2 citations · 5 across the 16 of their papers we have counts for
7 papers · 1 filter
Uncertainty-aware Causal Decision Making via Effect Bound Decomposition
Md Musfiqur Rahman, Ziwei Jiang, Hilaf Hasson +1
Causal inference from observational data can provide strong evidence for finding the best action in a decision-making scenario without having to perform expensive randomized trials…
Node-Level Uncertainty Estimation in LLM-Generated SQL
Hilaf Hasson, Ruocheng Guo
We present a practical framework for detecting errors in LLM-generated SQL by estimating uncertainty at the level of individual nodes in the query's abstract syntax tree (AST). Our…
Goal-Conditioned Supervised Learning for Multi-Objective Recommendation
Shijun Li, Hilaf Hasson, Jing Hu +1
Multi-objective learning endeavors to concurrently optimize multiple objectives using a single model, aiming to achieve high and balanced performance across diverse objectives. How…
Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting
Hilaf Hasson, Danielle C. Maddix, Yuyang Wang +2
Ensembling is among the most popular tools in machine learning (ML) due to its effectiveness in minimizing variance and thus improving generalization. Most ensembling methods for b…
Testing Causality for High Dimensional Data
Arun Jambulapati, Hilaf Hasson, Youngsuk Park +1
Determining causal relationship between high dimensional observations are among the most important tasks in scientific discoveries. In this paper, we revisited the \emph{linear tra…
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms
Linbo Liu, Youngsuk Park, Trong Nghia Hoang +2
This work studies the threats of adversarial attack on multivariate probabilistic forecasting models and viable defense mechanisms. Our studies discover a new attack pattern that n…
Dynamic Regret for Strongly Adaptive Methods and Optimality of Online KRR
Dheeraj Baby, Hilaf Hasson, Yuyang Wang
We consider the framework of non-stationary Online Convex Optimization where a learner seeks to control its dynamic regret against an arbitrary sequence of comparators. When the lo…