A collection of some projects that I've worked on
Analysis of Symmetry and Conventions in Off-Belief Learning in Hanabi
Hanabi has been proposed as the new frontier for developing strategies in cooperative AI, currently a very nascent area of AI research. A recent algorithm that has been developed for multi-agent reinforcement learning in a cooperative context is the Off-Belief Learning (OBL) algorithm, which is based on iterated reasoning starting from a base policy. We investigate if policies learnt by agents using the OBL algorithm in the multi-player cooperative game Hanabi in the zero-shot coordination (ZSC) context are invariant across symmetries of the game, and if any conventions formed during training are arbitrary or natural, both of which are desirable properties.
Graphical Bayesian Networks for Predicting Asset Covariances
Covariance matrix prediction is a long-standing challenge in modern portfolio theory and quantitative finance. In this project, we investigate the effectiveness of Bayesian networks in predicting the covariance matrix of financial assets (specifically a subset of the S&P 500), evaluated against Heterogeneous Autoregressive (HAR) models.
Improving Domain Adaptation of Transformer Models For Generating Reddit Comments
We improve upon the recent success of large language models based on the transformer architecture by investigating and showing several methods that have empirically improved its performance in domain adaptation. We use a pre-trained GPT-2 model and perform fine-tuning on 5 different subreddits, and use different methods of ordering the training data based on our priors about the input to see how this affects the prediction quality of the trained model. We propose a new metric for evaluating causal language modeling tasks called APES (Average Perplexity Evaluation for Sentences) to address the limitations of existing metrics, and apply them to our results. Our results are evaluated against both LSTM and GPT-2 baselines.
Pseudo-determinism for Graph Streaming Problems
Given a fixed input for a search problem, pseudo-deterministic algorithms produce the same answer over multiple independent runs, with high probability. For example, we can efficiently find a certificate for inequality of multivariate polynomials pseudo-deterministically, but it is not known how to do so deterministically. The same notion can be extended to the streaming model. The problem of finding a nonzero element from a turnstile stream is previously shown to require linear space for both deterministic and pseudo-deterministic algorithms. Another model of streaming problems is that of graphs, where edge insertions and deletions occur along a stream. Some natural problems include connectivity, bipartiteness, and colorability of a graph. While the randomized and deterministic graph streaming algorithms have been mostly well-studied, we investigate pseudo-deterministic space lower bounds and upper bounds for graph theoretic streaming problems.