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Found 14 papers, 5 papers with code
Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models
Evaluated across a range of both masked and autoregressive LMs on benchmark GLUE tasks, MeZO-SVRG outperforms MeZO with up to 20% increase in test accuracies in both full- and partial-parameter fine-tuning settings.
Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting
Ensembling is among the most popular tools in machine learning (ML) due to its effectiveness in minimizing variance and thus improving generalization.
Testing Causality for High Dimensional Data
Determining causal relationship between high dimensional observations are among the most important tasks in scientific discoveries.
Adaptive Sampling for Probabilistic Forecasting under Distribution Shift
We present an adaptive sampling strategy that selects the part of the time series history that is relevant for forecasting.
First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting
Transformer-based models have gained large popularity and demonstrated promising results in long-term time-series forecasting in recent years.
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms
This work studies the threats of adversarial attack on multivariate probabilistic forecasting models and viable defense mechanisms.
Robust Probabilistic Time Series Forecasting
Probabilistic time series forecasting has played critical role in decision-making processes due to its capability to quantify uncertainties.
Multivariate Quantile Function Forecaster
We propose Multivariate Quantile Function Forecaster (MQF$^2$), a global probabilistic forecasting method constructed using a multivariate quantile function and investigate its application to multi-horizon forecasting.
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting
Quantile regression is an effective technique to quantify uncertainty, fit challenging underlying distributions, and often provide full probabilistic predictions through joint learnings over multiple quantile levels.
Variance Reduced Training with Stratified Sampling for Forecasting Models
In large-scale time series forecasting, one often encounters the situation where the temporal patterns of time series, while drifting over time, differ from one another in the same dataset.
Domain Adaptation for Time Series Forecasting via Attention Sharing
Recently, deep neural networks have gained increasing popularity in the field of time series forecasting.
Structured Policy Iteration for Linear Quadratic Regulator
In this paper, we introduce the \textit{Structured Policy Iteration} (S-PI) for LQR, a method capable of deriving a structured linear policy.
Variable Metric Proximal Gradient Method with Diagonal Barzilai-Borwein Stepsize
Under this metric selection for VM-PG, the theoretical convergence is analyzed.
Network Inference via the Time-Varying Graphical Lasso
Many important problems can be modeled as a system of interconnected entities, where each entity is recording time-dependent observations or measurements.
