Author name cluster

Sinong Geng

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

9 papers

2 author rows

TMLR Journal 2025 Journal Article

Factor Learning Portfolio Optimization Informed by Continuous-Time Finance Models

Sinong Geng
Houssam Nassif
Zhaobin Kuang
Anders Max Reppen
K. Ronnie Sircar

We study financial portfolio optimization in the presence of unknown and uncontrolled system variables referred to as stochastic factors. Existing work falls into two distinct categories: (i) reinforcement learning employs end-to-end policy learning with flexible factor representation, but does not precisely model the dynamics of asset prices or factors; (ii) continuous-time finance methods, in contrast, take advantage of explicitly modeled dynamics but pre-specify, rather than learn, factor representation. We propose FaLPO (factor learning portfolio optimization), a framework that interpolates between these two approaches. Specifically, FaLPO hinges on deep policy gradient to learn a performant investment policy that takes advantage of flexible representation for stochastic factors. Meanwhile, FaLPO also incorporates continuous-time finance models when modeling the dynamics. It uses the optimal policy functional form derived from such models and optimizes an objective that combines policy learning and model calibration. We prove the convergence of FaLPO and provide performance guarantees via a finite-sample bound. On both synthetic and real-world portfolio optimization tasks, we observe that FaLPO outperforms five leading methods. Finally, we show that FaLPO can be extended to other decision-making problems with stochastic factors.

PDF Details

ICLR Conference 2024 Conference Paper

Improving Offline RL by Blending Heuristics

Sinong Geng
Aldo Pacchiano
Andrey Kolobov
Ching-An Cheng

We propose **H**e**u**ristic **Bl**ending (HUBL), a simple performance-improving technique for a broad class of offline RL algorithms based on value bootstrapping. HUBL modifies the Bellman operators used in these algorithms, partially replacing the bootstrapped values with heuristic ones that are estimated with Monte-Carlo returns. For trajectories with higher returns, HUBL relies more on the heuristic values and less on bootstrapping; otherwise, it leans more heavily on bootstrapping. HUBL is very easy to combine with many existing offline RL implementations by relabeling the offline datasets with adjusted rewards and discount factors. We derive a theory that explains HUBL's effect on offline RL as reducing offline RL's complexity and thus increasing its finite-sample performance. Furthermore, we empirically demonstrate that HUBL consistently improves the policy quality of four state-of-the-art bootstrapping-based offline RL algorithms (ATAC, CQL, TD3+BC, and IQL), by 9% on average over 27 datasets of the D4RL and Meta-World benchmarks.

Details

UAI Conference 2023 Conference Paper

A Data-Driven State Aggregation Approach for Dynamic Discrete Choice Models

Sinong Geng
Houssam Nassif
Carlos A. Manzanares

In dynamic discrete choice models, a commonly studied problem is estimating parameters of agent reward functions (also known as ’structural’ parameters) using agent behavioral data. This task is also known as inverse reinforcement learning. Maximum likelihood estimation for such models requires dynamic programming, which is limited by the curse of dimensionality [Bellman, 1957]. In this work, we present a novel algorithm that provides a data-driven method for selecting and aggregating states, which lowers the computational and sample complexity of estimation. Our method works in two stages. First, we estimate agent Qfunctions, and leverage them alongside a clustering algorithm to select a subset of states that are most pivotal for driving changes in Q-functions. Second, with these selected "aggregated" states, we conduct maximum likelihood estimation using a popular nested fixed-point algorithm [Rust, 1987]. The proposed two-stage approach mitigates the curse of dimensionality by reducing the problem dimension. Theoretically, we derive finite-sample bounds on the associated estimation error, which also characterize the trade-off of computational complexity, estimation error, and sample complexity. We demonstrate the empirical performance of the algorithm in two classic dynamic discrete choice estimation applications.

Details

ICML Conference 2020 Conference Paper

Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actions

Sinong Geng
Houssam Nassif
Carlos A. Manzanares
A. Max Reppen
Ronnie Sircar

We propose a reward function estimation framework for inverse reinforcement learning with deep energy-based policies. We name our method PQR, as it sequentially estimates the Policy, the Q-function, and the Reward function by deep learning. PQR does not assume that the reward solely depends on the state, instead it allows for a dependency on the choice of action. Moreover, PQR allows for stochastic state transitions. To accomplish this, we assume the existence of one anchor action whose reward is known, typically the action of doing nothing, yielding no reward. We present both estimators and algorithms for the PQR method. When the environment transition is known, we prove that the PQR reward estimator uniquely recovers the true reward. With unknown transitions, we bound the estimation error of PQR. Finally, the performance of PQR is demonstrated by synthetic and real-world datasets.

