16 citations · 28 across the 6 of their papers we have counts for
6 papers
BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments
Yusuf Roohani, Andrew Lee, Qian Huang +6
Agents based on large language models have shown great potential in accelerating scientific discovery by leveraging their rich background knowledge and reasoning capabilities. In t…
MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation
Qian Huang, Jian Vora, Percy Liang +1
A central aspect of machine learning research is experimentation, the process of designing and running experiments, analyzing the results, and iterating towards some positive outco…
Scoring Black-Box Models for Adversarial Robustness
Jian Vora, Pranay Reddy Samala
Deep neural networks are susceptible to adversarial inputs and various methods have been proposed to defend these models against adversarial attacks under different perturbation mo…
PAC Mode Estimation using PPR Martingale Confidence Sequences
Shubham Anand Jain, Rohan Shah, Sanit Gupta +7
We consider the problem of correctly identifying the \textit{mode} of a discrete distribution with sufficiently high probability by observing a sequence of i.i.d. sam…
Recovery of Joint Probability Distribution from one-way marginals: Low rank Tensors and Random Projections
Jian Vora, Karthik S. Gurumoorthy, Ajit Rajwade
Joint probability mass function (PMF) estimation is a fundamental machine learning problem. The number of free parameters scales exponentially with respect to the number of random…
Stochastic Approximation Algorithms for Principal Component Analysis
Jian Vora
Principal Component Analysis is a novel way of of dimensionality reduction. This problem essentially boils down to finding the top k eigen vectors of the data covariance matrix. A…