37 citations · 40 across the 7 of their papers we have counts for
7 papers
Contextual bandits with entropy-based human feedback
Raihan Seraj, Lili Meng, Tristan Sylvain
In recent years, preference-based human feedback mechanisms have become essential for enhancing model performance across diverse applications, including conversational AI systems s…
AutoCast++: Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval
Qi Yan, Raihan Seraj, Jiawei He +2
Machine-based prediction of real-world events is garnering attention due to its potential for informed decision-making. Whereas traditional forecasting predominantly hinges on stru…
ConR: Contrastive Regularizer for Deep Imbalanced Regression
Mahsa Keramati, Lili Meng, R. David Evans
Imbalanced distributions are ubiquitous in real-world data. They create constraints on Deep Neural Networks to represent the minority labels and avoid bias towards majority labels.…
What Constitutes Good Contrastive Learning in Time-Series Forecasting?
Chiyu Zhang, Qi Yan, Lili Meng +1
In recent years, the introduction of self-supervised contrastive learning (SSCL) has demonstrated remarkable improvements in representation learning across various domains, includi…
Robust Reinforcement Learning Objectives for Sequential Recommender Systems
Melissa Mozifian, Tristan Sylvain, Dave Evans +1
Attention-based sequential recommendation methods have shown promise in accurately capturing users' evolving interests from their past interactions. Recent research has also explor…
Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting
Amin Shabani, Amir Abdi, Lili Meng +1
The performance of time series forecasting has recently been greatly improved by the introduction of transformers. In this paper, we propose a general multi-scale framework that ca…
Nana-HDR: A Non-attentive Non-autoregressive Hybrid Model for TTS
Shilun Lin, Wenchao Su, Li Meng +3
This paper presents Nana-HDR, a new non-attentive non-autoregressive model with hybrid Transformer-based Dense-fuse encoder and RNN-based decoder for TTS. It mainly consists of thr…