Archive for Oregon
pinot noir, Domaine Serene
Posted in Travel, Wines with tags JSM 2024, Oregon, Oregon wines, Pacific North West, pinot noir, Portland, wildfire, Willamette River on August 20, 2024 by xi'an
50 shades of green [jatp]
Posted in Mountains, pictures, Running, Travel with tags jatp, John Muir, JSM 2024, mist, Mount Hood, Mount Hood National Forest, Mount Sashta, Mount Shasta, Oregon, Pacific crest trail, Timberline Lodge, US parks, USA, volcanoes, wilderness on August 19, 2024 by xi'an
2024 Huckleberry Running Festival 10K [39:28, 2nd/97, +174m, 18⁰]
Posted in Mountains, pictures, Running, Travel with tags 10k, Caen, Grand Master, half-marathon, hiking, huckleberry, JSM 2024, Les Courants de la Liberté, local race, Mount Hood, Oregon, podium, road race, tee-shirt, USA, vacations, Welches, Zigzag on August 16, 2024 by xi'an
A chance glance at a local newspaper, while checking for path conditions in the Zigzag Ranger station, made me aware of a local race taking place next to our vacation rental in Mount Hood Village! I first registered for the 5K then upgraded for the 10K, as the half-marathon sounded too much, esp. after an 18K fast training three days earlier… The route was nice, with two big hills and trees all around. I kept the first runner in my radar for the first seven kilometres but he then took off and won by almost two minutes. Very happy with the result, though, despite running the 5K and 10K more slowly than in Caen last month, where the altitude differential was much more modest. Too bad the tee-shirt is not that great! But the organising team was very friendly and excited at having an international participant to the races, for the very first time.
JSM 2024, Portland, miniday 4
Posted in Books, pictures, Running, Statistics, Travel, University life with tags AI, American Statistical Association, ASA, Bayesian decision theory, Bayesian nonparametrics, Bayesian privacy, BIRS, British Columbia, classification, contextual integrity, data science, differential privacy, draw bridge, ERC, federated learning, generalised Bayesian inference, Joint Statistical Meeting, JSM 2024, Kelowna, Ocean, open water swimming, Oregon, Pacific North West, Portland, Seattle, United States of America, University of Washington, Willamette River on August 11, 2024 by xi'an
Final (half)day at JSM is always a sad thing as most people have left, people are busy dismantling booths and packing boxes, and the few remaining participants are fidgety and sitting on their suitcases (there may have been more suitcases than people in the main area that day!), cafés are minimally staffed or simply closed. Hence not the best time-slot to deliver one’s talk! Still, a few dozen people attended our session. The session topic was Bridge the Gap: Differential Privacy and Statistical Analysis, organised by Bei Jiang whom I met last summer in Kelowna, at a BIRS workshop on privacy. Where I spoke on setting up a complete decision-theoretic framework, as developed (and still in development) within our Ocean group, esp. Joshua Bon, Stan du Ché, and Judith Rousseau. (Rather than on the original plan of talking about convergence versus privacy, as a criticism of differential privacy.) The other talks were by Shurong Li, strongly set with differential privacy when record linkage is present, and Xuan Bi on a fully decentralised federated learning with local exchanges of global gradients that limit privacy leaks. (With a mention of gossip learning I hadn’t seen previously!)
I enjoyed even more the session due to Naisyin Wang giving a discussion on the three talks, in closes the particular because I had not seen her in years, if a few times since she was my teaching assistant in Cornell in a Bayesian decision theory class I was building on the spot (with Linda Zhao as a student!). Which nicely closes the loop given the topic of my talk, of which she was quite supportive! While calling for debiasing post-processing and wondering about a two-dimensional decision theoretic perspective rather than a unidimensional one under hard privacy constraints.
On the way out, after parting from the few friends remaining in the convention centre, I spotted the nearby (steel) bridge being raised, although I could not see the boat responsible for it. I had been unaware of this possibility while running over and under it, as well as swimming thrice under it. And we left Portland in the early afternoon, heading for Seattle and a celebration of Adrian Raftery’s career.
