arXiv subjects

Ziyun Chen

Publications and source records attributed to Ziyun Chen.

12 recordsLinked to original sources

arXiv2026

Evaluating Remote Sensing Image Captions Beyond Metric Biases

The core objective of image captioning is to achieve lossless semantic compression from visual signals into textual modalities. However, the reliance on manually curated reference texts for evaluation essentially forces models to mimic specific human annotation styles, thereby masking the true descriptive capabilities of advanced foundation models. This systemic misalignment prompts a critical question: Is task-specific fine-tuning truly necessary for Remote Sensing Image Captioning, or is the perceived performance gap merely an artifact of flawed evaluation criteria? To investigate this discrepancy, we propose ReconScore, a novel reference-free evaluation metric. Rather than computing textual similarities, we assess caption quality by its capability to reconstruct the original visual elements solely from the generated text, effectively neutralizing human annotation biases. Applying this metric, we uncover a profound, counterintuitive truth: inherently powerful, unfine-tuned MLLMs surpass their fine-tuned counterparts in authentic zero-shot RSIC tasks. Driven by this structural discovery, we introduce RemoteDescriber, a completely training-free generation methodology. By employing ReconScore as a self-correction mechanism, we iteratively refine the semantic precision of MLLM outputs without any computational fine-tuning overhead. Comprehensive experiments demonstrate that RemoteDescriber achieves state-of-the-art performance on three datasets. Furthermore, we validate ReconScore's reliability and analyze the flaws of traditional metrics. Our code is available at https://github.com/hhu-czy/RemoteDescriber.

cs.CV↗

arXiv2026

Separating Oblivious and Adaptive Models of Variable Selection

Sparse recovery is among the most well-studied problems in learning theory and high-dimensional statistics. In this work, we investigate the statistical and computational landscapes of sparse recovery with $\ell_\infty$ error guarantees. This variant of the problem is motivated by \emph{variable selection} tasks, where the goal is to estimate the support of a $k$-sparse signal in $\mathbb{R}^d$. Our main contribution is a provable separation between the \emph{oblivious} (``for each'') and \emph{adaptive} (``for all'') models of $\ell_\infty$ sparse recovery. We show that under an oblivious model, the optimal $\ell_\infty$ error is attainable in near-linear time with $\approx k\log d$ samples, whereas in an adaptive model, $\gtrsim k^2$ samples are necessary for any algorithm to achieve this bound. This establishes a surprising contrast with the standard $\ell_2$ setting, where $\approx k \log d$ samples suffice even for adaptive sparse recovery. We conclude with a preliminary examination of a \emph{partially-adaptive} model, where we show nontrivial variable selection guarantees are possible with $\approx k\log d$ measurements.

math.ST↗

arXiv2025

High-Accuracy List-Decodable Mean Estimation

In list-decodable learning, we are given a set of data points such that an $α$-fraction of these points come from a nice distribution $D$, for some small $α\ll 1$, and the goal is to output a short list of candidate solutions, such that at least one element of this list recovers some non-trivial information about $D$. By now, there is a large body of work on this topic; however, while many algorithms can achieve optimal list size in terms of $α$, all known algorithms must incur error which decays, in some cases quite poorly, with $1 / α$. In this paper, we ask if this is inherent: is it possible to trade off list size with accuracy in list-decodable learning? More formally, given $ε> 0$, can we can output a slightly larger list in terms of $α$ and $ε$, but so that one element of this list has error at most $ε$ with the ground truth? We call this problem high-accuracy list-decodable learning. Our main result is that non-trivial high-accuracy guarantees, both information-theoretically and algorithmically, are possible for the canonical setting of list-decodable mean estimation of identity-covariance Gaussians. Specifically, we demonstrate that there exists a list of candidate means of size at most $L = \exp \left( O\left( \tfrac{\log^2 1 / α}{ε^2} \right)\right)$ so that one of the elements of this list has $\ell_2$ distance at most $ε$ to the true mean. We also design an algorithm that outputs such a list with runtime and sample complexity $n = d^{O(\log L)} + \exp \exp (\widetilde{O}(\log L))$. We do so by demonstrating a completely novel proof of identifiability, as well as a new algorithmic way of leveraging this proof without the sum-of-squares hierarchy, which may be of independent technical interest.

