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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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publications
Differentially Private Neural Network Training Under the Hidden State Assumption
Published in arXiv preprint arXiv:2407.08233, 2024
We introduce DP-DT, a framework that separates representation learning from privacy protection mechanisms, restricting noise injection to weight aggregation while using auxiliary models for feature extraction on private data. We establish convergence guarantees for non-convex objectives and show that privacy loss can converge to a constant bound rather than accumulating with iterations under the Hidden State Assumption. Empirical validation across vision and language tasks shows improved privacy-utility trade-offs compared to existing methods.
EvoAlpha: Evolutionary Alpha Factor Discovery with Large Language Models
Published in NeurIPS 2025 Workshop on Generative AI in Finance, 2025
We propose EvoAlpha, a framework that leverages large language models to automatically discover formulaic alpha factors for quantitative trading through an evolutionary search process. Our approach combines the code generation capabilities of LLMs with evolutionary algorithms to iteratively create, evaluate, and refine trading signals, enabling the discovery of novel alpha factors that outperform traditional hand-crafted approaches.
DualOptim: Enhancing Efficacy and Stability in Machine Unlearning with Dual Optimizers
Published in Neural Information Processing Systems (NeurIPS) 2025, 2025
Existing machine unlearning (MU) approaches exhibit significant sensitivity to hyperparameters, requiring meticulous tuning that limits practical deployment. In this work, we first empirically demonstrate the instability and suboptimal performance of existing popular MU methods when deployed in different scenarios. To address this issue, we propose Dual Optimizer (DualOptim), which incorporates adaptive learning rate and decoupled momentum factors. Empirical and theoretical evidence demonstrates that DualOptim contributes to effective and stable unlearning. Through extensive experiments, we show that DualOptim can significantly boost MU efficacy and stability across diverse tasks, including image classification, image generation, and large language models, making it a versatile approach to empower existing MU algorithms.
AlphaBench: Benchmarking Large Language Models in Formulaic Alpha Factor Mining
Published in International Conference on Learning Representations (ICLR) 2026, 2026
We introduce AlphaBench, a comprehensive benchmark designed to evaluate large language models on the task of formulaic alpha factor mining for quantitative finance. Our benchmark provides standardized evaluation protocols, diverse task scenarios, and rigorous metrics to assess the capability of LLMs in generating effective trading signals across different market conditions and asset classes.