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Higher School of Economics

Academic institutioneurope · ru
Official website
Research library354linked papers
Opportunities0open roles
Selected work

Representative Papers

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

Vikhr: Constructing a State-of-the-art Bilingual Open-Source Instruction-Following Large Language Model for Russian

May 22, 2024MRL

To address the poor generation quality and low computational efficiency of existing large language models for Russian, this paper introduces Vikhr—the first high-performance, bilingual, open-source instruction-following model natively optimized for Russian. Methodologically, Vikhr employs full-parameter continual pretraining followed by supervised instruction fine-tuning, deliberately avoiding parameter-efficient adaptations such as LoRA to achieve vocabulary-level native Russian support. Built upon the Mistral architecture, it features a custom Russian–English tokenizer, alongside substantial expansion of high-quality Russian instruction data and pretraining corpora, enhanced by multi-stage data cleaning and synthetic data generation. Experiments demonstrate that Vikhr establishes new state-of-the-art results among open-source models on multiple Russian-language benchmarks, with several metrics surpassing those of proprietary commercial models. All model weights, datasets, and training code are publicly released.

4 citationsRead paper

Strategizing with AI: Insights from a Beauty Contest Experiment

Feb 05, 2025Social Science Research Network

This study investigates the economic decision-making capabilities of large language models (LLMs) in the Beauty Contest Game, focusing on strategic reasoning, opponent modeling, and parameter adaptability. We construct virtual agents based on state-of-the-art LLMs—including GPT, Claude, and Gemini—and conduct systematic iterative experiments in both multiplayer and two-player settings, incorporating dynamic feedback mechanisms. Our key contribution is the first empirical demonstration that most LLMs (except Llama) dynamically infer opponents’ cognitive levels and adjust strategies accordingly: in multiplayer games, they submit significantly lower numbers—closer to the Nash equilibrium—indicating greater strategic depth and environmental adaptability. However, in two-player games, they consistently fail to reliably identify and execute the dominant strategy. These results suggest that while LLMs hold promise for simulating higher-order human economic reasoning, their strategic rationality exhibits structural limitations, particularly in contexts requiring precise dominance reasoning.

3 citationsRead paper

Enhancing PIBT via Multi-Action Operations

Nov 12, 2025

Myopic, rule-based MAPF solvers such as PIBT suffer significant performance degradation in orientation-aware scenarios due to their neglect of rotational motion costs. Method: We propose Enhanced PIBT—the first PIBT variant incorporating multi-action rollout, which extends the single-step decision horizon to multiple consecutive actions, thereby alleviating myopia. We further integrate graph-guided pre-filtering of feasible orientations and large-neighborhood search for online path refinement. Contribution/Results: The method maintains millisecond-level response times—enabling real-time coordination of thousands of agents—while achieving state-of-the-art performance on the LMAPF-T benchmark: a 12.7% improvement in path efficiency, a 9.3% increase in task success rate, and substantially enhanced robustness and scalability under orientation constraints.

2 citationsRead paper

TabGraphs: A Benchmark and Strong Baselines for Learning on Graphs with Tabular Node Features

Sep 22, 2024arXiv.org

Existing graph learning benchmarks are severely limited by narrow domain coverage and overreliance on a few citation networks, hindering the transfer of graph foundation models to diverse real-world applications. Method: We introduce the first benchmark for heterogeneous tabular-feature graphs—comprising 12 real-world business scenarios with mixed numerical/categorical node attributes and structural relationships—and conduct systematic evaluation across GNNs, XGBoost/LightGBM, MLPs, and various feature encoding and graph augmentation strategies. Contribution/Results: We find that GNNs yield only marginal average AUC gains (+1.8%); in contrast, lightweight graph-aware feature engineering—e.g., k-NN neighborhood aggregation coupled with target encoding—enables XGBoost to outperform GNNs on six tasks, with a maximum improvement of +2.3% AUC. This work bridges the gap between tabular and graph learning, highlights the pivotal role of feature engineering in graph-structured tabular tasks, and establishes a new paradigm for fair, practical graph model evaluation and deployment.

2 citationsRead paper
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