Emily Fox — accepted AI-conference papers

2026

PETRI: Learning Unified Cell Embeddings from Unpaired Modalities via Early-Fusion Joint Reconstruction

Ryan W Conrad, Ethan Weinberger, Saradha Venkatachalapathy, Yuwen Chen, Darshini Shah, Bay Johnson +3

ICLR 2026poster

Integrating multimodal screening data is challenging because biological signals only partially overlap and cell-level pairing is frequently unavailable. Existing approaches either require pairing or fail to capture both shared and modality-specific information in an end-to-end manner. We present PET…

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2025

HDP-Flow: Generalizable Bayesian Nonparametric Model for Time Series State Discovery

Sana Tonekaboni, Tina Behrouzi, Addison Weatherhead, Emily Fox, David Blei, Anna Goldenberg

UAI 2025

We introduce HDP-Flow, a Bayesian nonparametric (BNP) model for unsupervised state discovery in dynamic, non-stationary time series data. Unlike prior work that assumes fixed states, HDPFlow models evolving datasets with unknown and variable latent states. By integrating the adaptability of BNP mode

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2025

KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors

Benson Chen, Tomasz Danel, Gabriel H. S. Dreiman, Patrick J. McEnaney, Nikhil Jain, Kirill Novikov +15

ICML 2025poster

DNA-Encoded Libraries (DELs) represent a transformative technology in drug discovery, facilitating the high-throughput exploration of vast chemical spaces. Despite their potential, the scarcity of publicly available DEL datasets presents a bottleneck for the advancement of machine learning methodolo…

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2024

Automated Statistical Model Discovery with Language Models

Michael Y. Li, Emily Fox, Noah Goodman

ICML 2024poster

Statistical model discovery is a challenging search over a vast space of models subject to domain-specific constraints. Efficiently searching over this space requires expertise in modeling and the problem domain. Motivated by the domain knowledge and programming capabilities of large language models…

Cited by 13 Source ↗PDF ↗Scholar ↗

2024

Hybrid$^2$ Neural ODE Causal Modeling and an Application to Glycemic Response

Bob Junyi Zou, Matthew E Levine, Dessi P. Zaharieva, Ramesh Johari, Emily Fox

ICML 2024oral

Hybrid models composing mechanistic ODE-based dynamics with flexible and expressive neural network components have grown rapidly in popularity, especially in scientific domains where such ODE-based modeling offers important interpretability and validated causal grounding (e.g., for counterfactual re…

Cited by 4 Source ↗PDF ↗code ↗Scholar ↗

2023

Sequence Modeling with Multiresolution Convolutional Memory

Jiaxin Shi, Ke Alexander Wang, Emily Fox

ICML 2023poster

Efficiently capturing the long-range patterns in sequential data sources salient to a given task---such as classification and generative modeling---poses a fundamental challenge. Popular approaches in the space tradeoff between the memory burden of brute-force enumeration and comparison, as in trans…

Cited by 18 Source ↗PDF ↗code ↗Scholar ↗

2019

A Simple Adaptive Tracker with Reminiscences

Christopher Xie, Emily Fox, Zaid Harchaoui

ICRA 2019poster

Correlation filters have provided exceptional results in the field of visual object tracking in the past few years. However, these methods typically learn a single filter to be robust to many different appearance changes, which can be challenging. We propose a simple solution to this problem by util…

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2015

Streaming Variational Inference for Bayesian Nonparametric Mixture Models

Alex Tank, Nicholas Foti, Emily Fox

AISTATS 2015poster

In theory, Bayesian nonparametric (BNP) models are well suited to streaming data scenarios due to their ability to adapt model complexity based on the amount of data observed. Unfortunately, such benefits have not been fully realized in practice; existing inference algorithms either are not applicab…

Cited by 39 Source ↗PDF ↗Scholar ↗