12 citations · 37 across the 17 of their papers we have counts for
17 papers
PersonaMem-v3: Toward Omni-Platform Personal Intelligence for Holistic User Understanding, Recommendation, and Agentic Tasks
Bowen Jiang, Yuan Yuan, Zhuoqun Hao +12
Personal intelligence is becoming a central frontier for user-facing AI agents. To be helpful in everyday life, agents must understand users across the digital contexts where their…
The Radio-Frequency Transformer for Signal Separation
Egor Lifar, Semyon Savkin, Rachana Madhukara +3
We study a problem of signal separation: estimating a signal of interest (SOI) contaminated by an unknown non-Gaussian background/interference. Given the training data consisting o…
GSRM: Generative Speech Reward Model for Speech RLHF
Maohao Shen, Tejas Jayashankar, Osama Hanna +10
Recent advances in speech language models, such as GPT-4o Voice Mode and Gemini Live, have demonstrated promising speech generation capabilities. Nevertheless, the aesthetic natura…
PersonaMem-v2: Towards Personalized Intelligence via Learning Implicit User Personas and Agentic Memory
Bowen Jiang, Yuan Yuan, Maohao Shen +13
Personalization is one of the next milestones in advancing AI capability and alignment. We introduce PersonaMem-v2, the state-of-the-art dataset for LLM personalization that simula…
Satori-SWE: Evolutionary Test-Time Scaling for Sample-Efficient Software Engineering
Guangtao Zeng, Maohao Shen, Delin Chen +8
Language models (LMs) perform well on standardized coding benchmarks but struggle with real-world software engineering tasks such as resolving GitHub issues in SWE-Bench, especiall…
Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search
Maohao Shen, Guangtao Zeng, Zhenting Qi +7
Large language models (LLMs) have demonstrated remarkable reasoning capabilities across diverse domains. Recent studies have shown that increasing test-time computation enhances LL…
Extremum Encoding for Joint Baseband Signal Compression and Time-Delay Estimation for Distributed Systems
Amir Weiss, Yuval Kochman, Gregory W. Wornell
The ubiquitous time-delay estimation (TDE) problem becomes nontrivial when sensors are non-co-located and communication between them is limited. Building on the recently proposed "…
Estimating the Number and Locations of Boundaries in Reverberant Environments with Deep Learning
Toros Arikan, Luca M. Chackalackal, Fatima Ahsan +4
Underwater acoustic environment estimation is a challenging but important task for remote sensing scenarios. Current estimation methods require high signal strength and a solution…
RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge
Alejandro Lancho, Amir Weiss, Gary C. F. Lee +4
We address the critical problem of interference rejection in radio-frequency (RF) signals using a data-driven approach that leverages deep-learning methods. A primary contribution…
A Joint Data Compression and Time-Delay Estimation Method For Distributed Systems via Extremum Encoding
Amir Weiss, Yuval Kochman, Gregory W. Wornell
Motivated by the proliferation of mobile devices, we consider a basic form of the ubiquitous problem of time-delay estimation (TDE), but with communication constraints between two…
Thermometer: Towards Universal Calibration for Large Language Models
Maohao Shen, Subhro Das, Kristjan Greenewald +3
We consider the issue of calibration in large language models (LLM). Recent studies have found that common interventions such as instruction tuning often result in poorly calibrate…
Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?
Maohao Shen, J. Jon Ryu, Soumya Ghosh +4
This paper questions the effectiveness of a modern predictive uncertainty quantification approach, called \emph{evidential deep learning} (EDL), in which a single neural network mo…
Score-based Source Separation with Applications to Digital Communication Signals
Tejas Jayashankar, Gary C. F. Lee, Alejandro Lancho +3
We propose a new method for separating superimposed sources using diffusion-based generative models. Our method relies only on separately trained statistical priors of independent…
Gibbs-Based Information Criteria and the Over-Parameterized Regime
Haobo Chen, Yuheng Bu, Gregory W. Wornell
Double-descent refers to the unexpected drop in test loss of a learning algorithm beyond an interpolating threshold with over-parameterization, which is not predicted by informatio…
Towards Robust Data-Driven Underwater Acoustic Localization: A Deep CNN Solution with Performance Guarantees for Model Mismatch
Amir Weiss, Andrew C. Singer, Gregory W. Wornell
Key challenges in developing underwater acoustic localization methods are related to the combined effects of high reverberation in intricate environments. To address such challenge…
A Bilateral Bound on the Mean-Square Error for Estimation in Model Mismatch
Amir Weiss, Alejandro Lancho, Yuheng Bu +1
A bilateral (i.e., upper and lower) bound on the mean-square error under a general model mismatch is developed. The bound, which is derived from the variational representation of t…
On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation
Maohao Shen, Yuheng Bu, Gregory Wornell
Due to privacy, storage, and other constraints, there is a growing need for unsupervised domain adaptation techniques in machine learning that do not require access to the data use…