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Michael Shvartsman

7 accepted papers

2025

AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench

NeurIPS 2025spotlight

AI research agents are demonstrating great potential to accelerate scientific progress by automating the design, implementation, and training of machine learning models. We focus on methods for improving agents' performance on MLE-bench, a challenging benchmark where agents compete in Kaggle competi…

Cited by 0SourcecodeScholar
2025

The Automated LLM Speedrunning Benchmark: Reproducing NanoGPT Improvements

NeurIPS 2025poster

Rapidly improving large language models (LLMs) have the potential to assist in scientific progress. One critical skill in this endeavor is the ability to faithfully reproduce existing work. To evaluate the capability of AI agents to reproduce complex code in an active research area, we introduce the…

Cited by 0SourcecodeScholar
2024

Response Time Improves Gaussian Process Models for Perception and Preferences

UAI 2024poster

Models for human choice prediction in preference learning and perception science often use binary response data, requiring many samples to accurately learn latent utilities or perceptual intensities. The response time (RT) to make each choice captures additional information about the decision proces…

2023

A Semi-parametric Model for Decision Making in High-Dimensional Sensory Discrimination Tasks

AAAI 2023technical

Psychometric functions typically characterize binary sensory decisions along a single stimulus dimension. However, real-life sensory tasks vary along a greater variety of dimensions (e.g. color, contrast and luminance for visual stimuli). Approaches to characterizing high-dimensional sensory spaces…

2022

Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation

AISTATS 2022poster

Level set estimation (LSE) is the problem of identifying regions where an unknown function takes values above or below a specified threshold. Active sampling strategies for efficient LSE have primarily been studied in continuous-valued functions. Motivated by applications in human psychophysics wher…

2018

Matrix-normal models for fMRI analysis

AISTATS 2018poster

Multivariate analysis of fMRI data has bene- fited substantially from advances in machine learning. Most recently, a range of prob- abilistic latent variable models applied to fMRI data have been successful in a variety of tasks, including identifying similarity pat- terns in neural data, combining…

2015

A Theory of Decision Making Under Dynamic Context

NeurIPS 2015poster

The dynamics of simple decisions are well understood and modeled as a class of random walk models (e.g. Laming, 1968; Ratcliff, 1978; Busemeyer and Townsend, 1993; Usher and McClelland, 2001; Bogacz et al., 2006). However, most real-life decisions include a rich and dynamically-changing influence of…