Michal Valko : Research
My Google Scholar profile, DeepMind profile, and HAL profile.

2020

  • Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, Michal Valko: Bootstrap Your Own Latent: A new approach to self-supervised learning, bibtex abstract abstract yt by Yannic twitter code
  • Pierre Ménard, Omar Darwiche Domingues, Emilie Kaufmann, Anders Jonsson, Edouard Leurent, Michal Valko: Fast active learning for pure exploration in reinforcement learning, arXiv preprint, bibtex abstract abstract
  • Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric: A provably efficient sample collection strategy for reinforcement learning arXiv preprint, bibtex abstract abstract
  • Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko: A kernel-based approach to non-stationary reinforcement learning in metric spaces, in Theoretical Foundations of RL Workshop @ ICML 2020 [oral - 6% acceptance rate] (ICML 2020 - RL Theory) arXiv preprint video
  • Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric: Reward-free Exploration beyond finite-horizon, in Theoretical Foundations of RL Workshop @ ICML 2020 (ICML 2020 - RL Theory) video
  • Daniele Calandriello*, Michał Dereziński*, Michal Valko: Sampling from a k-DPP without looking at all items, arXiv preprint
  • Pierre Perrault, Etienne Boursier, Vianney Perchet, Michal Valko: Statistical efficiency of Thompson sampling for combinatorial semi-bandits, arXiv preprint bibtex abstract abstract
  • Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Edouard Leurent, Michal Valko: Adaptive reward-free exploration, in Theoretical Foundations of RL Workshop @ ICML 2020 (ICML 2020 - RL Theory) arXiv preprint, video bibtex abstract abstract
  • Anders Jonsson, Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Edouard Leurent, Michal Valko: Planning in Markov decision processes with gap-dependent sample complexity, arXiv preprint bibtex
  • Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko: Regret bounds for kernel-based reinforcement learning, arXiv preprint bibtex abstract abstract
  • Guillaume Gautier, Rémi Bardenet, Michal Valko: Fast sampling from β-ensembles, arXiv preprint bibtex abstract abstract
  • Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Rémi Munos: Monte-Carlo tree search as regularized policy optimization, in International Conference on Machine Learning (ICML 2020) talk arXiv preprint
  • Yunhao Tang, Michal Valko, Rémi Munos: Taylor expansion policy optimization, in International Conference on Machine Learning (ICML 2020) arXiv preprint talk
  • Rémy Degenne, Pierre Ménard, Xuedong Shang, Michal Valko: Gamification of pure exploration for linear bandits, in International Conference on Machine Learning (ICML 2020) arXiv preprint talk
  • Pierre Perrault, Zheng Wen, Jennifer Healey, Michal Valko: Budgeted online influence maximization, in International Conference on Machine Learning (ICML 2020)
  • Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric: No-regret exploration in goal-oriented reinforcement learning, in International Conference on Machine Learning (ICML 2020) arXiv preprint talk
  • Aadirupa Saha, Pierre Gaillard, Michal Valko: Improved sleeping bandits with stochastic action sets and adversarial rewards, in International Conference on Machine Learning (ICML 2020) arXiv preprint talk
  • Anne Manegueu, Claire Vernade, Alexandra Carpentier, Michal Valko: Delayed bandits with different and unknown delay distributions with unbounded support, in International Conference on Machine Learning (ICML 2020) and (GPSD 2O2O) and (WiML 2019) arXiv preprint
  • Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco: Near-linear time Gaussian process optimization with adaptive batching and resparsification, in International Conference on Machine Learning (ICML 2020) and (OPT 2019) arXiv preprint talk
  • Pierre Perrault, Vianney Perchet, Michal Valko: Covariance-adapting algorithm for semi-bandits with application to sparse rewards, in Conference on Learning Theory (COLT 2020), video bibtex abstract abstract
  • Xuedong Shang, Rianne de Heide, Emilie Kaufmann, Pierre Ménard, Michal Valko: Fixed-confidence guarantees for Bayesian best-arm identification, in International Conference on Artificial Intelligence and Statistics (AISTATS 2020) talk arXiv preprint
  • Côme Fiegel, Victor Gabillon, Michal Valko: Adaptive multi-fidelity optimization with fast learning rates in International Conference on Artificial Intelligence and Statistics (AISTATS 2020)
  • Victor Gabillon, Rasul Tutunov, Michal Valko, Haitham Bou Ammar: Derivative-free & order-robust optimisation in International Conference on Artificial Intelligence and Statistics (AISTATS 2020) talk
  • Julien Seznec, Pierre Ménard, Alessandro Lazaric, Michal Valko: A single algorithm for both restless and rested rotting bandits, in International Conference on Artificial Intelligence and Statistics (AISTATS 2020) talk
  • Tomáš Kocák, Rémi Munos, Branislav Kveton, Shipra Agrawal, Michal Valko: Spectral Bandits, accepted for publication to Journal of Machine Learning Research (JMLR 2020)
  • Branislav Kveton, Zheng Wen, Azin Ashkan, Michal Valko: Learning to Act Greedily: Polymatroid Semi-Bandits, accepted for publication to Journal of Machine Learning Research (JMLR 2020) bibtex abstract abstract arXiv preprint

