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Columbia University

Academic institutionnorthamerica · us
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Research library2,055linked papers
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Selected work

Representative Papers

An unscented Kalman filter method for real time input-parameter-state estimation

Nov 04, 2025

This work addresses the joint online estimation of unknown inputs, time-varying parameters, and dynamic states for linear and nonlinear systems with output-only measurements. We propose a unified recursive framework based on the Unscented Kalman Filter (UKF), which augments the state space to jointly incorporate inputs, parameters, and states. By extending the unscented transform to this augmented space, our method avoids Jacobian computation, enabling robust and computationally efficient handling of strong nonlinearities and unknown input disturbances. A real-time data-driven update mechanism, combined with nonlinear function approximation, ensures rapid convergence and high estimation accuracy. In both simulations and physical experiments, the approach achieves millisecond-level response times and superior estimation precision, consistently outperforming conventional Extended Kalman Filters (EKF) and particle filters.

63 citationsRead paper

IdealGPT: Iteratively Decomposing Vision and Language Reasoning via Large Language Models

May 24, 2023Conference on Empirical Methods in Natural Language Processing

Existing vision-language models (VLMs) exhibit limited performance on zero-shot multi-step reasoning tasks, primarily due to their reliance on domain-specific subproblem decomposers and their tendency to force final answers even under insufficient information—compromising reasoning reliability. This paper proposes the first domain-agnostic, adaptive iterative decomposition framework: an LLM first generates subquestions; a VLM then provides visually grounded subanswers via multimodal grounding; finally, the LLM aggregates results and dynamically decides whether to terminate. This enables trustworthy, self-correcting convergence. The framework integrates zero-shot prompting synergy with a divide-and-conquer architecture, substantially enhancing reasoning robustness. Under zero-shot settings, it achieves absolute accuracy gains of +10.2% and +15.6% over the strongest GPT-4–based baselines on the VCR and SNLI-VE benchmarks, respectively.

42 citations6 influentialRead paper

Medical Hallucinations in Foundation Models and Their Impact on Healthcare

Feb 26, 2025arXiv.org

Medical foundation models may generate “hallucinations”—factual, logical, or evidence-inconsistent errors—that jeopardize clinical decision-making and patient safety. To address this, we first propose a multidimensional taxonomy of medical hallucinations and establish a real-world, clinician-annotated benchmark dataset derived from authentic clinical cases; we further validate its clinical impact via an international physician survey. Methodologically, we integrate expert annotation, empirical behavioral surveys, and large language model (LLM) evaluation to systematically assess the efficacy of chain-of-thought (CoT) reasoning and retrieval-augmented generation (RAG) in mitigating hallucinations. Results show both techniques significantly reduce hallucination rates, yet residual hallucinations remain clinically hazardous. Building on these findings, we introduce a patient-safety-centered AI governance and ethics framework, offering theoretical foundations and actionable pathways for responsible deployment of medical AI. (149 words)

41 citationsRead paper

Projection Inference for set-identified SVARs

Apr 18, 2025

This paper addresses inference challenges for structural vector autoregressive (SVAR) models under set identification. We propose a projection-based inferential method that simultaneously delivers asymptotic frequentist coverage and robust Bayesian credibility: the Wald ellipsoid for reduced-form parameters is projected onto the structural parameter space to construct joint confidence regions. We establish, for the first time in general stationary SVARs, that this projection method achieves asymptotic 1−α frequentist coverage and robust Bayesian credibility. Moreover, we introduce a posterior-calibrated radius adjustment algorithm that ensures exact robust credibility of 1−α while guaranteeing precise 1−α coverage over the identification set. Theoretically, our work unifies dual guarantees—frequentist and robust Bayesian—within a coherent framework; computationally, it remains efficient and implementable. Empirically, we replicate the Baumeister–Hamilton (2015) labor supply–demand model, demonstrating the method’s tightness and robustness.

15 citations1 influentialRead paper

Explicit Second-Order Min-Max Optimization Methods with Optimal Convergence Guarantee

Oct 23, 2022arXiv.org

For unconstrained convex-concave minimax optimization, this paper proposes a class of inexact regularized Newton-type algorithms that incorporate second-order information into the hypergradient framework while ensuring global convergence under inexact computations. Theoretically, it achieves the first $O(varepsilon^{-2/3})$ iteration complexity—matching the known lower bound—for such problems. Each iteration requires only one Schur decomposition and $O(loglog(1/varepsilon))$ linear solver calls, eliminating the redundant $loglog$ factor present in prior second-order methods. Through analysis based on the restricted gap function, we establish boundedness of iterates and convergence of the averaged sequence to an $varepsilon$-saddle point. Experiments on synthetic and real-world datasets demonstrate that the proposed method significantly outperforms existing second-order minimax optimization algorithms in both accuracy and efficiency.

14 citations6 influentialRead paper
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