New in #TOCHI: "Practitioner Motives to Use Different Hyperparameter Optimization Methods"
Interviews and surveys reveal performance goals, model understanding, org context, and tool limits shape HPO choices, explaining manual tuning’s lasting appeal.
🔗 dl.acm.org/doi/abs/10.1...
#ML#HPO
(1/2)
Interviews and surveys reveal performance goals, model understanding, org context, and tool limits shape HPO choices, explaining manual tuning’s lasting appeal.
🔗 dl.acm.org/doi/abs/10.1...
#ML#HPO
(1/2)
Practitioner Motives to Use Different Hyperparameter Optimization Methods | ACM Transactions on Computer-Human Interaction
Programmatic hyperparameter optimization (HPO) methods, such as Bayesian optimization
and evolutionary algorithms, are known for their sample efficiency in identifying
optimal configurations for machi...
dl.acm.org
August 15, 2025 at 10:04 PM
New in #TOCHI: "Practitioner Motives to Use Different Hyperparameter Optimization Methods"
Interviews and surveys reveal performance goals, model understanding, org context, and tool limits shape HPO choices, explaining manual tuning’s lasting appeal.
🔗 dl.acm.org/doi/abs/10.1...
#ML#HPO
(1/2)
Interviews and surveys reveal performance goals, model understanding, org context, and tool limits shape HPO choices, explaining manual tuning’s lasting appeal.
🔗 dl.acm.org/doi/abs/10.1...
#ML#HPO
(1/2)