2019

  • Jean-Bastien Grill*, Omar Darwiche Domingues*, Pierre Ménard, Rémi Munos, Michal Valko: Planning in entropy-regularized Markov decision processes and games, in Neural Information Processing Systems (NeurIPS 2019) bibtex
  • Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Pérolat, Michal Valko, Georgios Piliouras, Rémi Munos: Multiagent evaluation under incomplete information, in Neural Information Processing Systems (NeurIPS 2019) bibtex
  • Michał Dereziński*, Daniele Calandriello*, Michal Valko: Exact sampling of determinantal point processes with sublinear time preprocessing, in Neural Information Processing Systems (NeurIPS 2019) and (ICML 2019 - NEGDEP) bibtex abstract abstract talk video
  • Guillaume Gautier, Rémi Bardenet, Michal Valko: On two ways to use determinantal point processes for Monte Carlo integration, in Neural Information Processing Systems (NeurIPS 2019) and (ICML 2019 - NEGDEP) bibtex video
  • Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco: Gaussian process optimization with adaptive sketching: Scalable and no regret, in Conference on Learning Theory (COLT 2019) and (ICML 2019 - NEGDEP) and (SWSL 2019) bibtex abstract abstract video talk poster
  • Peter Bartlett, Victor Gabillon, Jennifer Healey, Michal Valko: Scale-free adaptive planning for deterministic dynamics & discounted rewards in International Conference on Machine Learning (ICML 2019) bibtex abstract abstract video talk poster
  • Pierre Perrault, Vianney Perchet, Michal Valko: Exploiting structure of uncertainty for efficient matroid semi-bandits, in International Conference on Machine Learning (ICML 2019) bibtex abstract abstract video talk poster
  • Xuedong Shang, Emilie Kaufmann, Michal Valko: A simple dynamic bandit-based algorithm for hyper-parameter tuning, in shop on Automated Machine Learning at International Conference on Machine Learning (ICML 2019 - AutoML) bibtex abstract abstract poster code
  • Guillaume Gautier, Rémi Bardenet, Michal Valko: DPPy: Sampling determinantal point processes with Python, Journal of Machine Learning Research (JMLR 2019) bibtex abstract abstract arXiv preprint
  • Julien Seznec, Andrea Locatelli, Alexandra Carpentier, Alessandro Lazaric, Michal Valko: Rotting bandits are not harder than stochastic ones, in International Conference on Artificial Intelligence and Statistics (AISTATS 2019) bibtex abstract abstract talk poster [full oral presentation - 2.5% acceptance rate]
  • Andrea Locatelli, Alexandra Carpentier, Michal Valko: Active multiple matrix completion with adaptive confidence sets, in International Conference on Artificial Intelligence and Statistics (AISTATS 2019) bibtex abstract abstract talk poster
  • Pierre Perrault, Vianney Perchet, Michal Valko: Finding the bandit in a graph: Sequential search-and-stop, in International Conference on Artificial Intelligence and Statistics (AISTATS 2019) bibtex abstract abstract poster
  • Peter L. Bartlett, Victor Gabillon, Michal Valko: A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption, in Algorithmic Learning Theory (ALT 2019) bibtex abstract abstract talk 1 talk 2
  • Xuedong Shang, Emilie Kaufmann, Michal Valko: General parallel optimization without metric, in Algorithmic Learning Theory (ALT 2019) bibtex abstract abstract talk
  • Guillaume Gautier, Rémi Bardenet, Michal Valko: Les processus ponctuels déterminantaux en apprentissage automatique bibtex abstract abstract (Gretsi 2019)

