Doctoral Consortium Accepted Papers

In many allocation problems, understanding individual agents’ needs, wants, and tradeoffs is crucial for providing fair and efficient solutions. This paper begins with motivating applications and critical definitions. We review existing results, such as advising agents on relaxing restrictions for improved resource allocation, optimizing task allocation in online settings without rejection of a task, and more. We conclude by outlining three potential directions for future research.

Smart grid system encompasses large power plants in the wholesale market and retail customers in the tariff market. An electricity broker liaises between the wholesale and tariff markets by procuring electricity from the power plants and selling it to subscribed customers. In our work, we address the prominent challenges in the smart grid system to achieve better efficiency. We discuss the wholesale market, for which we design efficient bidding strategies in periodic double auctions (PDAs), and the tariff market, which includes tariff contract generation strategies and peak demand mitigation strategies. We use the PowerTAC simulator as a test-bed; also utilise these strategies for our autonomous broker, VidyutVanika, which has been proven efficient in the PowerTAC tournaments.

Auction-based federated learning (AFL) is an important area of FL incentive mechanism design. It effectively incentivizes high-quality data owners (DOs) to participate in data consumers’ (DCs, i.e., servers’) FL training tasks. However, AFL is still evolving, with existing methods primarily addressing optimal DC-DO matching or DC selection problems in monopoly markets. To enhance the practicality of AFL, we introduce stakeholder-oriented decision support in AFL. This facilitates optimal and strategic decision-making for all stakeholders, improving the efficiency and sustainability of the AFL ecosystem.

Deep learning (DL) relies on discovering correlation patterns in low-level data and aggregating the information to solve a task. Despite success in a wide variety of applications, ranging from natural language to vision tasks, the learned patterns are often brittle and do not transfer out of the training data distribution (i.e. to different domains). Causality theory proposes methods to discover and estimate cause-effect relationships beyond correlations. Its powerful inference frameworks have been recently highlighted as a potential way to improve the lack of out-of-distribution generalisation in deep neural networks. However, their applications to deep learning problems remain largely under-explored. Our work attempts to bridge this gap and apply causal graphical models to abstract and causal reasoning problems in natural language and vision, requiring strong generalisation abilities beyond correlations. We integrate causal graph modelling methods into deep vision networks and Large Language Models to improve their capacity to perform strong and out-of-distribution reasoning on complex abstract problems.

Facility location problems have been studied in settings like hospital placement or the competition between stores. In some cases, a central authority coordinates facility placements to optimize metrics like the coverage of an area or emergency response time. In many cases, however, facilities are placed by multiple rational agents to maximize their utility, e.g., the number of clients they attract. In previous research, these games feature simplistic client behavior independent of other clients’ strategic choices, e.g., visiting the closest facility. Our goal is to understand what happens if clients also act selfishly, resulting in a two-stage game consisting of strategic facility and client agents. In three recent publications, we investigated such two-stage models for clients that optimize their waiting times. We showed the existence and gave algorithms for (approximate) subgame perfect equilibria, a common extension of Nash equilibria for sequential games. To learn more about this domain, we intend to investigate further natural client behaviors and eventually create a more general model or hierarchy of two-sided facility location games. With this, we aim to make predictions in real-world settings, e.g., the placement of renewable energy infrastructure.

Dynamic adaptation and learning, akin to natural organisms, is crucial for robots operating in real-world scenarios like search and rescue missions. We propose a solution combining intuition from embodied evolution and Bayes theory to promote flexible exploration in foraging tasks. Our investigation focuses on three main areas: 1) leveraging communication and prior knowledge to develop adaptable strategies in agent groups, 2) addressing challenges from sparse rewards or limited data availability, and 3) developing methods for concurrent evaluation and training in a single iteration, filling a current gap in learning-based solutions. Future directions include exploring decentralized coordination among agents and incorporating assistance based on prospective memory and altruism in multi-agent reinforcement learning.

Across disciplines, cooperation is a fundamental research topic. While socially desirable to a population, it often bears a cost to the individual who, in their own self-interest, rationally chooses not to engage in costly cooperation. As such, much work has been done in understanding the biological mechanisms behind cooperation in human and animal populations. In my PhD project, I develop and apply these mechanisms both to artificial multi-agent systems and real social systems. I examine how factors such as agent heterogeneity and different learning algorithms affect not only the level of cooperation within a system, but also the level of fairness in the distribution of payoffs. In previous work, I showed how the effectiveness of the social norm-based mechanism of indirect reciprocity is affected when in-group biased cooperation is present. Beyond my future work on online platforms, I also plan to explore the effects of space, gossip, and partial and subjective observations to widen the potential scope of applications.

We investigate the existence and computation of fair and efficient allocations of indivisible chores to agents with additive preferences. We consider the popular envy-based fairness notions of envy-freeness up to one chore (EF1) and the efficiency notion of Pareto-optimality (PO). The existence of an allocation of chores that is simultaneously EF1 and PO is regarded a major open problem in discrete fair division. We show that an EF1 and PO allocation can be computed in polynomial time for certain structured instances. These results comprise the first non-trivial positive results for the problem and reveal insights towards settling the problem in its full generality.

