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Dan Biderman

4 accepted papers

2026

Agentic Collaboration as an Information Bottleneck Problem

ICLR 2026poster

Agentic language model (LM) systems have rapidly become central to modern workflows, powering applications like "Deep Research" and "Claude Code." As contexts grow beyond what even the largest frontier models can process effectively, multi-LM architectures have emerged to overcome context limitatio…

Cited by 0SourceScholar
2025

Cost-efficient Collaboration between On-device and Cloud Language Models

ICML 2025poster

We investigate an emerging setup in which a small, on-device language model (LM) with access to local data collaborates with a frontier, cloud-hosted LM to solve real-world tasks involving financial, medical, and scientific reasoning over long documents. *Can a local-remote collaboration reduce clo…

Cited by 0SourcePDFScholar
2021

Bias-Free Scalable Gaussian Processes via Randomized Truncations

ICML 2021spotlight

Scalable Gaussian Process methods are computationally attractive, yet introduce modeling biases that require rigorous study. This paper analyzes two common techniques: early truncated conjugate gradients (CG) and random Fourier features (RFF). We find that both methods introduce a systematic bias on…

2019

BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos

NeurIPS 2019poster

A fundamental goal of systems neuroscience is to understand the relationship between neural activity and behavior. Behavior has traditionally been characterized by low-dimensional, task-related variables such as movement speed or response times. More recently, there has been a growing interest in au…