2018

  • Jean-Bastien Grill, Michal Valko, Rémi Munos: Optimistic optimization of a Brownian, in Neural Information Processing Systems (NeurIPS 2018) bibtex abstract abstract poster
  • Xuedong Shang, Emilie Kaufmann, Michal Valko: Adaptive black-box optimization got easier: HCT needs only local smoothness, in European Workshop on Reinforcement Learning (EWRL 2018) bibtex abstract abstract poster
  • Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko, Michal Valko: Compressing the input for CNNs with the first-order scattering transform, in European Conference on Computer Vision (ECCV 2018) bibtex abstract abstract poster
  • Daniele Calandriello, Ioannis Koutis, Alessandro Lazaric, Michal Valko: Improved large-scale graph learning through ridge spectral sparsification, in International Conference on Machine Learning (ICML 2018) bibtex abstract abstract talk poster
  • Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon, Alan Malek, Michal Valko: Best of both worlds: Stochastic & adversarial best-arm identification, Conference on Learning Theory (COLT 2018) bibtex abstract abstract video talk poster

2017

  • Daniele Calandriello, Alessandro Lazaric, Michal Valko: Efficient second-order online kernel learning with adaptive embedding, in Neural Information Processing Systems (NeurIPS 2017) bibtex abstract abstract talk poster
  • Zheng Wen, Branislav Kveton , Michal Valko, Sharan Vaswani: Online influence maximization under independent cascade model with semi-bandit feedback, in Neural Information Processing Systems (NeurIPS 2017) bibtex abstract abstract
  • Daniele Calandriello, Alessandro Lazaric, Michal Valko: Second-order kernel online convex optimization with adaptive sketching, in International Conference on Machine Learning (ICML 2017) bibtex abstract abstract talk poster
  • Guillaume Gautier, Rémi Bardenet, Michal Valko: Zonotope hit-and-run for efficient sampling from projection DPPs, in International Conference on Machine Learning (ICML 2017) bibtex abstract abstract talk poster
  • Daniele Calandriello, Alessandro Lazaric, Michal Valko: Distributed adaptive sampling for kernel matrix approximation, in International Conference on Artificial Intelligence and Statistics (AISTATS 2017) and (ICML 2017 - LL) bibtex abstract abstract talk code poster
  • Akram Erraqabi, Alessandro Lazaric, Michal Valko, Emma Brunskill, Yu-En Liu: Trading off rewards and errors in multi-armed bandits, in International Conference on Artificial Intelligence and Statistics (AISTATS 2017) bibtex abstract abstract poster

