15 citations · 75 across the 31 of their papers we have counts for
9 papers · 1 filter
ML-Tool-Bench: Tool-Augmented Planning for ML Tasks
Yaswanth Chittepu, Raghavendra Addanki, Tung Mai +2
The development of autonomous machine learning (ML) agents capable of end-to-end data science workflows represents a significant frontier in artificial intelligence. These agents m…
Sample Constrained Treatment Effect Estimation
Raghavendra Addanki, David Arbour, Tung Mai +2
Treatment effect estimation is a fundamental problem in causal inference. We focus on designing efficient randomized controlled trials, to accurately estimate the effect of some tr…
Online MAP Inference and Learning for Nonsymmetric Determinantal Point Processes
Aravind Reddy, Ryan A. Rossi, Zhao Song +6
In this paper, we introduce the online and streaming MAP inference and learning problems for Non-symmetric Determinantal Point Processes (NDPPs) where data points arrive in an arbi…
Exact Representation of Sparse Networks with Symmetric Nonnegative Embeddings
Sudhanshu Chanpuriya, Ryan A. Rossi, Anup Rao +4
Many models for undirected graphs are based on factorizing the graph's adjacency matrix; these models find a vector representation of each node such that the predicted probability…
Coresets for Classification -- Simplified and Strengthened
Tung Mai, Anup B. Rao, Cameron Musco
We give relative error coresets for training linear classifiers with a broad class of loss functions, including the logistic loss and hinge loss. Our construction achieves $(1\pm ε…
Machine Unlearning via Algorithmic Stability
Enayat Ullah, Tung Mai, Anup Rao +2
We study the problem of machine unlearning and identify a notion of algorithmic stability, Total Variation (TV) stability, which we argue, is suitable for the goal of exact unlearn…
Fundamental Tradeoffs in Distributionally Adversarial Training
Mohammad Mehrabi, Adel Javanmard, Ryan A. Rossi +2
Adversarial training is among the most effective techniques to improve the robustness of models against adversarial perturbations. However, the full effect of this approach on mode…
Active Learning for Human-in-the-Loop Customs Inspection
Sundong Kim, Tung-Duong Mai, Sungwon Han +5
We study the human-in-the-loop customs inspection scenario, where an AI-assisted algorithm supports customs officers by recommending a set of imported goods to be inspected. If the…
Graph Neural Networks with Heterophily
Jiong Zhu, Ryan A. Rossi, Anup Rao +4
Graph Neural Networks (GNNs) have proven to be useful for many different practical applications. However, many existing GNN models have implicitly assumed homophily among the nodes…