Vedant Shah

Efficient Causal Graph Discovery Using Large Language Models

Yash More

2024-03-05

ICLR.cc/2024/Workshop/AGI (poster)

Towards DNA-Encoded Library Generation with GFlowNets

Michał Koziarski

Mohammed Abukalam

Vedant Shah

Louis Vaillancourt

Doris Alexandra Schuetz

Moksh J. Jain

Almer M. van der Sloot

Mathieu Bourgey

Anne Marinier

2024-03-04

ICLR.cc/2024/Workshop/GEM (poster)

Efficient Causal Graph Discovery Using Large Language Models

Yash More

We propose a novel framework that leverages LLMs for full causal graph discovery. While previous LLM-based methods have used a pairwise quer… (see more)y approach, this requires a quadratic number of queries which quickly becomes impractical for larger causal graphs. In contrast, the proposed framework uses a breadth-first search (BFS) approach which allows it to use only a linear number of queries. We also show that the proposed method can easily incorporate observational data when available, to improve performance. In addition to being more time and data-efficient, the proposed framework achieves state-of-the-art results on real-world causal graphs of varying sizes. The results demonstrate the effectiveness and efficiency of the proposed method in discovering causal relationships, showcasing its potential for broad applicability in causal graph discovery tasks across different domains.

2024-02-02

ArXiv (preprint)

Efficient Causal Graph Discovery Using Large Language Models

Yash More

We propose a novel framework that leverages LLMs for full causal graph discovery. While previous LLM-based methods have used a pairwise quer… (see more)y approach, this requires a quadratic number of queries which quickly becomes impractical for larger causal graphs. In contrast, the proposed framework uses a breadth-first search (BFS) approach which allows it to use only a linear number of queries. We also show that the proposed method can easily incorporate observational data when available, to improve performance. In addition to being more time and data-efficient, the proposed framework achieves state-of-the-art results on real-world causal graphs of varying sizes. The results demonstrate the effectiveness and efficiency of the proposed method in discovering causal relationships, showcasing its potential for broad applicability in causal graph discovery tasks across different domains.

2024-02-02

ArXiv (preprint)

Efficient Causal Graph Discovery Using Large Language Models

Yash More

We propose a novel framework that leverages LLMs for full causal graph discovery. While previous LLM-based methods have used a pairwise quer… (see more)y approach, this requires a quadratic number of queries which quickly becomes impractical for larger causal graphs. In contrast, the proposed framework uses a breadth-first search (BFS) approach which allows it to use only a linear number of queries. We also show that the proposed method can easily incorporate observational data when available, to improve performance. In addition to being more time and data-efficient, the proposed framework achieves state-of-the-art results on real-world causal graphs of varying sizes. The results demonstrate the effectiveness and efficiency of the proposed method in discovering causal relationships, showcasing its potential for broad applicability in causal graph discovery tasks across different domains.

2024-02-02

ArXiv (preprint)

Unlearning via Sparse Representations

Vedant Shah

Frederik Träuble

Ashish Malik

Hugo Larochelle

Michael Curtis Mozer

Sanjeev Arora

Anirudh Goyal

2023-11-26

ArXiv (preprint)

Unlearning via Sparse Representations

Vedant Shah

Frederik Träuble

Ashish Malik

Hugo Larochelle

Michael Curtis Mozer

Sanjeev Arora

Anirudh Goyal

Machine \emph{unlearning}, which involves erasing knowledge about a \emph{forget set} from a trained model, can prove to be costly and infea… (see more)sible by existing techniques. We propose a nearly compute-free zero-shot unlearning technique based on a discrete representational bottleneck. We show that the proposed technique efficiently unlearns the forget set and incurs negligible damage to the model's performance on the rest of the data set. We evaluate the proposed technique on the problem of \textit{class unlearning} using three datasets: CIFAR-10, CIFAR-100, and LACUNA-100. We compare the proposed technique to SCRUB, a state-of-the-art approach which uses knowledge distillation for unlearning. Across all three datasets, the proposed technique performs as well as, if not better than SCRUB while incurring almost no computational cost.

2023-11-26

ArXiv (preprint)

Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning

Dianbo Liu

Tianmin Shu

Michael Curtis Mozer

Nicolas Heess

2023-02-01

ICLR.cc/2023/Conference (poster)

Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel

Dianbo Liu

Tianmin Shu

Michael Curtis Mozer

Nicolas Heess

In Multi-Agent Reinforcement Learning (MARL), specialized channels are often introduced that allow agents to communicate directly with one a… (see more)nother. In this paper, we propose an alternative approach whereby agents communicate through an intelligent facilitator that learns to sift through and interpret signals provided by all agents to improve the agents’ collective performance. To ensure that this facilitator does not become a centralized controller, agents are incentivized to reduce their dependence on the messages it conveys, and the messages can only inﬂuence the selection of a policy from a ﬁxed set, not instantaneous actions given the policy. We demonstrate the strength of this architecture over existing baselines on several cooperative MARL environments.

2022-05-21

ArXiv (preprint)