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- [5 June, 2022] EcoSta 2022, Kyoto, Session EO349 [ Slides ]
- [28 Apr, 2022] IST Lisbon, MPML ("Mathematics, Physics, and Machine Learning") Seminar Series [ Slides ]
- [17 May, 2022] OIST course 'Foundations of ML' starts
- [13 Apr, 2022] EPFL CIS - RIKEN AIP Joint Seminar Series [ Slides ] [ Video ]
[12 Apr, 2022] 2022 SIAM conference on Uncertainty Quantification Cancelled!
- [24 Mar, 2022] Talk at MLT__init__ [ Slides ]
- [16 Mar, 2022] Cambridge CBL Reading Group [ Slides ]
- [11 Mar, 2022] DeepMind/ELLIS CSML Seminar Series at UCL [ Slides ] [ Video ]
- [1 Mar, 2022] ATR-AIP Joint Seminar on Neuroscience-Inspired AI [ Slides ] [ Video ]
- [22 Feb, 2022] AI4Sec Seminar Series at Huwaei Research Munich [ Slides ] [ Summary ]
- [17 Feb, 2022] StatML CDT seminar at Imperial College London and University of Oxford [ Slides ] [ Summary ]
- [14 Dec, 2021] Invited talk at the NeurIPs Bayesian DL workshop [ Slides ]
- [13 Dec, 2021] I was an invited panelist at the NeurIPs "I can't believe it's not better" workshop
- [6-14 Dec, 2021] Along with Freddie Kalaitzis and Wenkai Xu, I organized "Well-Being in ML" sessions through MeMentor
- [6 Dec, 2021] Meetups at NeurIPS 2021
- [6 Dec, 2021] I was a session chair for the Message-Passing tutorial by Wee-Sun Lee
- [5 Dec, 2021] We won the NeurIPS competition on Approximate Inference in Bayesian Deep Learning!
- [9 Nov, 2021] Tutorial at ACML2021 [ Slides ] [ Summary ]
- [1 Oct, 2021] Our Bayes-duality project is launched with a funding of $2.76 million by JST-ANR's CREST proposal
- [29 Sep, 2021] Two papers accepted at NeurIPS 2021 (acceptance rate 26%)
- [21 Sep, 2021] Invited talk at Tübingen AI Center (Philipp Hennig's group)
- [17 Sep, 2021] Invited talk at Secondmind [ Slides ]
- [16 Sep, 2021] Invited talk at Durham CDT Data Science Workshop [ Slides ]
- [15 Aug, 2021] Invited talk at KDD workshop 2021 Model Mining [ Slides ]
- [23 Jul, 2021] Invited talk at Theory and Foundation of Continual Learning [ Slides ] [ SlidesLive Video ]
- [9 Jul, 2021] New paper on the Bayesian Learning Rule
- [13 May, 2021] One paper on "Subset of Data for Deep GPs" accepted at UAI 2021 (acceptance rate 26%)
- [8 May, 2021] Two papers accepted at ICML 2021 (acceptance rate 21.4%)
- [10 Mar, 2021] AIP open seminar [ Video ] [ Slides ]
- [9 Feb, 2021] Invited speaker at ML for Soft Matter 2021 [ Slides ]
- [9 Feb, 2021] Talk at the CREST meeting in Tokyo Tech
- [21 Dec, 2020] Invited at CMStatistics 2020
- [6 Dec, 2020] MeMentor, a mentorship portal for ML, is launched at NeurIPS 2020
- [17 Nov, 2020] CoSy Seminars, Uppsala University
- [9 Nov, 2020] New team webpage launched
- [5 Nov, 2020] TU Darmstadt [ Slides ]
- [3 Nov, 2020] Waterlool AI Institute
- [28 Sep, 2020] Our Continual Deep Learning paper is accepted at NeurIPS 2020 for an oral preseentation (1% of all submissions)
- [17 Aug, 2020] New tutorial on DL with Bayes at SMILES, 2020 [ slides ] [ video ]
- [20 Jul, 2020] New tutorial on DL with Bayes at SPCOM 2020 [ slides ] [ video ]
- (May 30, 2020) ICML 2020 paper on Binary Neural Nets
- (May 30, 2020) ICML 2020 paper on Improving Bayes Learing Rule
- (May 30, 2020) ICML 2020 paper on Imitation Learning
- [25 May, 2020] New paper on AI for Social good accepted in Nature Communication
- [12 May, 2020] New talk on DNN2GP
- [1 Apr, 2020] Received a Kakenhi Grant (Series B) on "life-long learning" (approx. USD 158K, 2020-2023)
- (Mar 23-27, 2020) Workshop on Statistics, Optimization and Machine Learning, Niseko (Hokkaido), Japan.
