Arrow Research search

Author name cluster

Peter A. Beling

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.

8 papers
2 author rows

Possible papers

8

IJCAI Conference 2020 Conference Paper

An End-to-End Optimal Trade Execution Framework based on Proximal Policy Optimization

  • Siyu Lin
  • Peter A. Beling

In this article, we propose an end-to-end adaptive framework for optimal trade execution based on Proximal Policy Optimization (PPO). We use two methods to account for the time dependencies in the market data based on two different neural network architecture: 1) Long short-term memory (LSTM) networks, 2) Fully-connected networks (FCN) by stacking the most recent limit orderbook (LOB) information as model inputs. The proposed framework can make trade execution decisions based on level-2 limit order book (LOB) information such as bid/ask prices and volumes directly without manually designed attributes as in previous research. Furthermore, we use a sparse reward function, which gives the agent reward signals at the end of each episode as an indicator of its relative performances against the baseline model, rather than implementation shortfall (IS) or a shaped reward function. The experimental results have demonstrated advantages over IS and the shaped reward function in terms of performance and simplicity. The proposed framework has outperformed the industry commonly used baseline models such as TWAP, VWAP, and AC as well as several Deep Reinforcement Learning (DRL) models on most of the 14 US equities in our experiments.

PDF Details DOI

AILAW Journal 2020 Journal Article

Modeling law search as prediction

  • Faraz Dadgostari
  • Mauricio Guim
  • Peter A. Beling
  • Michael A. Livermore
  • Daniel N. Rockmore

Abstract Law search is fundamental to legal reasoning and its articulation is an important challenge and open problem in the ongoing efforts to investigate legal reasoning as a formal process. This Article formulates a mathematical model that frames the behavioral and cognitive framework of law search as a sequential decision process. The model has two components: first, a model of the legal corpus as a search space and second, a model of the search process (or search strategy ) that is compatible with that environment. The search space has the structure of a “multi-network”—an interleaved structure of distinct networks—developed in earlier work. In this Article, we develop and formally describe three related models of the search process. We then implement these models on a subset of the corpus of U.S. Supreme Court opinions and assess their performance against two benchmark prediction tasks. The first is to predict the citations in a document from its semantic content. The second is to predict the search results generated by human users. For both benchmarks, all search models outperform a null model with the learning-based model outperforming the other approaches. Our results indicate that through additional work and refinement, there may be the potential for machine law search to achieve human or near-human levels of performance.

Details DOI

JAIR Journal 2019 Journal Article

Multi-agent Inverse Reinforcement Learning for Certain General-sum Stochastic Games

  • Xiaomin Lin
  • Stephen C. Adams
  • Peter A. Beling

This paper addresses the problem of multi-agent inverse reinforcement learning (MIRL) in a two-player general-sum stochastic game framework. Five variants of MIRL are considered: uCS-MIRL, advE-MIRL, cooE-MIRL, uCE-MIRL, and uNE-MIRL, each distinguished by its solution concept. Problem uCS-MIRL is a cooperative game in which the agents employ cooperative strategies that aim to maximize the total game value. In problem uCE-MIRL, agents are assumed to follow strategies that constitute a correlated equilibrium while maximizing total game value. Problem uNE-MIRL is similar to uCE-MIRL in total game value maximization, but it is assumed that the agents are playing a Nash equilibrium. Problems advE-MIRL and cooE-MIRL assume agents are playing an adversarial equilibrium and a coordination equilibrium, respectively. We propose novel approaches to address these five problems under the assumption that the game observer either knows or is able to accurately estimate the policies and solution concepts for players. For uCS-MIRL, we first develop a characteristic set of solutions ensuring that the observed bi-policy is a uCS and then apply a Bayesian inverse learning method. For uCE-MIRL, we develop a linear programming problem subject to constraints that define necessary and sufficient conditions for the observed policies to be correlated equilibria. The objective is to choose a solution that not only minimizes the total game value difference between the observed bi-policy and a local uCS, but also maximizes the scale of the solution. We apply a similar treatment to the problem of uNE-MIRL. The remaining two problems can be solved efficiently by taking advantage of solution uniqueness and setting up a convex optimization problem. Results are validated on various benchmark grid-world games.

PDF Details DOI

TCS Journal 1998 Journal Article

Using fast matrix multiplication to find basic solutions

  • Peter A. Beling
  • Nimrod Megiddo

We consider the problem of finding a basic solution to a system of linear constraints (in standard form) given a non-basic solution to the system. We show that the known arithmetic complexity bounds for this problem admit considerable improvement. Our technique, which is similar in spirit to that used by Vaidya to find the best complexity bounds for linear programming, is based on reducing much of the computation involved to matrix multiplication. Consequently, our complexity bounds in their most general form are a function of the complexity of matrix multiplication. Using the best known algorithm for matrix multiplication, we achieve a running time of O(m 1. 594 n) arithmetic operations for an m × n problem in standard form. Previously, the best bound was O(m 2 n) arithmetic operations.

Details DOI

FOCS Conference 1991 Conference Paper

Polynomial Algorithms for LP over a Subring of the Algebraic Integers with Applications to LP with Circulant Matrices

  • Ilan Adler
  • Peter A. Beling

It is shown that a modified variant of the interior point method can solve linear programs (LPs) whose coefficients are real numbers from a subring of the algebraic integers. By defining the encoding size of such numbers to be the bit size of the integers that represent them in the subring, it is proved that the modified algorithm runs in time polynomial in the encoding size of the input coefficients, the dimension of the problem, and the order of the subring. The Tardos scheme is then extended to this case, yielding a running time that is independent of the objective and right-hand side data. As a consequence of these results, it is shown that LPs with real circulant coefficient matrices can be solved in strongly polynomial time. It is also shown how the algorithm can be applied to LPs whose coefficients belong to the extension of the integers by a fixed set of square roots. >

Details
v2026.09.13