JSM 2024, Portland, Day 3
Posted in pictures, Running, Statistics, Travel, University life with tags AI, American Statistical Association, Arianna Rosenbluth, ASA, bandits, Bayesian lasso, Bayesian model averaging, Bayesian model choice, Bayesian nonparametrics, classification, Committee of Presidents of Statistical Societies, completely random measures, convergence diagnostics, COPSS Presidents' Award, data science, David Blackwell, EM algorithm, generalised Bayesian inference, Gibbs measure, Ising model, jISBA, Joint Statistical Meeting, JSM 2024, missing species problem, Monte Carlo EM, Mount Hood National Forest, open water swimming, Oregon, Portland, Rutgers University, spike-and-slab prior, stochastic localization, Thompson sampling, United States of America, Willamette River on August 9, 2024 by xi'an
Bayesian contributed session as the first round of the third day (with a choice of five parallel sessions featuring Bayesian topics!!, actually easier to pick than among the following eight parallel sessions of the 10:30 schedule!!!), with a talk by Tahir Ekin on adversarial outlier detection that could connect with our Oceaner(c) privacy concerns. Then one involving spike & slab (a theme to figure prominently in this special day!!) in mixed response models by Sameer Deshpande, seeking a (unBayesian!) MAP for a latent variable model by Monte Carlo EM. Followed by a talk by Yunyi Shen on completely random measures for estimating the (distribution of the) number of species in heterogeneous populations. Next, Valentin Zulj on (frequentist rather than) Bayesian stacking, on estimating optimal weights for model averaging (which should be posterior probabilities in a pure Bayesian mindframe), including a score function that could lead to generalised Bayesian inference on said weights. Finishing with a talk by Chaegeun Song on correcting Bayesian credible sets towards (frequentist, again!!!) exact coverage for classification (which reminded me of my very first paper with George on correcting frequentist confidence for Binomial observations). With which I could not really engage as seeking a specific coverage level did not seem relevant, imho, but I appreciated the wheel plot representation.
My second morn session was about modern (what else?!) sampling algorithms, although I spent the first dozen minutes wondering whether or not I had entered the wrong room. Until Tianhao Wang focussed on Thompson sampling for bandits. It did prove far enough from my interest for my (sleep deprived) attention to drift too quickly. Only the talk by Yuchen Wu on a spike & slab (as suits the day!) challenge captured enough this wandering attention. Crossing further into my realm of primary topics by considering a target distribution that is a product of distributions. But I did not get from her presentation how a product measure decomposition was inducing higher efficiency (and did not find answers within the arXived preprint). Unless it exploited specific features of the target, like conditional independence between the components. The last talk was by Brice Huang on sampling low temperature Gibbs measures using stochastic localisation.
After coming upon a row of food trucks across the conference centre and being unfairly attracted by an Ethiopian injera picture into a terrible wrap, I returned for the Skeptical about AI session, just a few minutes late, only to find accessing the session was impossible! Quite sad to miss the presentations and the arguments (even though I had heard a previous talk by Genevera Allen when visiting Rutgers two years ago). As a second best, I then joined the recent (of course!) Advances in Bayesian Computation (aka ABC?!) session with a medley of topics, including a data subset versus data sketching model reduction by Sudipto Saha. Which could have consequences on our privacy strategies. And marginal evidence estimation for the Bayesian Lasso by Christopher Hans while avoiding data completion. And another latent variable model with a sequential variational Bayes approach by Bao Anh Vu, using at one point Cappé et al. (2005) EM-based approximation to the log likelihood gradient. Finishing by a back-to-the-future talk by Luke Duttweiler on MCMC convergence diagnostics. Comparing several chains via proximity maps that themselves require some preliminary knowledge about the MCMC kernel. (Nice title though, “the traceplot thickens”!)
The crux of the day was however the 2024 COPSS Award ceremony with several friends featuring among the recipients, Danielle Durante for the Emerging Leaders Award, Regina Liu for the Elizabeth L. Scott Award and Veronika Rockova for the Presidents’ Award. Congrats!!!