2016

  • Michal Valko: Bandits on graphs and structures, habilitation thesis, École normale supérieure de Cachan (ENS Cachan 2016) bibtex abstract abstract
  • Jean-Bastien Grill, Michal Valko, Rémi Munos: Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning, in Neural Information Processing Systems (NeurIPS 2016) bibtex abstract abstract talk poster [full oral presentation - 1.8% acceptance rate]
  • Daniele Calandriello, Alessandro Lazaric, Michal Valko: Pack only the essentials: Adaptive dictionary learning for kernel ridge regression, in Adaptive and Scalable Nonparametric Methods in Machine Learning at Neural Information Processing Systems (NeurIPS 2016 - ASNMML) bibtex abstract abstract poster
  • Akram Erraqabi, Alessandro Lazaric, Michal Valko, Emma Brunskill, Yu-En L@iu: Rewards and Errors in Multi-armed Bandit for Interactive Education, in Challenges in Machine Learning: Learning and Education workshop at Neural Information Processing Systems (NeurIPS 2016 - CIML) bibtex abstract abstract poster
  • Akram Erraqabi, Michal Valko, Alexandra Carpentier, Odalric-Ambrym Maillard: Pliable rejection sampling, in International Conference on Machine Learning (ICML 2016) bibtex abstract abstract talk long talk poster
  • Daniele Calandriello, Alessandro Lazaric, Michal Valko: Analysis of Nyström method with sequential ridge leverage scores, in Uncertainty in Artificial Intelligence (UAI 2016) bibtex abstract abstract poster spotlight
  • Tomáš Kocák, Gergely Neu, Michal Valko: Online learning with Erdős-Rényi side-observation graphs, in Uncertainty in Artificial Intelligence (UAI 2016) bibtex abstract abstract poster spotlight
  • Mohammad Ghavamzadeh, Yaakov Engel, Michal Valko: Bayesian policy gradient and actor-critic algorithms, Journal of Machine Learning Research (JMLR 2016) bibtex abstract abstract code
  • Tomáš Kocák, Gergely Neu, Michal Valko: Online learning with noisy side observations, in International Conference on Artificial Intelligence and Statistics (AISTATS 2016) bibtex abstract abstract talk poster [full oral presentation - 6% acceptance rate]
  • Alexandra Carpentier, Michal Valko: Revealing graph bandits for maximizing local influence, in International Conference on Artificial Intelligence and Statistics (AISTATS 2016) bibtex abstract abstract poster

2015

  • Jean-Bastien Grill, Michal Valko, Rémi Munos: Black-box optimization of noisy functions with unknown smoothness, in Neural Information Processing Systems (NeurIPS 2015) bibtex abstract abstract code, code in R poster
  • Alexandra Carpentier, Michal Valko: Simple regret for infinitely many armed bandits, in International Conference on Machine Learning (ICML 2015) bibtex abstract abstract talk poster arXiv
  • Manjesh Hanawal, Venkatesh Saligrama, Michal Valko, Rémi Munos: Cheap Bandits, in International Conference on Machine Learning (ICML 2015) bibtex abstract abstract talk poster
  • Daniele Calandriello, Alessandro Lazaric, Michal Valko: Large-scale semi-supervised learning with online spectral graph sparsification, in Resource-Efficient Machine Learning workshop at International Conference on Machine Learning (ICML 2015 - REML) bibtex abstract abstract poster
  • Julien Audiffren, Michal Valko, Alessandro Lazaric, Mohammad Ghavamzadeh: Maximum Entropy Semi-Supervised Inverse Reinforcement Learning, in International Joint Conferences on Artificial Intelligence (IJCAI 2015) bibtex abstract abstract talk poster

2014

  • Tomáš Kocák, Gergely Neu, Michal Valko, Rémi Munos: Efficient Learning by Implicit Exploration in Bandit Problems with Side Observations, in Neural Information Processing Systems (NeurIPS 2014) bibtex abstract abstracttalk poster
  • Alexandra Carpentier, Michal Valko: Extreme Bandits, in Neural Information Processing Systems (NeurIPS 2014) bibtex abstract abstractposter
  • Gergely Neu, Michal Valko: Online Combinatorial Optimization with Stochastic Decision Sets and Adversarial Losses, in Neural Information Processing Systems (NeurIPS 2014) bibtex abstract abstracttalk poster
  • Michal Valko, Rémi Munos, Branislav Kveton, Tomáš Kocák: Spectral Bandits for Smooth Graph Functions, in International Conference on Machine Learning (ICML 2014) bibtex abstract abstractslides poster
  • Tomáš Kocák, Michal Valko, Rémi Munos, Shipra Agrawal: Spectral Thompson Sampling, in AAAI Conference on Artificial Intelligence (AAAI 2014) bibtex abstract abstractslides poster
  • Philippe Preux, Rémi Munos, Michal Valko: Bandits attack function optimization, in IEEE Congress on Evolutionary Computation (CEC 2014) bibtex abstract abstract
  • Julien Audiffren, Michal Valko, Alessandro Lazaric, Mohammad Ghavamzadeh: MESSI: Maximum Entropy Semi-Supervised Inverse Reinforcement Learning, in NIPS Workshop on Novel Trends and Applications in Reinforcement Learning (NeurIPS 2014 - TCRL) bibtex abstract abstract
  • Tomáš Kocák, Michal Valko, Rémi Munos, Branislav Kveton, Shipra Agrawal: Spectral Bandits for Smooth Graph Functions with Applications in Recommender Systems, in AAAI Workshop on Sequential Decision-Making with Big Data (AAAI 2014 - SDMBD) bibtex abstract abstract

