35 citations · 50 across the 33 of their papers we have counts for
38 papers
Quantum Spectral Estimation with Multiple Initial States: Improved Error and Cost Scaling
Lingwei Zhang, Zhiyan Ding
We study quantum spectrum and eigenstate property estimation using multiple initial states and generalized Hadamard tests. Building on the QFAMES (quantum filtering and analysis of…
Explore Broadly, Reason Sharply: Push Small Models toward the Frontier via Sampling
Panagiotis Theodoropoulos, Nan Jiang, Xintong Duan +4
Power-sharpened sampling is an inference-time alternative to reinforcement-learning (RL) post-training for enhancing reasoning in large language models (LLMs). High-probability seq…
Interactive-Policy Distillation with Bidirectional Propose-and-Verify
Shutong Wu, Xiwen Chen, Brendan Rappazzo +4
On-policy distillation (OPD) trains a student model on its self-generated trajectories with dense token-level teacher feedback. However, naive OPD may suffer from teacher unanchori…
S-SPPO: Semantic-Calibrated Self-Play Preference Optimization
Xiwen Chen, Wenhui Zhu, Jingjing Wang +13
Aligning Large Language Models (LLMs) with human preferences is often formulated via Direct Preference Optimization (DPO). However, the standard Bradley-Terry instantiation of DPO…
A Mechanistic Study of Tabular Foundation Models
Marin Biloš, James T. Wilson, Anderson Schneider +1
Tabular foundation models with different architectures converge in accuracy across a range of classification and regression tasks. This raises questions a leaderboard cannot answer…
Cubit: Token Mixer with Kernel Ridge Regression
Chuanyang Zheng, Jiankai Sun, Yihang Gao +6
Since its introduction in 2017, the Transformer has become one of the most widely adopted architectures in modern deep learning. Despite extensive efforts to improve positional enc…
AlphaLab: Autonomous Multi-Agent Research Across Optimization Domains with Frontier LLMs
Brendan R. Hogan, Xiwen Chen, James T. Wilson +5
We present AlphaLab, an autonomous research harness that leverages frontier LLM agentic capabilities to automate the full experimental cycle in quantitative, computation-intensive…
GeoNorm: Unify Pre-Norm and Post-Norm with Geodesic Optimization
Chuanyang Zheng, Jiankai Sun, Yihang Gao +11
The placement of normalization layers, specifically Pre-Norm and Post-Norm, remains an open question in Transformer architecture design. In this work, we rethink these approaches t…
AHA: Aligning Large Audio-Language Models for Reasoning Hallucinations via Counterfactual Hard Negatives
Yanxi Chen, Wenhui Zhu, Xiwen Chen +9
Although Large Audio-Language Models (LALMs) deliver state-of-the-art (SOTA) performance, they frequently suffer from hallucinations, e.g. generating text not grounded in the audio…
Small Vocabularies, Big Gains: Pretraining and Tokenization in Time Series Models
Alexis Roger, Gwen Legate, Kashif Rasul +2
Tokenization and transfer learning are two critical components in building state of the art time series foundation models for forecasting. In this work, we systematically study the…
Speculative Sampling for Parametric Temporal Point Processes
Marin Biloš, Anderson Schneider, Yuriy Nevmyvaka
Temporal point processes are powerful generative models for event sequences that capture complex dependencies in time-series data. They are commonly specified using autoregressive…
Understanding the Mixture-of-Experts with Nadaraya-Watson Kernel
Chuanyang Zheng, Jiankai Sun, Yihang Gao +13
Mixture-of-Experts (MoE) has become a cornerstone in recent state-of-the-art large language models (LLMs). Traditionally, MoE relies on as the router score funct…
Chart-RVR: Reinforcement Learning with Verifiable Rewards for Explainable Chart Reasoning
Sanchit Sinha, Oana Frunza, Kashif Rasul +2
The capabilities of Large Vision-Language Models (LVLMs) have reached state-of-the-art on many visual reasoning tasks, including chart reasoning, yet they still falter on out-of-di…
Improving Reasoning for Diffusion Language Models via Group Diffusion Policy Optimization
Kevin Rojas, Jiahe Lin, Kashif Rasul +4
Diffusion language models (DLMs) enable parallel, order-agnostic generation with iterative refinement, offering a flexible alternative to autoregressive large language models (LLMs…
Breaking the Additive Error Barrier for Private and Efficient Graph Sparsification via Private Expander Decomposition
Anders Aamand, Justin Y. Chen, Mina Dalirrooyfard +4
We study differentially private algorithms for graph cut sparsification, a fundamental problem in algorithms, privacy, and machine learning. While significant progress has been mad…
Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models
Tejas Vaidhya, Ayush Kaushal, Vineet Jain +5
Large language models (LLMs) are increasingly used across research and industry applications, yet their inference efficiency remains a significant challenge. As the computational p…
Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting
Roland Riachi, Kashif Rasul, Arjun Ashok +5
Recent works have demonstrated the effectiveness of adapting pre-trained language models (LMs) for forecasting time series in the low-data regime. We build upon these findings by a…
Reinforcing Multi-Turn Reasoning in LLM Agents via Fine-Grained Reward Structure and Credit Assignment
Quan Wei, Siliang Zeng, Chenliang Li +9
Reinforcement Learning (RL) approaches have been wildly used to enhance the reasoning capabilities of Large Language Model (LLM) agents in long-horizon, multi-turn scenarios. Such…
Multi-modal Time Series Analysis: A Tutorial and Survey
Yushan Jiang, Kanghui Ning, Zijie Pan +7
Multi-modal time series analysis has recently emerged as a prominent research area in data mining, driven by the increasing availability of diverse data modalities, such as text, i…
TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster
Kanghui Ning, Zijie Pan, Yu Liu +7
Large Language Models (LLMs) and Foundation Models (FMs) have recently become prevalent for time series forecasting tasks. While fine-tuning LLMs enables domain adaptation, they of…
Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting
Jan Schuchardt, Mina Dalirrooyfard, Jed Guzelkabaagac +3
Many forms of sensitive data, such as web traffic, mobility data, or hospital occupancy, are inherently sequential. The standard method for training machine learning models while e…
Variational Schrödinger Momentum Diffusion
Kevin Rojas, Yixin Tan, Molei Tao +2
The momentum Schrödinger Bridge (mSB) has emerged as a leading method for accelerating generative diffusion processes and reducing transport costs. However, the lack of simulation-…
Reweighting Improves Conditional Risk Bounds
Yikai Zhang, Jiahe Lin, Fengpei Li +4
In this work, we study the weighted empirical risk minimization (weighted ERM) schema, in which an additional data-dependent weight function is incorporated when the empirical risk…
Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate
Fengpei Li, Haoxian Chen, Jiahe Lin +7
For many complex simulation tasks spanning areas such as healthcare, engineering, and finance, Monte Carlo (MC) methods are invaluable due to their unbiased estimates and precise e…