- (Mar 18, 2020) Advances in Information Geometry (AIG) 2020 at ISM, Tokyo.
- (Feb 25, 2020) At MLT meetup
- (Feb 14, 2020) PRML Winter School 2020, Seoul, Korea.
- (Feb 13, 2020) KIAS, Seoul, Korea.
- (Feb 12, 2020) KAIST, Seoul, Korea.
- (Dec, 2019) Community service summary of the year 2018 (total 62 papers): Area Chair for ICLR 2020 (16 papers), NeurIPS 2019 (21 papers), ACML 2019 (3 papers), ICML 2019 (7 papers). Reviewer for AISTATS 2020 (6 papers), ICASSP 2019 (5 papers) and Bayesian Analysis (1 paper), JMLR (1 paper), Neural Networks (1 paper), Bayesian analysis (1 paper)
- (Dec 16, 2019) Talk at UBC [ Slides ].
- (Dec 9, 2019) An early draft of our paper Learning Algorithms from Bayesian Principles.
- (Dec 9, 2019) NeurIPS 2019 Tutorial on "Deep Learning with Bayesian Principles" [ Slides ] [ Video on SlidesLive ] [ YouTube ] .
- (Nov 18, 2019) Stats and Data Science Workshop, KAUST [ Slides ].
- (Nov 12-15, 2019) Organizing "Conference on Data Science" at the Fields institute, Toronto. [ Slides ]
- (Oct 1, 2019) Talk at Neurotechnology and AI, 2019 at RIKEN-AIP [ Slides ].
- (Sep 23, 2019) MSR, Cambridge [ Slides ].
- (Sep 20, 2019) DeepMind, London [ Slides ].
- (Sep 19, 2019) Gatsby/UCL, London [ Slides ].
- (Sep 17, 2019) Turing Institute, Edinburgh [ Slides ].
- (Sep 16, 2019) Imperial College, London [ Slides ].
- (Sep 12, 2019) EPFL [ Slides ].
- (Sep 9, 2019) TU, Berlin.
- (Aug 22, 2019) Tokyo Tech. Workshop [ Slides ]
- (July 29, 2019) Talk at Discrete Optimization Workshop at RIKEN, AIP Tokyo. [ Slides ]
- (Jun 12, 2019) Lecture on Bayesian Deep Learning at UTokyo.
- (June 3, 2019) I gave an invited talk at the PEAR-AIP workshop in Taiwan.
- (Apr 24, 2019) 2 papers at ICML 2019 are accepted.
- (Apr 15, 2019) I organized PreAISTATS ML Seminar at RIKEN AIP (9 speakers).
- (Mar 19, 2019) Invited talk at the AIP Symposium [ Slides ]
- (Mar 15-16, 2019) Organizing IIT-Hyderabad and RIKEN-AIP joint workshop on ML and applications in Hyderabad, India.
- (Mar 4-8, 2019) Invited Talk at "Conference on Algorithms, Optimization and Learning in Dynamics Environments" in Hanoi, Vietnam [ Slides ]
- (Feb. 17-22, 2019) Organized Dagstuhl Seminar on "AI for Social Good" [ Press Release ]
- (Feb. 15, 2019) Invited talk at the conference on theoretical foundations of machine learning (TFML) in Krakow, Poland. [ Slides ]
- (Jan. 30, 2019) A lecture on "Fundamentals of ML" at TUAT.
- (Dec, 2018) Community service summary of the year 2018 (total 70 papers): Area Chair for AI-Stats 2019 (10 papers), ICLR 2019 (17 papers), NeurIPS 2018 (18 papers), ICML 2018 (20ish papers), and ACML 2018 (10ish). Reviewer for ICASSP 2019 (4 papers) and UAI 2018 (7 papers).
- (Dec. 2, 2018) Invited talk at AABI Symposium in NeurIPS 2018 [ Slides ]
- (Nov. 19, 2018) Talk at IIT Mumbai, India [ Slides ]
- (Nov. 5, 2018) Talk at IIIT Bangalore, India [ Slides ]
- (Nov. 2, 2018) Talk at IISc Bangalore, India [ Slides ]
- (Nov. 1, 2018) Talk at SUTD, Singapore [ Slides ]
- (Sep. 29, 2018) Invited paper presentation at ISITA 2018 [ Slides ]
(Sep. 24, 2018) Talk on "fast computation of uncertainty in deep learning" at the NUS-RIKEN Workshop in Singapore [ Slides ]
(Sep. 20, 2018) Talk on "fast computation of uncertainty in deep learning" at the Workshop "AI meets Life Sciences" in Karolinska institutet, Stockholm [ Slides ]
(Sep. 5, 2018) Talk at the UK-Japan AI Workshop at the UK Embassy in Tokyo [ Slides ]
(July 24, 2018) Talk at the First conference on Discrete Optimization and Machine Learning in Tokyo [ Slides ]
(July 16,17,18, 2018) Talks at University of Cambridge, University of Oxford, and DeepMind London [ Slides ]
(July 12, 2018) Talk at ICML 2018 [ Slides ]
(July 5, 2018) Talk at TU Berlin in Klaus-Robert Muller on Natural-Gradient Variational Inference [ Slides ]
(July 2, 2018) Talk at DTU Copenhagen in Ole Winther's group on Natural-Gradient Variational Inference [ Slides ]
(June 28-29, 2018) A 2-day tutorial on Approximate Bayesian Inference at the DS3 workshop.