2013

  • Michal Valko, Alexandra Carpentier, Rémi Munos: Stochastic Simultaneous Optimistic Optimization, in International Conference on Machine Learning (ICML 2013) bibtex abstract abstract demo code, code in R slides poster talk
  • Michal Valko, Nathan Korda, Rémi Munos, Ilias Flaounas, Nello Cristianini: Finite-Time Analysis of Kernelised Contextual Bandits, in Uncertainty in Artificial Intelligence (UAI 2013) and (JFPDA 2013). bibtex abstract abstract poster spotlight code
  • Branislav Kveton, Michal Valko: Learning from a Single Labeled Face and a Stream of Unlabeled Data, in IEEE International Conference on Automatic Face and Gesture Recognition (FG 2013) [spotlight] bibtex abstract abstract
  • Milos Hauskrecht, Iyad Batal, Michal Valko, Shyam Visweswaran, Gregory F. Cooper, Gilles Clermont: Outlier detection for patient monitoring and alerting, in Journal of Biomedical Informatics (JBI 2013) bibtex abstract abstract

2012

  • Michal Valko, Mohammad Ghavamzadeh, Alessandro Lazaric: Semi-supervised apprenticeship learning, in Journal of Machine Learning Research Workshop and Conference Proceedings: European Workshop on Reinforcement Learning (EWRL 2012) bibtex abstract abstracttalk poster

2011

  • Michal Valko, Branislav Kveton, Hamed Valizadegan, Gregory F. Cooper, Milos Hauskrecht: Conditional Anomaly Detection with Soft Harmonic Functions, in International Conference on Data Mining (ICDM 2011) bibtex abstract abstract
  • Thomas C. Hart, Patricia M. Corby, Milos Hauskrecht, Ok Hee Ryu, Richard Pelikan, Michal Valko, Maria B. Oliveira, Gerald T. Hoehn, and Walter A. Bretz: Identification of Microbial and Proteomic Biomedicalkers in Early Childhood Caries in International Journal of Dentistry (IJD 2011) bibtex abstract abstract
  • Michal Valko: Adaptive Graph-Based Algorithms for Conditional Anomaly Detection and Semi-Supervised Learning, PhD thesis, University of Pittsburgh (PITT 2011) bibtex abstract abstract
  • Michal Valko, Hamed Valizadegan, Branislav Kveton, Gregory F. Cooper, Milos Hauskrecht: Conditional Anomaly Detection Using Soft Harmonic Functions: An Application to Clinical Alerting, Workshop on Machine Learning for Global Challenges in International Conference on Machine Learning (ICML 2011 - Global) bibtex abstract abstract poster spotlight

2010

  • Michal Valko, Branislav Kveton, Ling Huang, Daniel Ting: Online Semi-Supervised Learning on Quantized Graphs in Uncertainty in Artificial Intelligence (UAI 2010) bibtex abstract abstract Video: Adaptation, Video: OfficeSpace, spotlight poster
  • Branislav Kveton, Michal Valko, Ali Rahimi, Ling Huang: Semi-Supervised Learning with Max-Margin Graph Cuts in International Conference on Artificial Intelligence and Statistics (AISTATS 2010) bibtex abstract abstract
  • Milos Hauskrecht, Michal Valko, Shyam Visweswaram, Iyad Batal, Gilles Clermont, Gregory Cooper: Conditional Outlier Detection for Clinical Alerting in Annual American Medical Informatics Association conference (AMIA 2010) bibtex abstract abstract [Homer Warner Best Paper Award]
  • Branislav Kveton, Michal Valko, Matthai Phillipose, Ling Huang: Online Semi-Supervised Perception: Real-Time Learning without Explicit Feedback in IEEE Online Learning for Computer Vision Workshop in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010 - OLCV) [best paper Google Award] bibtex abstract abstract
  • Michal Valko, Milos Hauskrecht: Feature importance analysis for patient management decisions in International Congress on Medical Informatics (MEDINFO 2010) bibtex abstract abstract

