Xianghang Liu

Xianghang Liu, Bartłomiej Twardowski, Tri Kurniawan Wijaya

In Federated Learning (FL) of click-through rate (CTR) prediction, users' data is not shared for privacy protection. The learning is performed by training locally on client devices and communicating only model changes to the server. There are two main challenges: (i) the client heterogeneity, making FL algorithms that use the weighted averaging to aggregate model updates from the clients have slow progress and unsatisfactory learning results; and (ii) the difficulty of tuning the server learning rate with trial-and-error methodology due to the big computation time and resources needed for each experiment. To address these challenges, we propose a simple online meta-learning method to learn a strategy of aggregating the model updates, which adaptively weighs the importance of the clients based on their attributes and adjust the step sizes of the update. We perform extensive evaluations on public datasets. Our method significantly outperforms the state-of-the-art in both the speed of convergence and the quality of the final learning results.

Francesco Fabbri, Xianghang Liu, Jack R. McKenzie et al.

Federated Learning (FL) has emerged as a key approach for distributed machine learning, enhancing online personalization while ensuring user data privacy. Instead of sending private data to a central server as in traditional approaches, FL decentralizes computations: devices train locally and share updates with a global server. A primary challenge in this setting is achieving fast and accurate model training - vital for recommendation systems where delays can compromise user engagement. This paper introduces FedFNN, an algorithm that accelerates decentralized model training. In FL, only a subset of users are involved in each training epoch. FedFNN employs supervised learning to predict weight updates from unsampled users, using updates from the sampled set. Our evaluations, using real and synthetic data, show: 1. FedFNN achieves training speeds 5x faster than leading methods, maintaining or improving accuracy; 2. the algorithm's performance is consistent regardless of client cluster variations; 3. FedFNN outperforms other methods in scenarios with limited client availability, converging more quickly.

Farhad Bazyari, Xianghang Liu, Sean Moran

Osborne and Dredze (2014) reported that Twitter was the timeliest social-media source of breaking news, trailing only newswire. Twelve years on, the platform landscape has shifted - Google+ is gone, X replaced Twitter, Bluesky and Threads have appeared - and platform data now flows almost exclusively through commercial social-listening providers that redact key fields. We revisit the question with two sampling designs run through the same downstream pipeline. Sample A draws N = 50 events from the Wikipedia Current Events Portal (WCEP) ranked by article pageviews. Sample B draws N = 109 events from Polymarket prediction markets ranked by USD trading volume, with each event's news moment pinned to the largest 1-hour trade-volume spike. Both samples are pulled from one commercial provider across nine indexed channels. We report three findings. (1) The X-vs-news direction depends on the sample. News leads X by a median of 21.6 min on Sample A (n = 6 paired); the same comparison is tied at -0.02 min on Sample B (n = 16 paired, X earliest in 38%). (2) The channel ecosystem has diversified. Bluesky, Facebook public, and YouTube together account for 24-32% of earliest channel wins; the 2014 "X versus newswire" framing no longer fits. (3) Coverage gaps are structural. Even with U.S.-relevance filtering and a pageview prior, the provider's index returns no on-topic evidence on 24% of randomly-sampled WCEP events. The paper's contribution is the cross-surface design that exposes the sample dependency in (1).

Xianghang Liu, Justin Domke

Markov chain Monte Carlo (MCMC) algorithms are simple and extremely powerful techniques to sample from almost arbitrary distributions. The flaw in practice is that it can take a large and/or unknown amount of time to converge to the stationary distribution. This paper gives sufficient conditions to guarantee that univariate Gibbs sampling on Markov Random Fields (MRFs) will be fast mixing, in a precise sense. Further, an algorithm is given to project onto this set of fast-mixing parameters in the Euclidean norm. Following recent work, we give an example use of this to project in various divergence measures, comparing univariate marginals obtained by sampling after projection to common variational methods and Gibbs sampling on the original parameters.

Justin Domke, Xianghang Liu

Inference in general Ising models is difficult, due to high treewidth making tree-based algorithms intractable. Moreover, when interactions are strong, Gibbs sampling may take exponential time to converge to the stationary distribution. We present an algorithm to project Ising model parameters onto a parameter set that is guaranteed to be fast mixing, under several divergences. We find that Gibbs sampling using the projected parameters is more accurate than with the original parameters when interaction strengths are strong and when limited time is available for sampling.