-Brain White Matter Architecture Using Local Connectome Fingerprints. PLoS Comput. Biol. 12(11) (2016) - [c66]Danica J. Sutherland, Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider:
Linear-Time Learning on Distributions with Approximate Kernel Embeddings. AAAI 2016: 2073-2079 - [c65]Siamak Ravanbakhsh, Barnabás Póczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner:
Stochastic Neural Networks with Monotonic Activation Functions. AISTATS 2016: 809-818 - [c64]Chun-Liang Li, Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider:
High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models. AISTATS 2016: 884-892 - [c63]Junier B. Oliva, Avinava Dubey, Andrew Gordon Wilson, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing:
Bayesian Nonparametric Kernel-Learning. AISTATS 2016: 1078-1086 - [c62]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Stochastic Frank-Wolfe methods for nonconvex optimization. Allerton 2016: 1244-1251 - [c61]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Fast incremental method for smooth nonconvex optimization. CDC 2016: 1971-1977 - [c60]Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Stochastic Variance Reduction for Nonconvex Optimization. ICML 2016: 314-323 - [c59]Siamak Ravanbakhsh, Barnabás Póczos, Russell Greiner:
Boolean Matrix Factorization and Noisy Completion via Message Passing. ICML 2016: 945-954 - [c58]Siamak Ravanbakhsh, Junier B. Oliva, Sebastian Fromenteau, Layne Price, Shirley Ho, Jeff G. Schneider, Barnabás Póczos:
Estimating Cosmological Parameters from the Dark Matter Distribution. ICML 2016: 2407-2416 - [c57]Xuezhi Wang, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos:
Nonparametric Risk and Stability Analysis for Multi-Task Learning Problems. IJCAI 2016: 2146-2152 - [c56]Abhijeet Tallavajhula, Barnabás Póczos, Alonzo Kelly:
Nonparametric distribution regression applied to sensor modeling. IROS 2016: 619-625 - [c55]Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos:
Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations. NIPS 2016: 992-1000 - [c54]Shashank Singh, Simon S. Du, Barnabás Póczos:
Efficient Nonparametric Smoothness Estimation. NIPS 2016: 1010-1018 - [c53]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization. NIPS 2016: 1145-1153 - [c52]Kumar Avinava Dubey, Sashank J. Reddi, Sinead A. Williamson, Barnabás Póczos, Alexander J. Smola, Eric P. Xing:
Variance Reduction in Stochastic Gradient Langevin Dynamics. NIPS 2016: 1154-1162 - [c51]Shashank Singh, Barnabás Póczos:
Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators. NIPS 2016: 1217-1225 - [c50]Kirthevasan Kandasamy, Gautam Dasarathy, Barnabás Póczos, Jeff G. Schneider:
The Multi-fidelity Multi-armed Bandit. NIPS 2016: 1777-1785 - [c49]Chun-Liang Li, Barnabás Póczos:
Utilize Old Coordinates: Faster Doubly Stochastic Gradients for Kernel Methods. UAI 2016 - [i39]Siamak Ravanbakhsh, Barnabás Póczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner:
Stochastic Neural Networks with Monotonic Activation Functions. CoRR abs/1601.00034 (2016) - [i38]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Fast Incremental Method for Nonconvex Optimization. CoRR abs/1603.06159 (2016) - [i37]Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Stochastic Variance Reduction for Nonconvex Optimization. CoRR abs/1603.06160 (2016) - [i36]Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos:
Multi-fidelity Gaussian Process Bandit Optimisation. CoRR abs/1603.06288 (2016) - [i35]Shashank Singh, Barnabás Póczos:
Analysis of k-Nearest Neighbor Distances with Application to Entropy Estimation. CoRR abs/1603.08578 (2016) - [i34]Shashank Singh, Barnabás Póczos:
Exponential Concentration of a Density Functional Estimator. CoRR abs/1603.08584 (2016) - [i33]Shashank Singh, Barnabás Póczos:
Generalized Exponential Concentration Inequality for Rényi Divergence Estimation. CoRR abs/1603.08589 (2016) - [i32]Shashank Singh, Simon S. Du, Barnabás Póczos:
Efficient Nonparametric Smoothness Estimation. CoRR abs/1605.05785 (2016) - [i31]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Fast Stochastic Methods for Nonsmooth Nonconvex Optimization. CoRR abs/1605.06900 (2016) - [i30]Shashank Singh, Barnabás Póczos:
Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators. CoRR abs/1606.01554 (2016) - [i29]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Stochastic Frank-Wolfe Methods for Nonconvex Optimization. CoRR abs/1607.08254 (2016) - [i28]Sashank J. Reddi, Jakub Konecný, Peter Richtárik, Barnabás Póczos, Alexander J. Smola:
AIDE: Fast and Communication Efficient Distributed Optimization. CoRR abs/1608.06879 (2016) - [i27]Siamak Ravanbakhsh, François Lanusse, Rachel Mandelbaum, Jeff G. Schneider, Barnabás Póczos:
Enabling Dark Energy Science with Deep Generative Models of Galaxy Images. CoRR abs/1609.05796 (2016) - [i26]Kirthevasan Kandasamy, Gautam Dasarathy, Jeff G. Schneider, Barnabás Póczos:
The Multi-fidelity Multi-armed Bandit. CoRR abs/1610.09726 (2016) - [i25]Chun-Liang Li, Siamak Ravanbakhsh, Barnabás Póczos:
Annealing Gaussian into ReLU: a New Sampling Strategy for Leaky-ReLU RBM. CoRR abs/1611.03879 (2016) - [i24]Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos:
Deep Learning with Sets and Point Clouds. CoRR abs/1611.04500 (2016) - [i23]Simon Shaolei Du, Jayanth Koushik, Aarti Singh, Barnabás Póczos:
Transformation Function Based Methods for Model Shift. CoRR abs/1612.01020 (2016) - 2015
- [j8]In-Soo Jung, Mario Berges, James H. Garrett Jr., Barnabás Póczos:
Exploration and evaluation of AR, MPCA and KL anomaly detection techniques to embankment dam piezometer data. Adv. Eng. Informatics 29(4): 902-917 (2015) - [c48]Sashank Jakkam Reddi, Barnabás Póczos, Alexander J. Smola:
Doubly Robust Covariate Shift Correction. AAAI 2015: 2949-2955 - [c47]Aaditya Ramdas, Sashank Jakkam Reddi, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
On the Decreasing Power of Kernel and Distance Based Nonparametric Hypothesis Tests in High Dimensions. AAAI 2015: 3571-3577 - [c46]Zoltán Szabó, Arthur Gretton, Barnabás Póczos, Bharath K. Sriperumbudur:
Two-stage sampled learning theory on distributions. AISTATS 2015 - [c45]Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry A. Wasserman:
On Estimating L22 Divergence. AISTATS 2015 - [c44]Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Hy Trac, Shirley Ho, Jeff G. Schneider:
Fast Function to Function Regression. AISTATS 2015 - [c43]Sashank J. Reddi, Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
On the High Dimensional Power of a Linear-Time Two Sample Test under Mean-shift Alternatives. AISTATS 2015 - [c42]Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos:
High Dimensional Bayesian Optimisation and Bandits via Additive Models. ICML 2015: 295-304 - [c41]Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos:
Bayesian Active Learning for Posterior Estimation - IJCAI-15 Distinguished Paper. IJCAI 2015: 3605-3611 - [c40]Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, Larry A. Wasserman, James M. Robins:
Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations. NIPS 2015: 397-405 - [c39]Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants. NIPS 2015: 2647-2655 - [c38]Sashank J. Reddi, Barnabás Póczos, Alexander J. Smola:
Communication Efficient Coresets for Empirical Loss Minimization. UAI 2015: 752-761 - [i22]Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos:
High Dimensional Bayesian Optimisation and Bandits via Additive Models. CoRR abs/1503.01673 (2015) - [i21]Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
An Analysis of Active Learning With Uniform Feature Noise. CoRR abs/1505.04215 (2015) - [i20]Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants. CoRR abs/1506.06840 (2015) - [i19]Aaditya Ramdas, Sashank J. Reddi, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing. CoRR abs/1508.00655 (2015) - [i18]Danica J. Sutherland, Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider:
Linear-time Learning on Distributions with Approximate Kernel Embeddings. CoRR abs/1509.07553 (2015) - [i17]Junier B. Oliva, Danica J. Sutherland, Barnabás Póczos, Jeff G. Schneider:
Deep Mean Maps. CoRR abs/1511.04150 (2015) - 2014
- [c37]Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing:
Fast Distribution To Real Regression. AISTATS 2014: 706-714 - [c36]Junier B. Oliva, Barnabás Póczos, Timothy D. Verstynen, Aarti Singh, Jeff G. Schneider, Fang-Cheng Yeh, Wen-Yih Isaac Tseng:
FuSSO: Functional Shrinkage and Selection Operator. AISTATS 2014: 715-723 - [c35]Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
An Analysis of Active Learning with Uniform Feature Noise. AISTATS 2014: 805-813 - [c34]Shashank Singh, Barnabás Póczos:
Generalized Exponential Concentration Inequality for Renyi Divergence Estimation. ICML 2014: 333-341 - [c33]Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry A. Wasserman:
Nonparametric Estimation of Renyi Divergence and Friends. ICML 2014: 919-927 - [c32]Shashank Singh, Barnabás Póczos:
Exponential Concentration of a Density Functional Estimator. NIPS 2014: 3032-3040 - [c31]Sashank J. Reddi, Barnabás Póczos:
k-NN Regression on Functional Data with Incomplete Observations. UAI 2014: 692-701 - [i16]Zoltán Szabó, Arthur Gretton, Barnabás Póczos, Bharath K. Sriperumbudur:
Consistent, Two-Stage Sampled Distribution Regression via Mean Embedding. CoRR abs/1402.1754 (2014) - [i15]Sashank J. Reddi, Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
Kernel MMD, the Median Heuristic and Distance Correlation in High Dimensions. CoRR abs/1406.2083 (2014) - [i14]Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Jeff G. Schneider:
Fast Function to Function Regression. CoRR abs/1410.7414 (2014) - [i13]Zoltán Szabó, Arthur Gretton, Barnabás Póczos, Bharath K. Sriperumbudur:
Learning Theory for Distribution Regression. CoRR abs/1411.2066 (2014) - [i12]Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, Larry A. Wasserman, James M. Robins:
Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations. CoRR abs/1411.4342 (2014) - [i11]Aaditya Ramdas, Sashank J. Reddi, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
On the High-dimensional Power of Linear-time Kernel Two-Sample Testing under Mean-difference Alternatives. CoRR abs/1411.6314 (2014) - 2013
- [c30]Barnabás Póczos, Aarti Singh, Alessandro Rinaldo, Larry A. Wasserman:
Distribution-Free Distribution Regression. AISTATS 2013: 507-515 - [c29]Liang Xiong, Barnabás Póczos, Jeff G. Schneider:
Efficient Learning on Point Sets. ICDM 2013: 847-856 - [c28]Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider:
Distribution to Distribution Regression. ICML (3) 2013: 1049-1057 - [c27]Sashank J. Reddi, Barnabás Póczos:
Scale Invariant Conditional Dependence Measures. ICML (3) 2013: 1355-1363 - [c26]Danica J. Sutherland, Barnabás Póczos, Jeff G. Schneider:
Active learning and search on low-rank matrices. KDD 2013: 212-220 - [i10]Barnabás Póczos, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman:
Distribution-Free Distribution Regression. CoRR abs/1302.0082 (2013) - [i9]Junier B. Oliva, Barnabás Póczos, Timothy D. Verstynen, Aarti Singh, Jeff G. Schneider, Fang-Cheng Yeh, Wen-Yih Isaac Tseng:
FuSSO: Functional Shrinkage and Selection Operator. CoRR abs/1311.2234 (2013) - [i8]Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing:
Fast Distribution To Real Regression. CoRR abs/1311.2236 (2013) - 2012
- [j7]Zoltán Szabó, Barnabás Póczos, András Lörincz:
Separation theorem for independent subspace analysis and its consequences. Pattern Recognit. 45(4): 1782-1791 (2012) - [c25]Barnabás Póczos, Liang Xiong, Danica J. Sutherland, Jeff G. Schneider:
Nonparametric kernel estimators for image classification. CVPR 2012: 2989-2996 - [c24]Zoltán Szabó, Barnabás Póczos, András Lörincz:
Collaborative Filtering via Group-Structured Dictionary Learning. LVA/ICA 2012: 247-254 - [c23]Barnabás Póczos, Zoubin Ghahramani, Jeff G. Schneider:
Copula-based Kernel Dependency Measures. ICML 2012 - [c22]Barnabás Póczos, Jeff G. Schneider:
Nonparametric Estimation of Conditional Information and Divergences. AISTATS 2012: 914-923 - [i7]Zoltán Szabó, Barnabás Póczos, András Lörincz:
Collaborative Filtering via Group-Structured Dictionary Learning. CoRR abs/1201.0341 (2012) - [i6]Barnabás Póczos, Liang Xiong, Danica J. Sutherland, Jeff G. Schneider:
Support Distribution Machines. CoRR abs/1202.0302 (2012) - [i5]Barnabás Póczos, Liang Xiong, Jeff G. Schneider:
Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions. CoRR abs/1202.3758 (2012) - [i4]Barnabás Póczos, Zoubin Ghahramani, Jeff G. Schneider:
Copula-based Kernel Dependency Measures. CoRR abs/1206.4682 (2012) - 2011
- [c21]Zoltán Szabó, Barnabás Póczos, András Lörincz:
Online group-structured dictionary learning. CVPR 2011: 2865-2872 - [c20]Barnabás Póczos, Zoltán Szabó, Jeff G. Schneider:
Nonparametric divergence estimators for independent subspace analysis. EUSIPCO 2011: 1718-1722 - [c19]Zoltán Szabó, Barnabás Póczos:
Nonparametric independent process analysis. EUSIPCO 2011: 1849-1853 - [c18]Liang Xiong, Barnabás Póczos, Jeff G. Schneider:
Group Anomaly Detection using Flexible Genre Models. NIPS 2011: 1071-1079 - [c17]Barnabás Póczos, Liang Xiong, Jeff G. Schneider:
Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions. UAI 2011: 599-608 - [c16]Barnabás Póczos, Jeff G. Schneider:
On the Estimation of alpha-Divergences. AISTATS 2011: 609-617 - [c15]Liang Xiong, Barnabás Póczos, Jeff G. Schneider, Andrew J. Connolly, Jake VanderPlas:
Hierarchical Probabilistic Models for Group Anomaly Detection. AISTATS 2011: 789-797 - 2010
- [j6]Zoltán Szabó, Barnabás Póczos, András Lörincz:
Auto-regressive independent process analysis without combinatorial efforts. Pattern Anal. Appl. 13(1): 1-13 (2010) - [c14]Siamak (Moshen) Ravanbakhsh, Barnabás Póczos, Russell Greiner:
A Cross-Entropy Method that Optimizes Partially Decomposable Problems: A New Way to Interpret NMR Spectra. AAAI 2010: 1280-1286 - [c13]Liuyang Li, Barnabás Póczos, Csaba Szepesvári, Russell Greiner:
Budgeted Distribution Learning of Belief Net Parameters. ICML 2010: 879-886 - [c12]Dávid Pál, Barnabás Póczos, Csaba Szepesvári:
Estimation of Renyi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs. NIPS 2010: 1849-1857 - [c11]Barnabás Póczos, Sergey Kirshner, Csaba Szepesvári:
REGO: Rank-based Estimation of Renyi Information using Euclidean Graph Optimization. AISTATS 2010: 605-612 - [i3]Dávid Pál, Barnabás Póczos, Csaba Szepesvári:
Estimation of Rényi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs. CoRR abs/1003.1954 (2010)
2000 – 2009
- 2009
- [j5]Barnabás Póczos, András Lörincz:
Identification of Recurrent Neural Networks by Bayesian Interrogation Techniques. J. Mach. Learn. Res. 10: 515-554 (2009) - [c10]Barnabás Póczos, Yasin Abbasi-Yadkori, Csaba Szepesvári, Russell Greiner, Nathan R. Sturtevant:
Learning when to stop thinking and do something! ICML 2009: 825-832 - 2008
- [c9]Sergey Kirshner, Barnabás Póczos:
ICA and ISA using Schweizer-Wolff measure of dependence. ICML 2008: 464-471 - [i2]Barnabás Póczos, András Lörincz:
D-optimal Bayesian Interrogation for Parameter and Noise Identification of Recurrent Neural Networks. CoRR abs/0801.1883 (2008) - 2007
- [j4]Zoltán Szabó, Barnabás Póczos, András Lörincz:
Undercomplete Blind Subspace Deconvolution. J. Mach. Learn. Res. 8: 1063-1095 (2007) - [c8]Zoltán Szabó, Barnabás Póczos, András Lörincz:
Undercomplete Blind Subspace Deconvolution Via Linear Prediction. ECML 2007: 740-747 - [c7]Barnabás Póczos, Zoltán Szabó, Melinda Kiszlinger, András Lörincz:
Independent Process Analysis Without a Priori Dimensional Information. ICA 2007: 252-259 - [c6]Zoltán Szabó, Barnabás Póczos, Gábor Szirtes, András Lörincz:
Post Nonlinear Independent Subspace Analysis. ICANN (1) 2007: 677-686 - 2006
- [j3]Barnabás Póczos, András Lörincz:
Non-combinatorial estimation of independent autoregressive sources. Neurocomputing 69(16-18): 2416-2419 (2006) - [c5]Zoltán Szabó, Barnabás Póczos, András Lörincz:
Cross-Entropy Optimization for Independent Process Analysis. ICA 2006: 909-916 - 2005
- [j2]Gábor Szirtes, Barnabás Póczos, András Lörincz:
Neural Kalman filter. Neurocomputing 65-66: 349-355 (2005) - [c4]Barnabás Póczos, Bálint Takács, András Lörincz:
Independent Subspace Analysis on Innovations. ECML 2005: 698-706 - [c3]Barnabás Póczos, András Lörincz:
Independent Subspace Analysis Using k-Nearest Neighborhood Distances. ICANN (2) 2005: 163-168 - [c2]Barnabás Póczos, András Lörincz:
Independent subspace analysis using geodesic spanning trees. ICML 2005: 673-680 - 2003
- [j1]András Lörincz, Barnabás Póczos:
Cost Component Analysis. Int. J. Neural Syst. 13(3): 183-192 (2003) - [i1]Barnabás Póczos, András Lörincz:
Kalman-filtering using local interactions. CoRR cs.AI/0302039 (2003) - 2002
- [c1]Botond Szatmáry, Barnabás Póczos, Julian Eggert, Edgar Körner, András Lörincz:
Non-negative matrix factorization extended by sparse code shrinkage and weight sparsification non-negative matrix factorization algorithms. ECAI 2002: 503-507
Coauthor Index
Simon S. Du
aka: Simon Shaolei Du
aka: Simon Shaolei Du
András Lörincz
aka: András Lorincz
aka: András Lorincz
Emmanouil A. Platanios
aka: Emmanouil Antonios Platanios
aka: Emmanouil Antonios Platanios
Siamak Ravanbakhsh
aka: Siamak (Moshen) Ravanbakhsh
aka: Siamak (Moshen) Ravanbakhsh
Sashank J. Reddi
aka: Sashank Jakkam Reddi
aka: Sashank Jakkam Reddi
[j14] [c115] [j13] [c98] [i85] [i81] [i76] [c91] [c86] [c83] [i69] [i64] [j11] [c81] [c78] [c77] [c76] [c75] [i52] [i51] [i46] [i44] [i41] [c66] [c65] [c64] [c63] [c58] [c57] [c55] [c50] [i39] [i36] [i27] [i26] [i24] [c44] [c42] [c41] [i22] [i18] [i17] [c37] [c36] [i14] [c29] [c28] [c26] [i9] [i8] [c25] [c23] [c22] [i6] [i5] [i4] [c20] [c18] [c17] [c16] [c15]
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