cs.LG↗

arXiv2025

Improved Regret and Contextual Linear Extension for Pandora's Box and Prophet Inequality

We study the Pandora's Box problem in an online learning setting with semi-bandit feedback. In each round, the learner sequentially pays to open up to $n$ boxes with unknown reward distributions, observes rewards upon opening, and decides when to stop. The utility of the learner is the maximum observed reward minus the cumulative cost of opened boxes, and the goal is to minimize regret defined as the gap between the cumulative expected utility and that of the optimal policy. We propose a new algorithm that achieves $\widetilde{O}(\sqrt{nT})$ regret after $T$ rounds, which improves the $\widetilde{O}(n\sqrt{T})$ bound of Agarwal et al. [2024] and matches the known lower bound up to logarithmic factors. To better capture real-life applications, we then extend our results to a natural but challenging contextual linear setting, where each box's expected reward is linear in some known but time-varying $d$-dimensional context and the noise distribution is fixed over time. We design an algorithm that learns both the linear function and the noise distributions, achieving $\widetilde{O}(nd\sqrt{T})$ regret. Finally, we show that our techniques also apply to the online Prophet Inequality problem, where the learner must decide immediately whether or not to accept a revealed reward. In both non-contextual and contextual settings, our approach achieves similar improvements and regret bounds.

cs.LG↗

arXiv2025

Lower Bounds for Learning Hamiltonians from Time Evolution

Learning about a Hamiltonian $H$ from its time evolution $e^{-iHt}$ is a fundamental task in quantum science. A flurry of recent work has developed powerful new algorithms with provable guarantees for this task, for a variety of natural settings. Despite this, relatively little is known about lower bounds for learning Hamiltonians. In particular, in the natural setting where we assume $H$ is a $k$-local Hamiltonian on $n$ qubits, all existing algorithms require total evolution time at least $n^{\Omega (k)}$ to learn the parameters of $H$, and it remained open whether one could obtain even faster algorithms -- or at the very least, if one could obtain better runtimes for simpler tasks, such as estimating a single designated coefficient of the Hamiltonian. In this work we show the answer is essentially no, by obtaining strong lower bounds for these problems. We find that not only do $k$-local Hamiltonians require $n^{\Omega(k)}$ time evolution or interactions to learn, but also that in several senses, learning anything about a Hamiltonian is just as hard as learning everything. In particular, we find the same $n^{\Omega(k)}$ lower bound holds for learning a single coefficient of a $k$-local Hamiltonian $H$, even if the rest of $H$ is already known. We also show an $n^{\Omega(k)}$ lower bound for the task of effective Hamiltonian learning, where one seeks only to learn a unitary that approximately implements time evolution of $H$. Several related lower bounds, such as for general sparse (but not necessarily local) $H$ are also given. On the technical side, we make a new connection between Hamiltonian learning lower bounds and the analysis of Boolean functions, where we introduce a novel extremal property that may be of independent interest.

quant-ph↗

arXiv2025

RemoteSAM: Towards Segment Anything for Earth Observation

We aim to develop a robust yet flexible visual foundation model for Earth observation. It should possess strong capabilities in recognizing and localizing diverse visual targets while providing compatibility with various input-output interfaces required across different task scenarios. Current systems cannot meet these requirements, as they typically utilize task-specific architecture trained on narrow data domains with limited semantic coverage. Our study addresses these limitations from two aspects: data and modeling. We first introduce an automatic data engine that enjoys significantly better scalability compared to previous human annotation or rule-based approaches. It has enabled us to create the largest dataset of its kind to date, comprising 270K image-text-mask triplets covering an unprecedented range of diverse semantic categories and attribute specifications. Based on this data foundation, we further propose a task unification paradigm that centers around referring expression segmentation. It effectively handles a wide range of vision-centric perception tasks, including classification, detection, segmentation, grounding, etc, using a single model without any task-specific heads. Combining these innovations on data and modeling, we present RemoteSAM, a foundation model that establishes new SoTA on several earth observation perception benchmarks, outperforming other foundation models such as Falcon, GeoChat, and LHRS-Bot with significantly higher efficiency. Models and data are publicly available at https://github.com/1e12Leon/RemoteSAM.