2008

  • Michal Valko, Gregory Cooper, Amy Seybert, Shyam Visweswaran, Melissa Saul, Milos Hauskrecht: Conditional anomaly detection methods for patient-management alert systems, Workshop on Machine Learning in Health Care Applications in International Conference on Machine Learning (ICML-2008 - MLHealth) bibtex abstract abstract talk
  • Michal Valko, Milos Hauskrecht: Distance metric learning for conditional anomaly detection, International Florida AI Research Society Conference (FLAIRS 2008) bibtex abstract abstract
  • Michal Valko, Richard Pelikan, Milos Hauskrecht: Learning predictive models for combinations of heterogeneous proteomic data sources, AMIA Summit on Translational Bioinformatics (STB 2008) [outstanding paper award] bibtex abstract abstracttalk

2007

  • Milos Hauskrecht, Michal Valko, Branislav Kveton, Shyam Visweswaram, Gregory Cooper: Evidence-based Anomaly Detection in Clinical Domains in Annual American Medical Informatics Association conference (AMIA 2007). [nominated for the best paper award] bibtex abstract abstract

2006

  • Wendy W. Chapman, John N. Dowling, Gregory F. Cooper, Milos Hauskrecht and Michal Valko: A Comparison of Chief Complaints and Emergency Department Reports for Identifying Patients with Acute Lower Respiratory Syndrome in Proceedings of the National Syndromic Surveillance Conference (ISDS 2006) bibtex abstract abstract
  • Miloš Hauskrecht, Richard Pelikan, Michal Valko, James Lyons-Weiler: Feature Selection and Dimensionality Reduction in Genomics and Proteomics. Fundamentals of Data Mining in Genomics and Proteomics, eds. Berrar, Dubitzky, Granzow. Springer (2006) bibtex abstract abstract

2005

  • Michal Valko, Nuno C. Marques, Marco Castelani: Evolutionary Feature Selection for Spiking Neural Network Pattern Classifiers in Proceedings of Portuguese Conference on Artificial Intelligence (EPIA 2005), eds. Bento et al., IEEE, pages 24-32. bibtex abstract abstract
  • Michal Valko Evolving Neural Networks for Statistical Decision Theory, Comenius University, Bratislava, 2005 (master thesis) (2005) Advisor: Radoslav Harman thesis@sk bibtex abstract abstract talk

Presentations

  • Michal Valko: Graph-Based Anomaly Detection with Soft Harmonic Functions: Presented at CS Department Research Competition (Research 2011) [#1st place] talk also at (Grad Expo 2011) and (CS DAY 2011) poster
  • Branislav Kveton, Michal Valko, Matthai Philiposse: Real-Time Adaptive Face Recognition, Presented at 23rd Neural Information Processing Systems conference (NeurIPS 2009), Video: Adaptation, Video: OfficeSpace, poster #1, poster #2
  • Michal Valko:, Branislav Kveton, Matthai Philiposse: Robust Face Recognition Using Online Learning, Presented at 9th University of Pittsburgh Science conference (SCIENCE 2009) Live Demonstration (Grad Expo 2010) talk and (CS Day 2010) poster
  • Michal Valko: Conditional anomaly detection with adaptive similarity metric: Presented at CS Department Research Competition (Research 2008) [#1st place] talk
  • Michal Valko, Milos Hauskrecht, G. Cooper, S. Visweswaran, M. Saul, A. Seybert, J. Harrison, A. Post: Conditional Anomaly Detection, Presented at (CS Day 2008) [#1st by people, #2nd by faculty] also at University of Pittsburgh, Arts & Sciences (Grad Expo 2008) poster

References

24-Apr-2020