18 citations · 27 across the 20 of their papers we have counts for
21 papers
Abductive Reasoning with Probabilistic Commonsense
Joseph Cotnareanu, Chiara Roverato, Han Zhou +3
Recent efforts to improve the reasoning abilities of Large Language Models (LLMs) have focused on integrating formal logic solvers within neurosymbolic frameworks. A key challenge…
A Balanced Neuro-Symbolic Approach for Commonsense Abductive Logic
Joseph Cotnareanu, Didier Chetelat, Yingxue Zhang +1
Although Large Language Models (LLMs) have demonstrated impressive formal reasoning abilities, they often break down when problems require complex proof planning. One promising app…
C3PO: Optimized Large Language Model Cascades with Probabilistic Cost Constraints for Reasoning
Antonios Valkanas, Soumyasundar Pal, Pavel Rumiantsev +2
Large language models (LLMs) have achieved impressive results on complex reasoning tasks, but their high inference cost remains a major barrier to real-world deployment. A promisin…
FEval-TTC: Fair Evaluation Protocol for Test-Time Compute
Pavel Rumiantsev, Soumyasundar Pal, Yingxue Zhang +1
The performance of Large Language Models (LLMs) and the associated dollar costs of API calls can fluctuate over time, potentially invalidating conclusions drawn in prior research.…
GraphPPD: Posterior Predictive Modelling for Graph-Level Inference
Soumyasundar Pal, Liheng Ma, Amine Natik +2
Accurate modelling and quantification of predictive uncertainty is crucial in deep learning since it allows a model to make safer decisions when the data is ambiguous and facilitat…
Reasoning on a Budget: A Survey of Adaptive and Controllable Test-Time Compute in LLMs
Mohammad Ali Alomrani, Yingxue Zhang, Derek Li +14
Large language models (LLMs) have rapidly progressed into general-purpose agents capable of solving a broad spectrum of tasks. However, current models remain inefficient at reasoni…
Omni-Thinker: Scaling Multi-Task RL in LLMs with Hybrid Reward and Task Scheduling
Derek Li, Jiaming Zhou, Leo Maxime Brunswic +8
The pursuit of general-purpose artificial intelligence depends on large language models (LLMs) that can handle both structured reasoning and open-ended generation. We present Omni-…
Extended-BMS Anomalies and Flat Space Holography
Laurent Baulieu, Luca Ciambelli, Tom Wetzstein
We geometrically determine the BRST symmetry of a three-dimensional extended BMS field theory defined at the null boundary of asymptotically flat four-dimensional spaces. We then d…
InnerThoughts: Disentangling Representations and Predictions in Large Language Models
Didier Chételat, Joseph Cotnareanu, Rylee Thompson +2
Large language models (LLMs) contain substantial factual knowledge which is commonly elicited by multiple-choice question-answering prompts. Internally, such models process the pro…
Extracting and Following Paths for Robust Relational Reasoning with Large Language Models
Ge Zhang, Mohammad Ali Alomrani, Hongjian Gu +7
Large language models (LLMs) possess vast semantic knowledge but often struggle with complex reasoning tasks, particularly in relational reasoning problems such as kinship or spati…
Refining Answer Distributions for Improved Large Language Model Reasoning
Soumyasundar Pal, Didier Chételat, Yingxue Zhang +1
Large Language Models (LLMs) have exhibited an impressive capability to perform reasoning tasks, especially if they are encouraged to generate a sequence of intermediate steps. Rea…
Enhancing Logical Reasoning in Large Language Models through Graph-based Synthetic Data
Jiaming Zhou, Abbas Ghaddar, Ge Zhang +7
Despite recent advances in training and prompting strategies for Large Language Models (LLMs), these models continue to face challenges with complex logical reasoning tasks that in…
CKGConv: General Graph Convolution with Continuous Kernels
Liheng Ma, Soumyasundar Pal, Yitian Zhang +3
The existing definitions of graph convolution, either from spatial or spectral perspectives, are inflexible and not unified. Defining a general convolution operator in the graph do…
Personalized Negative Reservoir for Incremental Learning in Recommender Systems
Antonios Valkanas, Yuening Wang, Yingxue Zhang +1
Recommender systems have become an integral part of online platforms. Every day the volume of training data is expanding and the number of user interactions is constantly increasin…
Multi-resolution Time-Series Transformer for Long-term Forecasting
Yitian Zhang, Liheng Ma, Soumyasundar Pal +2
The performance of transformers for time-series forecasting has improved significantly. Recent architectures learn complex temporal patterns by segmenting a time-series into patche…
Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems
Yuening Wang, Yingxue Zhang, Antonios Valkanas +4
Recommender systems now consume large-scale data and play a significant role in improving user experience. Graph Neural Networks (GNNs) have emerged as one of the most effective re…
Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter Configuration in Cellular Network
Mehrtash Mehrabi, Walid Masoudimansour, Yingxue Zhang +5
The mobile communication enabled by cellular networks is the one of the main foundations of our modern society. Optimizing the performance of cellular networks and providing massiv…
Spectral Augmentations for Graph Contrastive Learning
Amur Ghose, Yingxue Zhang, Jianye Hao +1
Contrastive learning has emerged as a premier method for learning representations with or without supervision. Recent studies have shown its utility in graph representation learnin…
Bidirectional Learning for Offline Model-based Biological Sequence Design
Can Chen, Yingxue Zhang, Xue Liu +1
Offline model-based optimization aims to maximize a black-box objective function with a static dataset of designs and their scores. In this paper, we focus on biological sequence d…
Node Copying: A Random Graph Model for Effective Graph Sampling
Florence Regol, Soumyasundar Pal, Jianing Sun +3
There has been an increased interest in applying machine learning techniques on relational structured-data based on an observed graph. Often, this graph is not fully representative…
Generalizable Cross-Graph Embedding for GNN-based Congestion Prediction
Amur Ghose, Vincent Zhang, Yingxue Zhang +3
Presently with technology node scaling, an accurate prediction model at early design stages can significantly reduce the design cycle. Especially during logic synthesis, predicting…