Details

UAI Conference 2019 Conference Paper

Joint Nonparametric Precision Matrix Estimation with Confounding

Sinong Geng
Mladen Kolar
Sanmi Koyejo

We consider the problem of precision matrix estimation where, due to extraneous confounding of the underlying precision matrix, the data are independent but not identically distributed. While such confounding occurs in many scientific problems, our approach is inspired by recent neuroscientific research suggesting that brain function, as measured using functional magnetic resonance imagine (fMRI), is susceptible to confounding by physiological noise such as breathing and subject motion. Following the scientific motivation, we propose a graphical model, which in turn motivates a joint nonparametric estimator. We provide theoretical guarantees for the consistency and the convergence rate of the proposed estimator. In addition, we demonstrate that the optimization of the proposed estimator can be transformed into a series of linear programming problems, and thus be efficiently solved in parallel. Empirical results are presented using simulated and real brain imaging data, which suggest that our approach improves precision matrix estimation, as compared to baselines, when confounding is present.

Details

ICML Conference 2019 Conference Paper

Partially Linear Additive Gaussian Graphical Models

Sinong Geng
Minhao Yan
Mladen Kolar
Sanmi Koyejo

We propose a partially linear additive Gaussian graphical model (PLA-GGM) for the estimation of associations between random variables distorted by observed confounders. Model parameters are estimated using an $L_1$-regularized maximal pseudo-profile likelihood estimator (MaPPLE) for which we prove a $\sqrt{n}$-sparsistency. Importantly, our approach avoids parametric constraints on the effects of confounders on the estimated graphical model structure. Empirically, the PLA-GGM is applied to both synthetic and real-world datasets, demonstrating superior performance compared to competing methods.

Details

UAI Conference 2018 Conference Paper

Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error

Sinong Geng
Zhaobin Kuang
Jie Liu 0006
Stephen J. Wright 0001
David Page

We study the L1 -regularized maximum likelihood estimator/estimation (MLE) problem for discrete Markov random fields (MRFs), where efficient and scalable learning requires both sparse regularization and approximate inference. To address these challenges, we consider a stochastic learning framework called stochastic proximal gradient (SPG; Honorio 2012a, Atchade et al. 2014, Miasojedow and Rejchel 2016). SPG is an inexact proximal gradient algorithm [Schmidt et al. , 2011], whose inexactness stems from the stochastic oracle (Gibbs sampling) for gradient approximation – exact gradient evaluation is infeasible in general due to the NP-hard inference problem for discrete MRFs [Koller and Friedman, 2009]. Theoretically, we provide novel verifiable bounds to inspect and control the quality of gradient approximation. Empirically, we propose the tighten asymptotically (TAY) learning strategy based on the verifiable bounds to boost the performance of SPG.

Details

ICML Conference 2018 Conference Paper

Temporal Poisson Square Root Graphical Models

Sinong Geng
Zhaobin Kuang
Peggy L. Peissig
David Page

We propose temporal Poisson square root graphical models (TPSQRs), a generalization of Poisson square root graphical models (PSQRs) specifically designed for modeling longitudinal event data. By estimating the temporal relationships for all possible pairs of event types, TPSQRs can offer a holistic perspective about whether the occurrences of any given event type could excite or inhibit any other type. A TPSQR is learned by estimating a collection of interrelated PSQRs that share the same template parameterization. These PSQRs are estimated jointly in a pseudo-likelihood fashion, where Poisson pseudo-likelihood is used to approximate the original more computationally intensive pseudo-likelihood problem stemming from PSQRs. Theoretically, we demonstrate that under mild assumptions, the Poisson pseudolikelihood approximation is sparsistent for recovering the underlying PSQR. Empirically, we learn TPSQRs from a real-world large-scale electronic health record (EHR) with millions of drug prescription and condition diagnosis events, for adverse drug reaction (ADR) detection. Experimental results demonstrate that the learned TPSQRs can recover ADR signals from the EHR effectively and efficiently.

Details

NeurIPS Conference 2017 Conference Paper

A Screening Rule for l1-Regularized Ising Model Estimation

Zhaobin Kuang
Sinong Geng
David Page

We discover a screening rule for l1-regularized Ising model estimation. The simple closed-form screening rule is a necessary and sufficient condition for exactly recovering the blockwise structure of a solution under any given regularization parameters. With enough sparsity, the screening rule can be combined with various optimization procedures to deliver solutions efficiently in practice. The screening rule is especially suitable for large-scale exploratory data analysis, where the number of variables in the dataset can be thousands while we are only interested in the relationship among a handful of variables within moderate-size clusters for interpretability. Experimental results on various datasets demonstrate the efficiency and insights gained from the introduction of the screening rule.

PDF Details