(June 27, 2018) Talk at ENSAE Paris on Natural-Gradient Variational Inference.
(June 23, 2018) A lecture on Approximate Bayesian Inference at Waseda University.
(June 6, 2018) A lecture on Bayesian Deep Learning at University of Tokyo.
(May 8, 2018) New talk at UBC, CS on "Bayesian Deep Learning by Weight-Perturbation in Adam".
(Apr, 2018) I taught a course (8 lectures) on Fundamentals of Machine Learning in April at Waseda University.
(April 30, 2018) We presented our new paper on Variational Message Passing for Structured VAEs at ICLR 2018.
(Mar 19, 2018) I gave an invited Talk at the Tokyo Deep Learning Workshop.
(Mar 8, 2018) I visited Srijith P. K. at IIT, Hyderbad, and gave a talk on "Bayesian Deep Learning by Weight-Perturbation in Adam".
- (Feb 23, 2018) New paper on Variational Message Passing for Structured VAEs at ICLR 2018.
- (Feb 20, 2018) New paper on Bayesian nonparametric Poisson-Process Allocation for Time-Sequence Modeling.
- (Dec. 4, 2017) New paper on Vprop for variational inference using RMSprop's implementation.
- (Dec. 1, 2017) Our new method CVI is implemented in the GPML toolbox. Thanks to Hannes Nickisch.
- (Dec 2017) In 2017, I was an area chair for NIPS 2017, a reviewer for ICLR-2018, AAAI-2018, ICML-2017 and UAI-2017, and an action-editor for JMLR. I reviewed a total 54 papers in 2017!
- (Nov 18, 2017) Mauricio A Alvarez visited from University of Sheffield, between Nov. 18-25, 2017.
- (Nov. 15, 2017) New paper on Variational Adaptive-Newton (VAN) method, a general-purpose optimization method.
- (Oct 2, 2017) Thang Bui visited from University of Cambridge between Oct. 2-20, 2017.
- (Aug 17, 2017) Mark Schmidt (UBC) and Yarin Gal (Cambridge University) visited my group.
- (Aug 4, 2017) New paper on Structured Inference-Networks for Structured deep-models in ICML workshop DeepStruct
- (July 24, 2017) I gave a talk at ERATO in Tokyo on Aug. 3, 2017
- (July 1, 2017) I gave a talk at ATR in Kyoto on July 10, 2017
- (June 25, 2017) Salma El Aloui (from École Polytechnique) and Zuozhu Liu (from Singapore University of Technology and Design) join as interns.
- (June 19, 2017) Prof. Havard Rue from KAUST visiting from Jun 19-25.
- (Apr 19, 2017) I gave a lecture at the University of Tokyo on Modern Approximate Bayesian Inference Methods. Download slides and their annotated version.
- (Apr 18, 2017) Prof. Marco Cuturi and Prof. Shun-ichi Amari visited AIP and gave talks about their work on Wasserstein distance.
- (Apr 17, 2017) Vaden Masrani (from UBC) and Kimia Nadjahi (from ENS Cachan) joined as interns in my group.
- (Mar 24, 2017) Heiko Strathmann from UCL is visiting from March 24-31, 2017.
- (Feb 27, 2017) Maja Rudolph from Columbia University visited AIP in March, 2017.
- (Feb 22, 2017) New talk at the PGM workshop 2017 in ISM about "Conjugate-Computation Variational Inference".
- (Jan 2016) I presented a poster at the Winter-Festa (YouTube link and "hand-made" poster).
- (Dec. 2016) New Paper at Bayesian Deep Learning workshop in NIPS 2016 for inference in Deep Exp-Family Models.
- (Oct-2016) I became a Team-Leader in Tokyo at RIKEN's newly established Center for Advanced Intelligence Project (AIP).
- (Aug-2016) A new paper at DSAA, 2016.
- (20-Dec-2015) A new paper at UAI, 2016.
- (10-Dec-2015) I got the teaching award for 2015!
