19 citations · 27 across the 20 of their papers we have counts for
10 papers · 1 filter
Towards Financial World Modeling
Humzah Merchant, Alec Guthrie, Simon Mahns +2
Building a world model requires a state representation useful for planning and decision-making---potentially over tasks unknown at training time. In the context of financial market…
LeNEPA: No-Augmentation Next-Latent Prediction for Time-Series Representation Learning
Alexander Chemeris, Ming Jin, Randall Balestriero
Time series are central to modern data mining applications, from industrial telemetry and server metrics to finance and physiology, yet time-series self-supervised learning often d…
Aionoscope: Debugging Latent-State Accessibility in Time-Series Representations
Alexander Chemeris, Ming Jin, Randall Balestriero
Time-series models are often evaluated by what they can forecast or classify, but those scores do not show whether their representations preserve the process state a user may want…
Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research
Patrik Reizinger, Randall Balestriero, David Klindt +1
Self-Supervised Learning (SSL) powers many current AI systems. As research interest and investment grow, the SSL design space continues to expand. The Platonic view of SSL, followi…
Eidetic Learning: an Efficient and Provable Solution to Catastrophic Forgetting
Nicholas Dronen, Randall Balestriero
Catastrophic forgetting -- the phenomenon of a neural network learning a task t1 and losing the ability to perform it after being trained on some other task t2 -- is a long-standin…
No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data
Daniel Cai, Randall Balestriero
Implicit neural representations (INRs) exhibit growing promise in addressing Earth representation challenges, ranging from emissions monitoring to climate modeling. However, existi…
Curvature Tuning: Provable Training-free Model Steering From a Single Parameter
Leyang Hu, Matteo Gamba, Randall Balestriero
The scaling of model and data sizes has reshaped the AI landscape, establishing finetuning pretrained models as the standard paradigm for solving downstream tasks. However, dominan…
ALLoRA: Adaptive Learning Rate Mitigates LoRA Fatal Flaws
Hai Huang, Randall Balestriero
Low-Rank Adaptation (LoRA) is the bread and butter of Large Language Model (LLM) finetuning. LoRA learns an additive low-rank perturbation, , of a pretrained matrix parameter $…
Cross-Entropy Is All You Need To Invert the Data Generating Process
Patrik Reizinger, Alice Bizeul, Attila Juhos +4
Supervised learning has become a cornerstone of modern machine learning, yet a comprehensive theory explaining its effectiveness remains elusive. Empirical phenomena, such as neura…
Multi-scale clustering and source separation of InSight mission seismic data
Ali Siahkoohi, Rudy Morel, Randall Balestriero +4
Unsupervised source separation involves unraveling an unknown set of source signals recorded through a mixing operator, with limited prior knowledge about the sources, and only acc…