activity
20222024
most citedClient Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach

8 citations · 12 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CL20241 cited

AI-Enhanced Cognitive Behavioral Therapy: Deep Learning and Large Language Models for Extracting Cognitive Pathways from Social Media Texts

Meng Jiang, Yi Jing Yu, Qing Zhao +8

Cognitive Behavioral Therapy (CBT) is an effective technique for addressing the irrational thoughts stemming from mental illnesses, but it necessitates precise identification of co…

cs.CV2024

Align-DFER: Pioneering Comprehensive Dynamic Affective Alignment for Dynamic Facial Expression Recognition with CLIP

Zeng Tao, Yan Wang, Junxiong Lin +9

The performance of CLIP in dynamic facial expression recognition (DFER) task doesn't yield exceptional results as observed in other CLIP-based classification tasks. While CLIP's pr…

cs.LG2024

Order-Optimal Regret in Distributed Kernel Bandits using Uniform Sampling with Shared Randomness

Nikola Pavlovic, Sudeep Salgia, Qing Zhao

We consider distributed kernel bandits where agents aim to collaboratively maximize an unknown reward function that lies in a reproducing kernel Hilbert space. Each agent seque…

cs.LG20232 cited

Non-parametric Probabilistic Time Series Forecasting via Innovations Representation

Xinyi Wang, Meijen Lee, Qing Zhao +1

Probabilistic time series forecasting predicts the conditional probability distributions of the time series at a future time given past realizations. Such techniques are critical i…

quant-ph2023

Multi-qubit State Tomography with Few Pauli Measurements

Xudan Chai, Teng Ma, Qihao Guo +3

In quantum information transformation and quantum computation, the most critical issues are security and accuracy. These features, therefore, stimulate research on quantum state ch…

cs.LG20238 cited

Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach

Dan Ben Ami, Kobi Cohen, Qing Zhao

Federated learning (FL) is an emerging machine learning (ML) paradigm used to train models across multiple nodes (i.e., clients) holding local data sets, without explicitly exchang…