- (06-Dec-2015) I am at NIPS 2015.
- (23-Oct-2015) Talk at Amazon, Berlin.
- (20-Oct-2015) Talk at TU, Berlin.
- (11-Oct-2015) I am a reviewer for AI-Stats 2016.
- (28-Sep-2015) I gave a talk at the theory seminar in EPFL about my research.
- (18-Sep-2015) I have a new paper in NIPS 2015 on "KL Proximal Variational Inference".
- (10-Sep-2015) I visited Frank Hutter in Freibourg and gave a talk there.
- (07-Sep-2015) I gave a talk about my work in NTNU, Norway.
- (25-Aug-2015) I offered a short course on "Fundamentals of ML" on August 25, 2015 in Zurich . More than 200 people registered and around 120 people attended.
- (07-Aug-2015) Course webpage for PCML is available.
- (15-Jul-2015) I attended ICML 2015 in Lille.
- (30-May-2015) I visited Masashi Sugiyama's lab in University of Tokyo from March-May, 2015.
- (Apr-2015) I am an area chair for NIPS 2015.
- (Feb-2015) I am now a 'scientific collaborator' at EPFL.
- (Dec-2014) I have a new paper at NIPS 2014. Unfortunately, I couldn't attend due to visa issues.
- (Dec-2014) I taught Pattern Classification and Machine Learning in EPFL from Sep 2014 to Feb 2015. The course had a total 190 Master level students and received a rating of median 5 out of 6.
Young-Jun presented our paper in ACML 2014.
- (Aug-2014) I gave a talk in Shogun-workshop in July 2014 (video link).
- (May-2014) I presented our paper in AI-Stats-2014 (video link).
- (May-2014) I mentored a project on variational inference for Google-Summer-of-Code-2014, along with Heiko Strathmann. Check out the Notebook outlining the project for Shogun toolbox.
- (Feb 2014) I joined as a post-doc with Matthias Grossglauser at LCA lab in EPFL.
- (Sep-2013) I gave an invited talk at the LGM-2013 workshop in Iceland.
- (Nov 2012) I joined as a post-doc with Matthias Seeger at LAPMAL lab in EPFL.
- (11 Mar 2012) Invited talks at EPFL, XRCE, and INRIA-SIERRA [ slides ].
- (08 Feb 2012) A tutorial report on Approximate message passing from my talk on DNOISE.
- (29 Sep 2011) Talk at Microsoft Research, Redmond [ video ] [ slides ]
- (29 Jun 2011) Talk at ICML 2011 [ slides ]
- (22 Apr 2011) Derivation of an EM algorithm for Latent Gaussian Model with Gaussian Likelihood [ pdf ]
- (14 Sep 2009) Derivation of Variational EM algorithm for Correlated Topic Model [ pdf ]
- (25 Feb 2009) Derivation of Gaussian likelihood with Gaussian prior on mean [ pdf ]
- (29 Jan 2009) A note on empirical Bayes estimate of Covariance for Multivariate Normal Distribution [ pdf ]
- (24 Dec 2008) Tech report on Bayesian search algorithms for decomposable Guassian graphical model [ pdf ]
- (27 Feb 2008) Updating Inverse of a Matrix when a Column is added/removed [ pdf ] [ code ]
- (25 Feb 2008) Talk on Kalman filter and demo code [ Slides ] [ Demo ]
- (25 Feb 2008) Notes on information filter [ pdf ]
- (30 Oct 2007) Presentation on Variational Bayes and Message passing at Machine learning Reading Group [ slides ]
- (02 Oct 2007) A note on Exchangeability, Polya’s Urn, and De-Finetti’s Theorem [ pdf ]
- (28 Sep 2007) Linear Algebra Tutorial [ Outline ] [ slides ]
- (18 Sep 2007) Probability Tutorial [ Outline ] [ Slides ]
- (14 June 2007) Talk on Brain-Computer Interface, CIFAR Time-series Workshop, Toronto [ slides ]
- (18 May 2007) Talk on Signal Compression and JPEG, UDLS [ Abstract ] [ slides ]
- (April 2007) Compressed Sensing, Compressed Classification and Joint Signal Recovery, Machine Learning course project [ pdf ]
- (April 2007) Gibbs Sampling for the Probit Regression Model with Gaussian Markov Random Field Latent Variables, Statistical Computation course project [ pdf ] [ slides ]
- (26 Jan 2007) Talk on "Introduction to probability theory, UDLS [ slides ] [ Abstract ]
- (Dec 2007) Game theory models for Pursuit-evasion games, Multi-agent systems course project [ pdf ]
- (Dec 2007) An incremental deployment algorithm for mobile sensors, Optimization course project [ pdf ]