Evan Dogariu

Y. Isabel Liu, Windsor Nguyen, Yagiz Devre et al. · princeton

Recent advances in state-space model architectures have shown great promise for efficient sequence modeling, but challenges remain in balancing computational efficiency with model expressiveness. We propose the Flash STU architecture, a hybrid model that interleaves spectral state space model layers with sliding window attention, enabling scalability to billions of parameters for language modeling while maintaining a near-linear time complexity. We evaluate the Flash STU and its variants on diverse sequence prediction tasks, including linear dynamical systems, robotics control, and language modeling. We find that, given a fixed parameter budget, the Flash STU architecture consistently outperforms the Transformer and other leading state-space models such as S4 and Mamba-2.

Alex Zhang, Evan Dogariu

The use of appearance codes in recent work on generative modeling has enabled novel view renders with variable appearance and illumination, such as day-time and night-time renders of a scene. A major limitation of this technique is the need to re-train new appearance codes for every scene on inference, so in this work we address this problem proposing a framework that learns a joint embedding space for the appearance and structure of the scene by enforcing a contrastive loss constraint between different modalities. We apply our framework to a simple Variational Auto-Encoder model on the RADIATE dataset \cite{sheeny2021radiate} and qualitatively demonstrate that we can generate new renders of night-time photos using day-time appearance codes without additional optimization iterations. Additionally, we compare our model to a baseline VAE that uses the standard per-image appearance code technique and show that our approach achieves generations of similar quality without learning appearance codes for any unseen images on inference.

Annie Marsden, Evan Dogariu, Naman Agarwal et al. · deepmind, princeton

We consider the problem of length generalization in sequence prediction. We define a new metric of performance in this setting -- the Asymmetric-Regret -- which measures regret against a benchmark predictor with longer context length than available to the learner. We continue by studying this concept through the lens of the spectral filtering algorithm. We present a gradient-based learning algorithm that provably achieves length generalization for linear dynamical systems. We conclude with proof-of-concept experiments which are consistent with our theory.

Evan Dogariu, Anand Brahmbhatt, Elad Hazan · princeton

We study the fundamental problem of learning a marginally stable unknown nonlinear dynamical system. We describe an algorithm for this problem, based on the technique of spectral filtering, which learns a mapping from past observations to the next based on a spectral representation of the system. Using techniques from online convex optimization, we prove vanishing prediction error for any nonlinear dynamical system that has finitely many marginally stable modes, with rates governed by a novel quantitative control-theoretic notion of learnability. The main technical component of our method is a new spectral filtering algorithm for linear dynamical systems, which incorporates past observations and applies to general noisy and marginally stable systems. This significantly generalizes the original spectral filtering algorithm to both asymmetric dynamics as well as incorporating noise correction, and is of independent interest.

Evan Dogariu

Neural networks have emerged as a powerful tool for solving complex tasks across various domains, but their increasing size and computational requirements have posed significant challenges in deploying them on resource-constrained devices. Neural network sparsification, and in particular pruning, has emerged as an effective technique to alleviate these challenges by reducing model size, computational complexity, and memory footprint while maintaining competitive performance. However, many pruning pipelines modify the standard training pipeline at only a single stage, if at all. In this work, we look to develop an end-to-end training pipeline that befits neural network pruning and sparsification at all stages of training. To do so, we make use of nonstandard model parameter initialization, pre-pruning training methodologies, and post-pruning training optimizations. We conduct experiments utilizing combinations of these methods, in addition to different techniques used in the pruning step, and find that our combined pipeline can achieve significant gains over current state of the art approaches to neural network sparsification.

Evan Dogariu, Jiatong Yu

In this work we present an overview of statistical learning, followed by a survey of robust streaming techniques and challenges, culminating in several rigorous results proving the relationship that we motivate and hint at throughout the journey. Furthermore, we unify often disjoint theorems in a shared framework and notation to clarify the deep connections that are discovered. We hope that by approaching these results from a shared perspective, already aware of the technical connections that exist, we can enlighten the study of both fields and perhaps motivate new and previously unconsidered directions of research.