cs.CV↗

arXiv2024

Prophet Secretary and Matching: the Significance of the Largest Item

The prophet secretary problem is a combination of the prophet inequality and the secretary problem, where elements are drawn from known independent distributions and arrive in uniformly random order. In this work, we design 1) a $0.688$-competitive algorithm, that breaks the $0.675$ barrier of blind strategies (Correa, Saona, Ziliotto, 2021), and 2) a $0.641$-competitive algorithm for the prophet secretary matching problem, that breaks the $1-1/e\approx 0.632$ barrier for the first time. Our second result also applies to the query-commit model of weighted stochastic matching and improves the state-of-the-art ratio (Derakhshan and Farhadi, 2023).

cs.DS↗

arXiv2024

Stochastic Online Correlated Selection

We study Stochastic Online Correlated Selection (SOCS), a family of online rounding algorithms for Non-IID Stochastic Online Submodular Welfare Maximization and special cases such as Online Stochastic Matching, Stochastic AdWords, and Stochastic Display Ads. At each step, the algorithm sees an online item's type and fractional allocation, then immediately allocates it to an agent. We propose a metric called the convergence rate for the quality of SOCS. This is cleaner than most metrics in the OCS literature. We propose a Type Decomposition that reduces SOCS to the two-way special case. First, we sample a surrogate type with half-integer allocation. The rounding is trivial for a one-way type fully allocated to an agent. For a two-way type split equally between two agents, we round it using two-way SOCS. We design the distribution of surrogate types to get two-way types as often as possible while respecting the original fractional allocation in expectation. Following this framework, we make progress on numerous problems: 1) Online Stochastic Matching: We improve the state-of-the-art $0.666$ competitive ratio for unweighted/vertex-weighted matching to $0.69$. 2) Query-Commit Matching: We enhance the ratio to $0.705$ in the Query-Commit model, improving the best previous $0.696$ and $0.662$ for unweighted and vertex-weighted matching. 3) Stochastic AdWords: We give a $0.6338$ competitive algorithm, breaking the $1-\frac{1}{e}$ barrier and answering a decade-old open question. 4) AdWords: The framework applies to the adversarial model if the rounding is oblivious to future items' distributions. We get the first multi-way OCS for AdWords, addressing an open question about OCS. This gives a $0.504$ competitive ratio for AdWords, improving the previous $0.501$. 5) Stochastic Display Ads: We design a $0.644$ competitive algorithm, breaking the $1-\frac{1}{e}$ barrier.

cs.DS↗

arXiv2023

Simultaneous Auctions are Approximately Revenue-Optimal for Subadditive Bidders

We study revenue maximization in multi-item auctions, where bidders have subadditive valuations over independent items. Providing a simple mechanism that is approximately revenue-optimal in this setting is a major open problem in mechanism design. In this paper, we present the first \emph{simple mechanism} whose revenue is at least a \emph{constant fraction} of the optimal revenue in multi-item auctions with subadditive bidders. Our mechanism is a simultaneous auction that incorporates either a personalized entry fee or a personalized reserve price per item. We prove that for any simultaneous auction that satisfies c-efficiency -- a new property we propose, its revenue is at least an $O(c)$-approximation to the optimal revenue. We further show that both the \emph{simultaneous first-price} and the \emph{simultaneous all-pay auction} are $1\over 2$-efficient. Providing revenue guarantees for non-truthful simple mechanisms, e.g., simultaneous auctions, in multi-dimensional environments has been recognized by Roughgarden et al. as an important open question. Prior to our result, the only such revenue guarantees are due to Daskalakis et al. for bidders who have additive valuations over independent items. Our result significantly extends the revenue guarantees of these non-truthful simple auctions to settings where bidders have combinatorial valuations.

