Bregman divergences are convex distance-like functionals which are locally Euclidean. Most algorithm handling Euclidean distances generalize to Bregman divergences. https://t.co/WSZqCaaIl2
Congratulations to Jinyoung Park and Huy Pham for proving the Kahn-Kalai conjecture---a central open problem in probabilistic combinatorics. Truly exciting breakthrough! https://t.co/BZxaceCDW7
The story of Jinyoung's extraordinary path to mathematics:
https://t.co/C7ADGgiKTV
Bayesian optimization is often described as a way of taking the human out of the loop for time consuming multi-dimensional optimization problems. But in many practical applications like policy optimization, decision makers have difficulty formulating an objective. 1/n #AISTATS
🎉Happy to announce our work https://t.co/f8xCKphOww on Efficient Kernel UCB for contextual bandits! 🎉 Join us at the poster session on Wed at 3:30 UTC #AISTATS2022 We improve the computational efficiency of the algorithm while recovering the same regret as the standard methods
Happy to announce our paper "Learning Inconsistent Preferences with Gaussian Processes" has been accepted to #AISTATS2022.
Interested in modelling inconsistent preferences? Come to our poster session 2 today at 6:15 p.m. BST!
https://t.co/Mim1Y9thmH
@javiergonzh@sejDino
Myself and others are organising a special issue for IEEE Journal on Selected Areas in Information Theory, on "Deep Learning Methods for Inverse Problems" - submission date is May 15.
https://t.co/sBDzoUoVAD
A Gaussian process is a collection of (infinitely many) random variables such that the marginal distribution over any finite subset is a multivariate Gaussian distribution.
This somewhat arcane description is easier to conceptualize when we focus on a subset of two variables:
Natural neighbor interpolation (Robin Sibson) is a generalization of piecewise linear interpolation to higher dimensions. https://t.co/ZoYLPC6FIA https://t.co/R0aaz8xpR5
Looking to hire a Ph.D. student to work on algorithmic data-driven decision making (Topics: Multi-agent learning, RL, Bayesian optimization, Experiment design) at UCL E&EE @ucleeenews. Retweets appreciated!
Details: https://t.co/gkVdsB5ymJ
The push-forward operator is a linear map between measures which operates by displacement of the support of the points. Corresponds to the composition of random vector by a function, and to the change of variable formula of integration. https://t.co/qwksw0EOEz
AISTATS 2022 is still accepting submissions! The deadline for abstracts is 1 week away - Friday, 8 October 2021 11:59am (UTC). And the deadline for papers is 2 weeks away - Friday, 15 October 2021 11:59am (UTC). 1/2
Reviewer 2 complains it has "too many ideas".
@prfsanjeevarora says "gave me some new ideas".
This Friday at Noon PT… come hear about the paper that reviewers don't want you to read! https://t.co/bjA3cXqfpU
I'm speaking at @ml_collective's DLCT: https://t.co/UUDjstsSWz
Fourier descriptors (Zahn and Roskies, 1972) are normalized Fourier moments features which are invariant to translation and rotation. https://t.co/e161gLtlYA
Fokker–Planck equation equivalently describes the movement of a random particule with a drift (as a stochastic ODE) and the evolution of its density (as a PDE). https://t.co/F8up4bYD2d
Relative smoothness is one of the most reinvented definitions in optimization. It gives a simple condition for convergence of mirror descent and dates back at least to 2010. Its many applications should be the way mirror descent is motivated in lectures.
https://t.co/N0hFjzZA0O
Today is the 202nd birthday anniversary of Sir George Gabriel Stokes, a big name in both #Physics and #Math. One of the theorems that bears his name in #VectorCalculus is a theorem so powerful that it contains many special cases that are themselves powerful theorems. #MathType