229 citations · 355 across the 11 of their papers we have counts for
11 papers
Human-aligned Chess with a Bit of Search
Yiming Zhang, Athul Paul Jacob, Vivian Lai +2
Chess has long been a testbed for AI's quest to match human intelligence, and in recent years, chess AI systems have surpassed the strongest humans at the game. However, these syst…
Modeling Boundedly Rational Agents with Latent Inference Budgets
Athul Paul Jacob, Abhishek Gupta, Jacob Andreas
We study the problem of modeling a population of agents pursuing unknown goals subject to unknown computational constraints. In standard models of bounded rationality, sub-optimal…
Regularized Conventions: Equilibrium Computation as a Model of Pragmatic Reasoning
Athul Paul Jacob, Gabriele Farina, Jacob Andreas
We present a model of pragmatic language understanding, where utterances are produced and understood by searching for regularized equilibria of signaling games. In this model (whic…
The Consensus Game: Language Model Generation via Equilibrium Search
Athul Paul Jacob, Yikang Shen, Gabriele Farina +1
When applied to question answering and other text generation tasks, language models (LMs) may be queried generatively (by sampling answers from their output distribution) or discri…
AutoReply: Detecting Nonsense in Dialogue Introspectively with Discriminative Replies
Weiyan Shi, Emily Dinan, Adi Renduchintala +4
Existing approaches built separate classifiers to detect nonsense in dialogues. In this paper, we show that without external classifiers, dialogue models can detect errors in their…
Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning
Anton Bakhtin, David J Wu, Adam Lerer +5
No-press Diplomacy is a complex strategy game involving both cooperation and competition that has served as a benchmark for multi-agent AI research. While self-play reinforcement l…
Modeling Strong and Human-Like Gameplay with KL-Regularized Search
Athul Paul Jacob, David J. Wu, Gabriele Farina +5
We consider the task of building strong but human-like policies in multi-agent decision-making problems, given examples of human behavior. Imitation learning is effective at predic…
Multitasking Inhibits Semantic Drift
Athul Paul Jacob, Mike Lewis, Jacob Andreas
When intelligent agents communicate to accomplish shared goals, how do these goals shape the agents' language? We study the dynamics of learning in latent language policies (LLPs),…
Straight to the Tree: Constituency Parsing with Neural Syntactic Distance
Yikang Shen, Zhouhan Lin, Athul Paul Jacob +3
In this work, we propose a novel constituency parsing scheme. The model predicts a vector of real-valued scalars, named syntactic distances, for each split position in the input se…
Boundary-Seeking Generative Adversarial Networks
R Devon Hjelm, Athul Paul Jacob, Tong Che +3
Generative adversarial networks (GANs) are a learning framework that rely on training a discriminator to estimate a measure of difference between a target and generated distributio…
Mode Regularized Generative Adversarial Networks
Tong Che, Yanran Li, Athul Paul Jacob +2
Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes. We argue t…