786 citations · 3k across the 61 of their papers we have counts for
24 papers · 1 filter
Stochastic Optimization Under Power-Law Spectra: Tight Bounds and Shuffling Analysis
Thomas Dybdahl Ahle, Yaroslav Bulatov, Christopher De Sa +1
Recent work has established that power-law spectral conditions on data enable tight convergence bounds for deterministic gradient descent, resolving the conflict between classical…
Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models
Tri Dao, Beidi Chen, Kaizhao Liang +4
Overparameterized neural networks generalize well but are expensive to train. Ideally, one would like to reduce their computational cost while retaining their generalization benefi…
Personalized Benchmarking with the Ludwig Benchmarking Toolkit
Avanika Narayan, Piero Molino, Karan Goel +2
The rapid proliferation of machine learning models across domains and deployment settings has given rise to various communities (e.g. industry practitioners) which seek to benchmar…
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems
Nimit S. Sohoni, Jared A. Dunnmon, Geoffrey Angus +2
In real-world classification tasks, each class often comprises multiple finer-grained "subclasses." As the subclass labels are frequently unavailable, models trained using only the…
Leveraging Organizational Resources to Adapt Models to New Data Modalities
Sahaana Suri, Raghuveer Chanda, Neslihan Bulut +7
As applications in large organizations evolve, the machine learning (ML) models that power them must adapt the same predictive tasks to newly arising data modalities (e.g., a new v…
HiPPO: Recurrent Memory with Optimal Polynomial Projections
Albert Gu, Tri Dao, Stefano Ermon +2
A central problem in learning from sequential data is representing cumulative history in an incremental fashion as more data is processed. We introduce a general framework (HiPPO)…