Good Papers

Showing papers from The University of Texas at Dallas Show all papers

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NeurIPS 2026SpotlightU Texas at DallasNew Jersey Institute of TechnoloThe University of Texas at DallaUT DallasUTDallasOptimization

Neural Dual Bounds: Valid-by-Construction JGLP Warm-Starts for MAP and Constrained MAP

Akshay Vyas, Shivvrat Arya, Brij Malhotra, Tahrima Rahman and 2 more

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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57%Worth a look
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NeurIPS 2026SpotlightSouthern Methodist University, SNortheasternMLCommonsThe University of Texas at DallaPurdueDatasets & benchmarks

OTel: Open Telco AI Datasets, Benchmarks, and Models

Farbod Tavakkoli, Gregory Diamos, Kenneth Church, David Kanter and 14 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
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NeurIPS 2026George WashingtonThe University of Texas at DallaRL for LLMs

OPPO: Bayesian Value Recursion for Token-Level Credit Assignment in Policy Optimization

Yu Li, Rui Miao, Tian Lan, Zhengling Qi

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U GeorgiaThe University of Texas at DallaU Texas at DallasU Central FloridaUT DallasGraph neural networks

From Nodes to Pixels: Topological and Structural Two-View Graph Imaging

Md Joshem Uddin, Soham Changani, Sai K Navuluru, Cuneyt Akcora and 1 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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88%Must read
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NeurIPS 2026U California, IrvineThe University of Texas at DallaGeorge WashingtonU California, Santa BarbaraDeep RL

TOPPO: Rethinking PPO for Multi-Task Reinforcement Learning with Critic Balancing

TOPPO balances critic gradients to fix PPO's multi-task ill-conditioning, outperforming SAC baselines with fewer parameters and steps.

Yuanpeng Li, Rui Miao, Gefei Lin, Annie Qu

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 15 of 20 reviewers recommend it
lenient 4/5
medium 9/10
strict 2/5
78%Highly rated
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NeurIPS 2026Skyfall.AIU TorontoTuftsThe University of Texas at DallaSkyfallAIAgent benchmarks & environments

Mini Amusement Parks (MAPs): A Testbed for Modelling Business Decisions

MAPs introduces a mini amusement-park simulator benchmarking integrated business decision-making, finding experts outperform state-of-the-art agents by over 11x due to weaknesses in long-horizon planning, sample-efficient learning, and spatial reasoning.

Stéphane Aroca-Ouellette, Ian Berlot-Attwell, Panagiotis Lymperopoulos, Abhiramon Rajasekharan and 4 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 0/5