Tmux is powerful for parallelization but can be frustrating for monitoring experiments at scales. Like, you can run multiple parallel projects in different sessions but it’s hard to monitor each session — because you will have to manually ‘attach’ to a session to see outputs, and tmux has limited scrollback history and clunky scrolling interaction (you’ll have to Ctrl-v [ into copy mode to scroll). And once a tmux session dies — all terminal outputs are lost....
Came across a paper that proposed an interesting mechanism to improve rating systems. This is a very very good idea. RewardRating: A Mechanism Design Approach to Improve Rating Systems Vakilinia, Faizian, Khalili. Games (2022) https://doi.org/10.3390/g13040052 “To improve rating systems, in this paper, we take a novel mechanism-design approach to increase the cost of fake ratings while providing incentives for honest ratings.” … “Our proposed mechanism RewardRating is inspired by the stock market model in which users can invest in their ratings for services and receive a reward on the basis of future ratings....
I was surprised to discover a lovely — and fairly well-loved — harp-flute duet that had somehow escaped my notice: The Fog is Lifting, Op. 41, by Carl Nielsen. It’s always a joy to stumble across a hidden gem in the repertoire. This piece isn’t about flashy technique or dense chromatic harmonies. What it does offer is space — space for the performers to shape tone, color, and atmosphere. It’s all about timbre....
Our Beyond-Bayesian-Bandits reading group covered this paper today: Multitasking: Efficient Optimal Planning for Bandit Superprocesses Dylan Hadfield-Menell and Stuart Russell. [Link](https://people.csail.mit.edu/dhm/files/bsp_bbvi.pdf to paper) and its supplementary materials. A bandit superprocess is a decision problem composed from multiple independent Markov decision processes (MDPs), coupled only by the constraint that, at each time step, the agent may act in only one of the MDPs. Multitasking problems of this kind are ubiquitous in the real world, yet very little is known about them from a computational viewpoint, beyond the observation that optimal policies for the superprocess may prescribe actions that would be suboptimal for an MDP considered in isolation....
I was — and still am — a huge Sherlock Holmes fan. I first got into it around age 10, thanks to a Chinese edition I found during a summer holiday at my grandparents’ house. I barely remember the plots now, but the heat, the watermelon, and those long, lazy afternoons spent binging the stories without a single worry in the world were so memorable. There’s something about Holmes that makes him perfect summer reading....
Incoherence is sufficient for exact matrix completion under random sampling. Coherent matrix can also be accurately recovered if random sampling can be contingent on each entry’s local coherence.
Why matrix completion is equivalent to minimizing the recovered matrix’s spectral norm.
Tchaikovsky didn’t like Wagner’s Ring of Nibelungen
(Ariana in Shanghai 2025)
Given a partially observed matrix $ A \in \mathbb{R}^{m \times n} $, where only entries $ A_{ij} $ for $ (i, j) \in \Omega \subseteq [m] \times [n] $ are known, the goal is to recover the missing values and construct a full matrix $ \hat{A} \in \mathbb{R}^{m \times n} $. For example, think of known entries of $A_{ij}$ as observation of $m$ consumers’ past ratings on $n$ products. The goal is to make predictions....