cs.GT↗

arXiv2022

Strong Revenue (Non-)Monotonicity of Single-parameter Auctions

Consider Myerson's optimal auction with respect to an inaccurate prior, e.g., estimated from data, which is an underestimation of the true value distribution. Can the auctioneer expect getting at least the optimal revenue w.r.t. the inaccurate prior since the true value distribution is larger? This so-called strong revenue monotonicity is known to be true for single-parameter auctions when the feasible allocations form a matroid. We find that strong revenue monotonicity fails to generalize beyond the matroid setting, and further show that auctions in the matroid setting are the only downward-closed auctions that satisfy strong revenue monotonicity. On the flip side, we recover an approximate version of strong revenue monotonicity that holds for all single-parameter auctions, even without downward-closedness. As applications, we get sample complexity upper bounds for single-parameter auctions under matroid constraints, downward-closed constraints, and general constraints. They improve the state-of-the-art upper bounds and are tight up to logarithmic factors.

cs.GT↗

arXiv2021

Ionization-density-dependent Scintillation Pulse Shape and Mechanism of Luminescence Quenching in LaBr3:Ce

Pulse-shape discrimination (PSD) is usually achieved using the different fast and slow decay components of inorganic scintillators, such as BaF2, CsI:Tl, etc. However, LaBr3:Ce is considered to not possess different components at room temperature, but has been proved to have the capability of discriminating γ and α events using fast digitizers. In this paper, ionization-density-dependent transport and rate equations are used to quantitatively model the competing processes in a particle track. With one parameter set, the model reproduces the nonproportionality response of electrons or α particles, and explains the measured α and γ pulse-shape difference well. In particular, the nonlinear quenching of excited dopant ions, Ce3+, is confirmed herein to mainly contribute observable ionization α and γ pulse-shape differences. Further study of the luminescence quenching can also help to better understand the fundamental physics of nonlinear quenching and thus improve the crystal engineering. Moreover, based on the mechanism of dopant quenching, the ionization-density-dependent pulse-shape differences in other fast single-decay-component inorganic scintillators, such as lutetium yttrium oxyorthosilicate, Lu2(1-x)Y2xSiO5:Ce (LYSO) and CeBr3, are also predicted and verified with experiments.

physics.ins-det↗

arXiv2019

GRID: a Student Project to Monitor the Transient Gamma-Ray Sky in the Multi-Messenger Astronomy Era

The Gamma-Ray Integrated Detectors (GRID) is a space mission concept dedicated to monitoring the transient gamma-ray sky in the energy range from 10 keV to 2 MeV using scintillation detectors onboard CubeSats in low Earth orbits. The primary targets of GRID are the gamma-ray bursts (GRBs) in the local universe. The scientific goal of GRID is, in synergy with ground-based gravitational wave (GW) detectors such as LIGO and VIRGO, to accumulate a sample of GRBs associated with the merger of two compact stars and study jets and related physics of those objects. It also involves observing and studying other gamma-ray transients such as long GRBs, soft gamma-ray repeaters, terrestrial gamma-ray flashes, and solar flares. With multiple CubeSats in various orbits, GRID is unaffected by the Earth occultation and serves as a full-time and all-sky monitor. Assuming a horizon of 200 Mpc for ground-based GW detectors, we expect to see a few associated GW-GRB events per year. With about 10 CubeSats in operation, GRID is capable of localizing a faint GRB like 170817A with a 90% error radius of about 10 degrees, through triangulation and flux modulation. GRID is proposed and developed by students, with considerable contribution from undergraduate students, and will remain operated as a student project in the future. The current GRID collaboration involves more than 20 institutes and keeps growing. On August 29th, the first GRID detector onboard a CubeSat was launched into a Sun-synchronous orbit and is currently under test.

astro-ph.IM↗