XBRL tagging in financial texts involves categorizing entities into numerous labels, presenting challenges for state-of-the-art models. Financial reports like 10-Q and 10-K, which must be tagged with XBRL according to a taxonomy with thousands of labels. The FNXL dataset exemplifies this with 2,794 labels. Manual tagging is neither scalable nor cost-effective, necessitating automatic annotation methods. Additionally, summarizing long Earnings Call Transcripts (ECTs) is crucial for financial decision-making. The ECTSum dataset highlights challenges in automatic summarization, including a high compression ratio and documents exceeding typical LLM token limits. This study proposes novel methods for both XBRL tagging and ECT summarization.

Sequential decision-making under uncertainty in multi-agent environments is a fundamental problem in artificial intelligence. Games serve as a base model for these problems. Finding optimal plans in games that model real-world scenarios necessitates scalable algorithms. In games with perfect information, algorithms that use a combination of search and deep reinforcement learning can scale to arbitrary-sized games and achieve superhuman performance. In games with imperfect information, the situation is more challenging due to the nature of the search. This work aims to develop algorithms that use search but can scale into larger games than currently possible.

The primary research challenge lies in mitigating and measuring geographical and demographic biases in generative models, which is crucial for ensuring fairness in AI applications. Existing models trained on web-crawled datasets like LAION-400M often perpetuate harmful stereotypes and biases, especially concerning minority groups or less-represented regions. To address this, I proposed a framework called CIC (Culturally-aware Image Caption) to generate culturally-aware image captions. This framework leverages visual question answering (VQA) to extract cultural visual elements from images. It prompts both caption prompts and cultural visual elements to generate culturally-aware captions using large language models (LLMs). Human evaluations confirm the effectiveness of our approach in depicting cultural information accurately. Two key future directions are outlined. First, current image caption evaluation methods are inadequate for assessing culturally-aware captions, necessitating the development of new evaluation metrics leveraging cultural datasets and representations. Second, ethical considerations, particularly concerning stereotypes embedded in existing models, demand consensus and standards development through diverse cultural perspectives. Addressing these challenges is vital for the responsible deployment of AI technologies in diverse real-world contexts.

Deep learning models have repeatedly shown their strengths in various application domains. However, their predictions often struggle to meet background knowledge requirements, which is a crucial condition for safety-critical systems. My research focuses on integrating requirements into neural networks to guide the learning process and ultimately produce outputs that ensure the requirements’ satisfaction. Here, I will discuss my proposed methods in the context of two real-world applications: tabular data generation and autonomous driving.

In my research, I focus on computationally hard problems in the area of computational social choice. I am interested in the study of input restrictions that guarantee the existence of efficient and scalable algorithms that can be of practical interest.

In recent years, there has been a significant interest in Large Language Models (LLMs) owing to their notable performance in natural language processing (NLP) tasks. However, while their results show promise in mathematical reasoning and software engineering tasks, LLMs have not yet achieved a satisfactory performance level in these domains. In response, current approaches have prioritized scaling up the size of LLMs, necessitating substantial computational resources and data. Our objective, however, is to pursue a different path by developing neurosymbolic language models. We propose to integrate logical and symbolic feedback during the training process, enabling significantly smaller language models to achieve far better reasoning capabilities than the LLMs currently in use.

Anomaly subgraph detection is a vital task in various real applications. However, with the advancement of AI technology, it faces new challenges: 1) Anomaly features are often deeply hidden within large datasets, and 2) Anomaly detection approaches are required to unveil the mechanisms behind anomaly generation. Our study focuses on detecting hidden anomaly subgraphs within big data and offering improved explanations for the root cause of anomalies by integrating multi-domain datasets.

Current approaches to learning cooperative multi-agent behaviors assume relatively restrictive settings. In fully cooperative multi-agent reinforcement learning, the learning algorithm controls all agents in the scenario, while in ad hoc teamwork, the learning algorithm usually assumes control over only a single agent in the scenario. However, many cooperative settings in the real world are much less restrictive. For example, in an autonomous driving scenario, a company might train its cars to cooperate with each other, yet once on the road, these cars must additionally cooperate with cars from other companies. Towards expanding the class of scenarios that cooperative learning methods may optimally address, this research agenda introduces and proposes to study N-agent ad hoc teamwork (NAHT), where a set of autonomous agents must interact and cooperate with dynamically varying numbers and types of teammates.

Due to the complexity of randomness, optimization problems are often modeled to be deterministic to be solvable. Specifically epistemic uncertainty, i.e., uncertainty that is caused due to a lack of knowledge, is not easy to model, let alone easy to subsequently solve. Despite this, taking uncertainty into account is often required for optimization models to produce robust decisions that perform well in practice. We analyze effective existing frameworks, aiming to improve robustness without increasing complexity. Specifically we focus on robustness in decision-focused learning, which is a framework aimed at making context-based predictions for an optimization problem’s uncertain parameters that minimize decision error.